Open Collections

UBC Theses and Dissertations

UBC Theses Logo

UBC Theses and Dissertations

Elucidating the neurobiology and individual differences of cost/benefit decision making using a novel… Hosking, Jeremy G. 2014

Your browser doesn't seem to have a PDF viewer, please download the PDF to view this item.

Item Metadata

Download

Media
24-ubc_2014_november_hosking_jeremy.pdf [ 3.31MB ]
Metadata
JSON: 24-1.0166997.json
JSON-LD: 24-1.0166997-ld.json
RDF/XML (Pretty): 24-1.0166997-rdf.xml
RDF/JSON: 24-1.0166997-rdf.json
Turtle: 24-1.0166997-turtle.txt
N-Triples: 24-1.0166997-rdf-ntriples.txt
Original Record: 24-1.0166997-source.json
Full Text
24-1.0166997-fulltext.txt
Citation
24-1.0166997.ris

Full Text

ELUCIDATING THE NEUROBIOLOGY AND INDIVIDUAL DIFFERENCES OF COST/BENEFIT DECISION MAKING USING A NOVEL RAT TASK OF COGNITIVE EFFORT by Jeremy G. Hosking B.Sc. (Honours) Neuroscience, University of Toronto, 2009   A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF  THE REQUIREMENTS FOR THE DEGREE OF  DOCTOR OF PHILOSOPHY  in  The Faculty of Graduate and Postdoctoral Studies (Neuroscience)  THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) September 2014  © Jeremy G. Hosking, 2014  ii  Abstract  Amotivational states and insufficient recruitment of mental effort have been observed in a variety of clinical populations, including depression, traumatic brain injury, post-traumatic stress disorder, and attention-deficit/hyperactivity disorder. Previous animal models of effort-based decision making have utilized physical costs whereas human studies of effort have been primarily cognitive in nature, and it is unclear whether the two types of effortful decision making are underpinned by the same neurobiological processes. We therefore validated a novel rat Cognitive Effort Task (rCET) based on the five-choice serial reaction-time task, a well-established measure of attention and impulsivity. Within each rCET trial, rats were given the choice between an easy or hard visuospatial discrimination, and successful hard trials were rewarded with double the number of sugar pellets. Similar to previous human studies, stable individual variation in choice behaviour was observed, with “workers” choosing hard trials significantly more than their “slacker” counterparts. We used a variety of pharmacological agents as well as temporary inactivation of select brain regions, and showed that the effects of these manipulations often interacted with animals’ baseline preferences. Amphetamine and caffeine caused workers to “slack off”, whereas slackers “worked harder” under amphetamine but not caffeine. Dopamine antagonism had no discernible effects on animals’ choice, contrary to the physical-effort literature. The cholinergic drug nicotine decreased slackers’ willingness to expend effort, whereas scopolamine more substantially decreased workers’ choice of the high-effort option. Temporary inactivation of the basolateral amygdala caused workers to slack off and slackers to work harder, whereas anterior cingulate and medial prefrontal cortex inactivations caused all animals to slack iii  off. In sum, we have shown for the first time that rats are differentially sensitive to cognitive effort when making decisions, independent of other processes such as impulsivity, and these baseline differences appear to be reflected by differences in underlying neurobiology. Further, we demonstrate that mental and physical effort are in part dissociable, both behaviourally and in terms of neurochemistry and neural circuitry. Such findings could inform our understanding of the neurobiological basis of decision making as well as impairments in effort-based decision making, and may contribute to novel therapeutic interventions.  iv  Preface  Experiment 1 (Chapter 3, all sections) has been previously published in the manuscript by Cocker PJ, Hosking Jay G, Benoit J, and Winstanley CA (2012) “Sensitivity to cognitive effort mediates psychostimulant effects on a novel rodent cost/benefit decision-making task” Neuropsychopharmacology 37(8): 1825-37. I performed all data and statistical analysis, and wrote the manuscript. Paul Cocker and James Benoit conducted all animal testing, including the preparation and administration of pharmacological agents; Paul also made figures for the manuscript. Dr. Catharine Winstanley designed the experiment and revised the manuscript.  Experiment 2 (Chapter 4, all sections) has been previously submitted by Hosking JG, Floresco SB, and Winstanley CA (June 2014) “Dopamine antagonism decreases willingness to expend physical, but not cognitive, effort: a comparison of two rodent cost/benefit decision-making tasks”. I conducted all animal testing, prepared and administered all pharmacological agents, performed all data and statistical analysis, made all figures, and wrote the manuscript. Dr. Catharine Winstanley contributed to both experimental design and manuscript revision, and Dr. Stan Floresco contributed to manuscript revision.  Experiment 3 (Chapter 5, all sections) has been previously submitted by Hosking JG, Lam FCW, and Winstanley CA (July 2014) “Nicotine increases impulsivity and decreases willingness to exert cognitive effort despite improving attention in ‘slacker’ rats: insights into cholinergic regulation of cost/benefit decision making”. I prepared and administered all pharmacological agents, performed all data and statistical analysis, made v  all figures, and wrote the manuscript. Fred Lam conducted all animal testing. Dr. Catharine Winstanley contributed to both experimental design and manuscript revision.  Experiment 4 (Chapter 6, all sections) has been previously published by Hosking JG, Cocker PJ, and Winstanley CA (2014) “Dissociable contributions of anterior cingulate cortex and basolateral amygdala on a rodent cost/benefit decision-making task of cognitive effort” Neuropsychopharmacology 39(7): 1558-67. I conducted all animal testing, performed surgery, prepared and administered all pharmacological agents, performed all data and statistical analysis, made all figures, and wrote the manuscript. Paul Cocker also performed surgery. Dr. Catharine Winstanley contributed to both experimental design and manuscript revision.  Experiment 5 (Chapter 7, all sections) has been previously submitted by Hosking JG, Cocker PJ, and Winstanley CA (August 2014) “Prefrontal cortical inactivations decrease willingness to expend cognitive effort on a rodent cost/benefit decision-making task”. I conducted all animal testing, performed surgery, prepared and administered all pharmacological agents, performed all data and statistical analysis, made all figures, and wrote the manuscript. Paul Cocker also performed surgery. Dr. Catharine Winstanley contributed to both experimental design and manuscript revision.  All animal testing was performed in accordance with the Canadian Council on Animal Care (CCAC) and received ethical approval by the University of British Columbia Animal Care Committee (ACC), certificate number A11-0123. vi  Table of contents  Abstract ............................................................................................................................... ii Preface................................................................................................................................ iv Table of contents ................................................................................................................ vi List of tables ..................................................................................................................... viii List of figures ..................................................................................................................... ix List of abbreviations ........................................................................................................... x Acknowledgements ............................................................................................................ xi Chapter 1: General introduction.......................................................................................... 1 1.1 A brief history of decision-making research ......................................................................... 1 1.2 Cost/benefit decision making in the rat: focus on effort-based choice ................................. 5 1.3 Cognitive effort, and extending the Five-Choice Serial Reaction-Time Task .................... 10 1.4 The rCET and experimental objectives ............................................................................... 15 Chapter 2: General methods.............................................................................................. 18 2.1 Subjects ............................................................................................................................... 18 2.2 Behavioural apparatus ......................................................................................................... 18 2.3 Habituation and pre-task training ........................................................................................ 19 2.4 Task training ....................................................................................................................... 19 2.5 The rat Cognitive Effort Task (rCET) ................................................................................. 20 2.6 Behavioural measures for the rCET .................................................................................... 21 2.7 Statistical analysis ............................................................................................................... 22 2.8 Figures ................................................................................................................................. 24 Chapter 3: Experiment 1 ................................................................................................... 26 3.1 Introduction ......................................................................................................................... 26 3.2 Additional methods ............................................................................................................. 28 3.3 Results ................................................................................................................................. 30 3.4 Discussion ........................................................................................................................... 41 3.5 Figures ................................................................................................................................. 51 Chapter 4: Experiment 2 ................................................................................................... 58 4.1 Introduction ......................................................................................................................... 58 4.2 Additional methods ............................................................................................................. 60 4.3 Results ................................................................................................................................. 62 4.4 Discussion ........................................................................................................................... 72 4.5 Figures ................................................................................................................................. 79 Chapter 5: Experiment 3 ................................................................................................... 87 5.1 Introduction ......................................................................................................................... 87 5.2 Additional methods ............................................................................................................. 88 5.3 Results ................................................................................................................................. 89 5.4 Discussion ........................................................................................................................... 94 5.5 Figures ............................................................................................................................... 101 vii  Chapter 6: Experiment 4 ................................................................................................. 105 6.1 Introduction ....................................................................................................................... 105 6.2 Additional methods ........................................................................................................... 107 6.3 Results ............................................................................................................................... 109 6.4 Discussion ......................................................................................................................... 113 6.5 Figures ............................................................................................................................... 120 Chapter 7: Experiment 5 ................................................................................................. 124 7.1 Introduction ....................................................................................................................... 124 7.2 Additional methods ........................................................................................................... 126 7.3 Results ............................................................................................................................... 128 7.4 Discussion ......................................................................................................................... 132 7.5 Figures ............................................................................................................................... 139 Chapter 8: General Discussion........................................................................................ 143 8.1 Summary of experimental findings ................................................................................... 143 8.2 Theoretical implications, and predictions for future studies ............................................. 146 8.3 Critical construct- and task-related considerations ........................................................... 150 8.4 Limitations ........................................................................................................................ 155 8.4 Concluding remarks .......................................................................................................... 159 Bibliography ................................................................................................................... 161 Appendices ...................................................................................................................... 179 Appendix 1: Baseline behavioural measures for the rat Cognitive Effort Task (rCET) ......... 179 Appendix 2: Behavioural measures during amphetamine challenge for the rCET ................. 180 Appendix 3: Behavioural measures during ethanol challenge for the rCET ........................... 181 Appendix 4: Behavioural measures during caffeine challenge for the rCET .......................... 182 Appendix 5: Behavioural measures during satiation manipulations for the rCET .................. 183 Appendix 6: Baseline behavioural measures for the yoked-control task ................................ 184 Appendix 7: Behavioural measures during amphetamine challenge for the yoked-control task ................................................................................................................................................. 185 Appendix 8: Behavioural measures during ethanol challenge for the yoked-control task ...... 186 Appendix 9: Behavioural measures during caffeine challenge for the yoked-control task ..... 187 Appendix 10: Behavioural measures during satiation manipulations for the yoked-control task ................................................................................................................................................. 188 Appendix 11: Distribution of HR choice for all animals across all cohorts............................ 189     viii  List of tables  Table 8.1. Summary of all pharmacology and inactivation results, Experiments 1-5 .... 160   ix  List of figures  Figure 2.1: The standard five-hole operant chamber used for the rCET. ......................... 24 Figure 2.2: Schematic diagram showing the trial structure of the rCET. ......................... 25 Figure 3.1. Baseline behaviour on the rCET and control task. ......................................... 51 Figure 3.2. HR choice across trials in baseline sessions, as divided by quartiles. ............ 52 Figure 3.3. Pharmacological manipulation of cognitive effort is dissociated from visuospatial attention and motor impulsivity. ................................................................... 53 Figure 3.4. Gradients of change for HR choice in response to amphetamine challenge on the rCET. ........................................................................................................................... 55 Figure 3.5. Pharmacological manipulation of choice behaviour on the control task. ....... 56 Figure 3.6. Satiation decreases motivation on the rCET. ................................................. 57 Figure 4.1. Experimental timeline and task schematics. ................................................... 79 Figure 4.2. Dopamine and norepinephrine pharmacology on the rCET. .......................... 81 Figure 4.3. Baseline behaviour on the EDT versus rCET. ................................................ 83 Figure 4.4. Dopamine and norepinephrine pharmacology on the EDT. ........................... 84 Figure 4.5. Reward magnitude manipulations on the rCET. ............................................ 85 Figure 5.1. Nicotinic drug challenges during the rCET. ................................................. 101 Figure 5.2. Muscarinic drug challenges during the rCET. .............................................. 103 Figure 6.1. Histological analysis of cannulae implantation. ........................................... 120 Figure 6.2. Effects of BLA inactivations on the rCET. .................................................. 121 Figure 6.3. Effects of ACC inactivations on the rCET. .................................................. 122 Figure 7.1. Histological analysis of cannulae implantation. ........................................... 139 Figure 7.2. Effects of prelimbic cortex (PL) inactivations on the rCET. ........................ 140 Figure 7.3. Effects of infralimbic cortex (IL) inactivations on the rCET. ...................... 141   x  List of abbreviations  5CSRTT ACC BacMus BLA NAc rCET VTA Five-Choice Serial Reaction-Time Task Anterior cingulate cortex Baclofen-muscimol infusion (inactivation) Basolateral amygdala Nucleus accumbens Rat Cognitive Effort Task Ventral Tegmental Area   xi  Acknowledgements  Thank you to Dr. Catharine Winstanley, who has been a mentor and model, who has known when to use the carrot and when to use the stick, who has both groomed my scientific style and also allowed me to find it on my own, who has given me all the tools to persevere and succeed in research (or fail of my own account), and who has made the last five years so meaningful. Cath, thanks.  Thank you to Paul Cocker, my friend foremost, but without whose substantial scientific contributions this dissertation would not exist. Paul was a teammate for virtually every long day in the lab, and tempered it with remarkable grace and good humour. For your support and guidance, for your dedication to sterile fields, for giving me a home when I had none, and for T2 and JW after hours of surgery, thank you, Paul.  Thanks also to Fred Lam, as dependable and friendly an undergraduate teammate as one can possibly expect; may you realize that medical school is folly and take the road of research.  Thank you to Dr. Stan Floresco, who not only introduced me to Cath in the first place, but also was kind enough to offer guidance as I tried to navigate the sea of effort-based decision-making literature. Along with Stan, I’d like to thank the other members of my supervisory committee, Dr. Adele Diamond and Dr. Vesna Sossi, who pointed me on a path to re-thinking prefrontal and monoamine functioning, respectively.  Thank you to Bear Miles and Jill Milton, who provided me a home and an office in the middle of the forest. Not only have I been free of distractions over these last three months, but I’ve had really great company and fantastic food, too. I’m a smarter man for having spent this time with you. xii   Thank you to my family: Jean and Ken, incredibly supportive and wise grandparents; Sean, devil’s advocate extraordinaire and always my role model; Jen and Evelyn, the best additions to a family that a brother/uncle can get; and Jan and Ken, loving parents, doubtlessly baffled by my many sharp turns in life but always encouraging me on, eager to see me succeed, and there for me always.  Finally, thank you to Zoë Miles. You are utterly loyal and utterly wonderful. My brain is mashed potatoes without you. Let’s go have an adventure.  1  Chapter 1: General introduction      1.1 A brief history of decision-making research  All organisms, be they single-celled protozoa or mammals with nervous systems comprised of tens of billions of cells, face the same challenge in their daily existences: to exploit some options at the exclusion of others in order to maximize their well-being and evolutionary fitness. Structurally simple species integrate a variety of signals from the environment, for example via opponent processes, and act on the winning input (Faumont et al., 2012). As animals’ nervous systems become more complex, they are able to add greater sophistication and flexibility to this decision-making machinery, via both generalized and specialized neurobiological modules underlying choice behaviour (Brase, 2014). Such sophistication allows organisms to make sense of a statistically “noisy” environment, guides species-specific navigation and exploitation of their ecological niche, and consequently benefits their fitness (Vartanian and Mandel, 2011). While Homo sapiens is arguably the most well-endowed with these faculties, it is to the common rat’s credit (or perhaps our detriment) that they demonstrate patterns of choice similar to human beings (Fellows and Farah, 2005; Zeeb et al., 2009).  In practice, decision making is a notoriously difficult subject to elucidate with scientific rigour. Newell and Simon’s (1972) early computer models of problem solving framed decisions as a moving through a problem space, with four key components: an initial state, where the individual is now; a goal state (or states), where the individual 2  wishes to be; sets of operators, the types of actions that can be taken through the problem space; and path constrictions, requirements other than the goal that need to be met. Realistically speaking, however, we often do not know where we are, where we want to go, how to get there, or what related obstacles we will encounter. Equally problematic to explain is our ability to avoid combinatorial explosion (Vervaeke et al., 2009); if an individual can choose between F options at a given moment, and an individual will make D choices on the way to a goal, then the total number of pathways to a goal would be FD. Using a chess game as an example, a problem space wherein the initial state, goal state, sets of operators, and path constrictions are quite small and clearly defined, there are on average 30 acceptable moves at any one turn and 60 turns per game, leading to 3060 possible paths, an immensely large number that would of course be impossible for the human brain to process. It is thus unsurprising that cognitive scientists continue to struggle with conceptualizing the decision-making processes by which animals successfully navigate the world (Vervaeke and Ferraro, 2013).  Behavioural economists, on the other hand, took a much more pragmatic approach to examining decision making by greatly constraining individuals’ choices, typically via dichotomous options (Tversky and Kahneman, 1974, 1981). These studies generally acknowledged the efficacy of individuals’ heuristic strategies to avoid problems such as combinatorial explosion, but also took a tone of prescriptivism by labelling human behaviour as systematically irrational (Bell et al., 1988). For example, individuals regularly demonstrate risk- and loss-aversion, rather than obey a linear function between objective returns and subjective value (Kahneman and Tversky, 1979). An in-depth examination of rationality is beyond the scope of this introduction, but suffice it to say 3  that the economists’ evaluation, based on participants’ hypothetical monetary returns and relatively narrow time-frame, may somewhat limit how far their interpretation can be generalized (for a review, see Brase, 2014).  Psychologists took a similar approach to studying decision making and also extended the behavioural economics framework. Whereas economics often queried participants with financially equivalent options framed as gain or loss, psychologists often instead provided participants with a choice between a low-cost, low-reward option versus a high-cost, high-reward option (Kirby and Marakovic, 1996). Costs in these tasks were typically framed as delay, the time a participant must wait to obtain a reward, or risk/probability, the likelihood of obtaining that reward after choosing a given option. Using the famous “marshmallow task”, wherein young participants could eat a marshmallow immediately or attempt to wait for a second marshmallow, remarkable validity was demonstrated for the prescriptive approach to decision making: children who could better delay their gratification also exhibited, as young adults and adults, better emotional coping, higher SAT scores, lower body-mass index (BMI), and more educational attainment (Mischel et al., 1989; Mischel et al., 2011; Schlam et al., 2013).  Indeed, in both symptom assessments by doctors and psychological testing by researchers, aberrant decision making is commonly reported in a large number disease states, including schizophrenia, major depressive disorder, bipolar disorder, attention-deficit/hyperactivity disorder, autism spectrum disorder, Alzheimer’s disease, Parkinson’s disease, traumatic brain injury, stroke, substance dependence, and pathological gambling (Bechara et al., 1999b; Dixon et al., 2003; Damasio, 2005; Gleichgerrcht et al., 2010; Damiano et al., 2012; Goschke, 2014). As such, novel 4  therapeutics that target the neural circuitry and neurochemistry of decision making may reflect an excellent trans-diagnosis treatment that would benefit the day-to-day lives of a significant proportion of the population.  However, there is evidence that prescriptivism has been overextended in our examination of decision making. In addition to the disease states reported above, so-called deficits or impairments to decision making have also been noted in older adults (Samanez-Larkin et al., 2011; Eppinger et al., 2013), adolescents (Smith et al., 2012; Christakou et al., 2013), healthy young adults whose parents are diagnosed with depression (Mannie et al., 2014), healthy individuals with exceptionally low or high anxiety (de Visser et al., 2010), individuals living in poverty (Haushofer and Fehr, 2014), and women (Byrnes et al., 1999; Brooks and Zank, 2005). This leads to the rather problematic interpretation that the ideal decision-making individuals are financially secure middle-aged men. A perhaps more parsimonious, and less socioculturally biased, interpretation is that individual differences are a normal part of any population, and our current measures are relatively narrow in terms of time-scale and task parameters. It therefore may be premature to apply such strong normative standards on choice behaviour. Such a descriptive, rather than prescriptive, approach puts aside the conceptual difficulties related to “ideal” or “rational” behaviour; descriptivism allows researchers to examine the neurobiological underpinnings of decision making as a function of individual differences, rather than considering all deviations from the mean as abnormal (Revelle et al., 1980; Anderson and Revelle, 1994; White et al., 2007; Diamond, 2011).  5  1.2 Cost/benefit decision making in the rat: focus on effort-based choice  Behavioural neuroscientists approached the subject of choice under the assumption that decision making can be understood as a collection of constituent psychological processes that are embodied by anatomically, functionally, and/or chemically dissociable neurobiological components. Specifically, decision making is presumed to involve the encoding of objective stimulus properties, long-term associations between a given option’s properties and its associated contingencies (e.g. costs, benefits), the subjective weighting of such properties and contingencies based on an individual’s current/future needs or previous experience, the ability to derive a net value for each option (e.g. benefits minus costs), the ability to select an appropriate action based on net value, and updating of valuation based on action outcomes or changing needs (Vartanian and Mandel, 2011; Dolan, 2012). While identifying the neurobiological locus of some of these processes is relatively easy, such as objective encoding in primary, secondary, and associative sensory cortices (Hubel and Wiesel, 1998), others have proven more challenging. For example, human neuroimaging evidence suggests valuation and action selection to have multiple loci with overlapping functioning (Cardinal et al., 2002; Kable and Glimcher, 2007; Botvinick et al., 2009; Basten et al., 2010; Bartra et al., 2013; Guthrie et al., 2013; Jimura et al., 2013).  Animal models of decision making (of which this introduction will focus on rats, although mice and non-human primates are also common) have proven extremely useful in disentangling the neural circuitry and neurochemistry that subserves choice. Similar to studies with humans, behavioural neuroscientists typically (although not exclusively) take advantage of a dichotomous, low-cost/low-reward versus high-cost/high-reward choice 6  structure (Fobbs and Mizumori, 2014). These models primarily use operant chambers, wherein task parameters can be rigidly controlled and many behavioural measures can be quantified, but mazes are also used. Options are often represented as a choice between two levers or nosepoke response apertures, and in place of monetary rewards, animals receive primary reinforcement via sugar pellets. Delay and risk/probability are again utilized as costs, but effort, the amount of work needed to obtain a reward, is a third cost often used in animal models of choice (Salamone et al., 1994; Floresco et al., 2008a). Interestingly, these effortful decision-making models were “back-translated” to a human task (Treadway et al., 2009a), an indication of the reciprocal, complementary nature of human and rat research.  To elucidate the role of specific neurotransmitters, neuromodulators, neuroanatomy, and neural circuitry in a given rat behaviour, a number of methodologies have been successfully implemented. Arguably the least invasive method is systemic pharmacology, wherein animals are administered a drug that acts on a specific neurotransmitter or neuromodulator (e.g. Floresco et al., 2008a). Such an approach cannot exclude peripheral drug effects, i.e. outside the central nervous system, but comes with considerable advantages: it provides a within-subjects comparison, avoiding issues of drift in behaviourally matched groups and reducing the number of animals required for a given study; multiple doses of a drug can be used, revealing dose-dependent effects; multiple drugs can be administered to the same animal, for example demonstrating effects of agonists versus antagonists or one neuromodulatory system versus another (Zeeb et al., 2009). A second method is selective lesions that target a specific brain region. Such lesions can be excitotoxic, via drugs such as quinolinic acid (Rogers et al., 7  2001), or selective to specific types of neurons, such as dopamine depletion of a region using 6-hydroxydopamine (Winstanley et al., 2005). While coarser and often subject to compensatory effects due to their permanent nature, lesions can help infer a region’s involvement in a given behaviour via loss-of-function. Contralateral disconnections, wherein one region is lesioned unilaterally and a second region is lesioned contralaterally, can also demonstrate a circuit governing behaviour (Hauber and Sommer, 2009). A refinement of the lesion process uses cannulation instead, allowing researchers the ability to directly inject drugs into a specific brain region. This can be used to temporarily inactivate a region, via an anaesthetic (Seamans et al., 1995) or GABA agonism (St Onge and Floresco, 2010), or to look at region-specific pharmacology. As these effects are temporary, cannulation surgery offers the same benefits as pharmacology (within-subjects effects, no possibility of drift between groups, fewer animals needed) with none of the drawbacks of lesions (between-subjects comparisons, permanence, compensatory effects). While other methodologies can be employed, including electrophysiological recordings of awake and behaving animals (Hyman et al., 2012) and small-animal neuroimaging (Endepols et al., 2010), pharmacology and selective lesions/inactivations are relatively simple to implement and effective at revealing key structures that need to be studied at finer spatial and temporal resolutions.  Through this combination of methods, animal models have done much to directly confirm the influence of a number of brain regions and neuromodulators in decision making. These putative cortico-limbic-striatal circuits include (but are not limited to) the medial prefrontal cortex (mPFC), orbitofrontal cortex (OFC), anterior cingulate cortex (ACC), basolateral amygdala (BLA), nucleus accumbens (NAc), and ventral tegmental 8  area (VTA), as well as the neuromodulators dopamine, serotonin, and acetylcholine (Floresco et al., 2008c; Zeeb et al., 2009; Zeeb and Winstanley, 2011, 2013; Fobbs and Mizumori, 2014). Each relevant region and its contributions to cognition and cost/benefit decision making will be discussed in Chapters 3 through 7. While there appears to be considerable overlap and some generalized neural mechanisms, a region’s (or neuromodulator’s) contribution to choice on a decision-making task is often dependent on the costs employed by that task; in other words, the neurobiology underlying risk- versus delay- versus effort-based decision making is in part dissociable (Floresco et al., 2008c).  As regards effort-based choice, the earliest model took advantage of a T-maze wherein animals could choose to scale a barrier in one arm for a larger reward, or to enter an open arm for a smaller reward. Using this model and its variants, midbrain dopamine has been shown to critically influence choice behaviour: systemic D2-family dopamine receptor antagonism, as well as depletions of NAc dopamine (which originates from the midbrain’s VTA), decreased animals’ willingness to exert effort for a larger reward (Salamone et al., 1994; Cousins et al., 1996; Salamone et al., 2007). A critical control used in many of these and subsequent studies demonstrated that the effects were not simply due to reduced hedonic aspects of the reward: when costs were made equivalent for both options, such as placing a barrier in both arms, lesioned/dopamine-antagonized animals chose the high-reward option, indicating that the manipulations were specifically affecting animals’ sensitivity to effort. A later study showed that amphetamine, which potentiates dopamine function amongst other effects, increased animals’ choice of high-effort/high-reward (HR) options, and also that D1- and D2-, but not D3-specific, dopamine 9  receptor antagonism decreased choice of the HR arm (Bardgett et al., 2009). In contrast to dopamine, serotonin pharmacology had no effect on choice behaviour for this task (Denk et al., 2005).  In addition to the NAc, a number of other brain regions were also implicated with this task of effort-based decision making. Large lesions to the mPFC, encompassing the prelimbic (PL) and infralimbic (IL) cortices as well as the ACC, decreased animals choice of HR (Walton et al., 2002a). When more specific prefrontal lesions were used, however, PL-IL lesions had no effect, whereas ACC lesions caused a similar decrement of effortful choice to the larger lesion (Walton et al., 2003b). In an interesting double dissociation, ACC lesions were again shown to decrease choice of HR on the effort-based decision making task, whereas OFC lesions had no effect; this pattern was reversed for delay-based decision making (Rudebeck et al., 2006a). One notable caveat, however, is that effects of ACC lesions were relatively transient, which could point to compensatory neurocircuitry or perhaps damage to neighbouring regions of the motor cortex. Temporary inactivation of the BLA, as well as contralateral BLA-ACC disconnections, likewise decreased animals’ effortful choice (Floresco and Ghods-Sharifi, 2007a). Interestingly, dopamine depletions of the ACC (which, like the NAc, is the target of VTA dopamine) had no effect on choice for this T-maze task, suggesting that the ACC’s contributions to effort-based decision making lie in parallel, and not downstream, of midbrain dopamine’s contributions (Walton et al., 2005).  A number of operant effort-based decision-making tasks were subsequently developed, wherein animals could press a low-effort/low-reward (LR) lever once for a small reward, or press the HR lever multiple times for a larger reward. While many of the 10  findings using these operant tasks paralleled those of the T-maze task, there were also a number of conflicting and novel results. For example, both dopamine D2-family receptor antagonism and excitotoxic ACC lesions decreased willingness to exert effort, but dopamine depletions of the NAc had no effect (Walton et al., 2009). Subsequent studies demonstrated that inactivation of the NAc core, but not NAc shell, decreased choice of HR (Ghods-Sharifi and Floresco, 2010), and inactivation of the BLA decreased choice of HR, as observed previously (Ghods-Sharifi et al., 2009). Both general dopaminergic antagonism and glutamate NMDA receptor antagonism decreased willingness to expend effort, but amphetamine had a bi-phasic effect, increasing choice of HR at low doses and decreasing HR at high doses (Floresco et al., 2008a).  Altogether, effort-based decision making appeared to rely on the contributions of ACC, BLA, NAc, and midbrain dopamine, but not mPFC, and until 2009 (i.e. the start of the candidate’s research), individual differences in animals’ effort-based choice and underlying neurobiology had not been examined (but see Randall et al., 2012).  1.3 Cognitive effort, and extending the Five-Choice Serial Reaction-Time Task  It can be argued, however, that most of the critical decisions we face in the industrialized world vary in their degree of mental, rather than physical effort. Preliminary human research on cognitive versus physical effort suggested different systemic catecholamine profiles (Fibiger et al., 1984) and divergent effects on subsequent task performance (Smit et al., 2005). Furthermore, most human tasks that varied effort were cognitive in nature. For example, the Stroop task requires individuals to verbally identify the colour of a word’s font, and the difficulty can be manipulated by making the word and colour 11  congruent (e.g. the word “red” in red font) or incongruent (e.g. the word “red” in blue font)(Wright et al., 2008). Incongruent trials are reported to feel much more effortful, which is reflected both in accuracy/response times and increases in measured skin conductance response (SCR). In one case study, a large left medial frontal lesion encompassing mPFC and ACC left a patient without the subjective sense of effort; when this individual performed the Stroop task, she could identify which variant was more difficult, but expressed that she had no conscious sensation of effort, a self-report that corresponded with her lack of SCR (Naccache et al., 2005). A number of tasks manipulate effort by modulating vigilance/sustained attention, directed attention, and to a lesser extent other cognitive constructs such as working memory (Cohen et al., 1982; Smit et al., 2004; Croxson et al., 2009).  Shortly after the start of the candidate’s research, a cognitive switching task was validated, wherein participants had to identify a number’s parity (i.e. odd or even) or magnitude (i.e. above or below 5); the rate at which a given option switched between these two types of discriminations determined that option’s difficulty, with faster task switching identified as more effortful (Kool et al., 2010). On this task, the human lateral PFC (in some respects a homologue to rat mPFC; see Uylings et al., 2003; Seamans et al., 2008), was shown to track individuals’ subjective sense of effort as well as their subsequent avoidance of high-effort options (McGuire and Botvinick, 2010), despite this region’s lack of involvement on the rat T-maze task. In fact, all existing animal models of effort-based decision making varied their options by the degree of physical, not cognitive, effort. As such, it was unclear whether cognitive effort relied on the same, separate, or overlapping circuitry as physical effort, and thus how relevant the data from existing 12  animal models were to our daily lives. Furthermore, it was unclear whether willingness to exert mental effort was related to other cognitive constructs, such as impulsivity (Pattij and Vanderschuren, 2008).  Effort reflects a strain on limited resources, and in that sense, attention is an excellent cost for a mental effort task. While undeniably a broad psychological construct, attention is generally regarded as the prioritization of some stimuli over others, be they internal or external. This prioritization is embodied by a number of neurobiological processes, including increased probability of neuronal firing, increased sensitivity or size of receptive fields, and increased processing within specialized brain regions. Although all neuromodulators and a number of brain regions may play a role, acetylcholine efflux and PFC activity in particular have been strongly linked to increased attentional processing, particularly of an endogenous/“top-down” nature (Klinkenberg et al., 2011; Poorthuis and Mansvelder, 2013). The need for this prioritization, as most of us are all too familiar, is because attentional reserves (and their related neurobiology) are finite; individuals cannot attend to everything at once. When two stimuli of similar modality are presented, for example, focusing on one leads to improved awareness of that stimulus but at the expense of degraded awareness for the other (Brancucci and Tommasi, 2011). In fact, the more attention we dedicate to a single task, the greater the effortful exertion, as measured by both self-report and physiological responses such as pupillary dilations and SCR (Hess and Polt, 1964; Tursky et al., 1969). Thus, psychologists have long associated attention with effort, noting that regardless of the specific costs, high effort exertion demands the participant’s attention (Kahneman, 1973); in other words, an individual cannot exert significant levels of effort without also attending to the target of that effort. 13  Some researchers postulate that heuristics can be understood as a strategy to reduce a given task’s effort, noting that performance can instead be done “automatically”, i.e. without attention (Shah and Oppenheimer, 2008). Altogether, while effort and attention cannot be conflated, there exists good evidence for a significant relationship between the two constructs.  For over thirty years, the Five-Choice Serial Reaction-Time Task (5CSRTT) has been used as an animal model of attention, as well as motor impulsivity (Carli et al., 1983). As per the cost/benefit decision-making tasks described above, the 5CSRTT also takes advantage of an operant chamber, this time with five response apertures along one wall, and sugar as its primary reward. In a given trial of the 5CSRTT, animals must identify via nosepoke which of the five apertures was illuminated in order to receive a sugar pellet reward. Researchers typically envisage the 5CSRTT as requiring visuospatial (rather than other sensory modalities), sustained (rather than brief), focused (rather than divided), endogenous/top-down (rather than exogenous/bottom-up) attention (e.g. Dalley et al., 2001). When the stimulus duration of the light is 0.5 seconds, animals are typically quite good (but far from perfect) at applying this attention to the task, achieving an accuracy of approximately 80% for a given cohort. If animals make a nosepoke response before the aperture is illuminated, the trial is counted as a premature response, a measure long associated with impulsive action (Robbins, 2002).  The 5CSRTT has proven extremely useful in revealing the role of neuromodulators and brain regions in visuospatial attention and impulsivity (Dalley et al., 2008). Pertinent to the current dissertation, mPFC lesions decreased animals’ accuracy, i.e. ability to perform the task, whereas ACC lesions increased premature responding 14  (Muir et al., 1996); subsequent experiments suggested that larger PL-IL-ACC lesions had greater effects on accuracy and premature responding than ACC or PL-IL alone (Passetti et al., 2002). Specific IL lesions had no effect on accuracy but increased premature responding (Chudasama et al., 2003). Depletions of basal forebrain acetylcholine, which projects to the cortex, impaired animals’ accuracy on the 5CSRTT, and systemic nicotine restored animals’ performance (Muir et al., 1995). Amphetamine reliably increased premature responding across many studies, whereas its effects on accuracy are minimal (e.g. Cole and Robbins, 1989; Bizarro et al., 2004). Norepinephrine depletions of the locus coeruleus had no effects on animals’ performance, although deficits could be unmasked via altering task parameters or administering amphetamine (Carli et al., 1983; Cole and Robbins, 1987). Altogether, the 5CSRTT is therefore an excellent task from which to develop a new animal model, as new data can always be compared to the extensive previous research.  Critically, the difficulty of the 5CSRTT can be directly manipulated by changing the stimulus duration: the briefer an aperture is illuminated, the lower the animals’ accuracy (Robbins, 2002). In other words, shorter stimulus durations make it harder for animals to detect the stimulus, and thus they need to pay more attention (i.e. exert greater mental effort) in order to obtain reward. Taking advantage of this stimulus-duration parameter in the 5CSRTT, we simply inserted a choice component at the onset of each trial, represented by the presentation of two response levers: if the LR lever was pressed, the stimulus duration was long, little attention was needed, and animals were rewarded with a single sugar pellet if they nosepoked correctly; if the HR lever was pressed, the stimulus duration was short, much more attention was needed, and animals were 15  rewarded with two sugar pellets following a correct nosepoke. In effect, animals could choose to expend greater mental effort in hopes of attaining a greater reward. This was the genesis of the rat Cognitive Effort Task (rCET).  1.4 The rCET and experimental objectives  To reiterate, each self-initiated trial of the rCET, of which there is no fixed limit over the 30-minute session, provides animals with the choice between a cognitively easy but minimally rewarding trial (via pressing the LR lever), or a trial in which the cognitive effort demands and potential reward are greater. Regardless of the choice, each trial also has a 5-second inter-trial interval, in which the animal must withhold all responding or be punished with a 5-second time-out.  The rCET has proven useful in a number of ways. First, it allows researchers to elucidate the neurobiological mechanisms underlying decision making with cognitive effort costs. Second, it has separate measures of a number of psychological constructs, including animals’ willingness to exert effort (effort-based choice, decision making), their ability to perform the task (visuospatial attention), and their rates of premature responding (motor impulsivity). Third, response latencies are recorded for all relevant behaviours, which may reveal between-subject differences as well as general impairments to motor function. Fourth, the rCET is linked to two excellent bodies of scientific research, namely animal models of cost/benefit decision making and the 5CSRTT, and thus can make informed predictions of experimental manipulations to cortico-limbic-striatal circuitry. Fifth, and quite serendipitously, the rCET’s difficulty is titrated such that it consistently provides a spectrum of choice behaviours, from animals 16  who greatly avoid high-effort options (“slackers”) to animals that choose high-effort options more than the average (“workers”). As such, it has provided an excellent means of studying individual differences in decision making and their relation to underlying neurobiology.  The aims of the five experiments within this dissertation, which have all been submitted to or published in peer-reviewed academic journals, are as follows:  Experiment 1 (Chapter 3) validates the task and its associated costs, via a yoked-control task (where effort costs were removed) and via reinforcer devaluation through satiation. It also examines the effects of three pharmacological agents commonly used by individuals: amphetamine, alcohol, and caffeine. Finally, it attempts to explain the basis of individual differences in effort-based choice, by comparing choice preferences to other behavioural measures, and by examining the interactions between choice preference and pharmacological challenges.  Experiment 2 (Chapter 4) examines the relationship between neuromodulators and different types of effort. Specifically, it directly compares choice behaviour on the rCET and a previously established task of physical effort, the Effort Discounting Task. It then compares effects of two dopamine antagonists, and two norepinephrine agonists, on the two decision-making tasks. Finally, it adds another control to validate the rCET, showing that changes in reward magnitude are accompanied by changes in choice.  Experiment 3 (Chapter 5) considers the role of the neuromodulator acetylcholine in decision making with mental effort costs. While cholinergic function has been well established in 5CSRTT literature, its contributions to cost/benefit decision making are 17  relatively underexplored. Four pharmacological agents are systemically administered, agonists and antagonists for both the nicotinic and muscarinic cholinergic receptors.  Experiment 4 (Chapter 6) compares the roles of two brain regions previously implicated in effort-based decision making, the ACC and BLA, via temporary inactivations. Whereas it has previously proven difficult to disentangle each region’s contribution to effort-based choice, the rCET allowed for a dissociation between the two regions. Furthermore, it highlighted the role of individual differences and underlying neurobiology.  Experiment 5 (Chapter 7) assesses the influence of the mPFC (PL and IL) on the rCET, again using temporary inactivations. Previous studies using animal models suggested that the mPFC plays no role in effort-based choice, but here we provide evidence that the mPFC contributes to choice behaviour on the rCET, when the costs are cognitive (rather than physical, as in the previous studies).  18  Chapter 2: General methods  2.1 Subjects  Subjects were male Long-Evans rats (Charles River Laboratories, St. Constant, Quebec, Canada) weighing 275-300g at the start of each experiment, maintained at 85% of their free-feeding weight, and food restricted to 14-16g rat chow per day. Water was available ad libitum. Animals were pair-housed in a climate-controlled colony room on a 12hr reverse light-dark cycle (lights off: 8:00am; temperature: 21°C). All housing and testing were in accordance with the Canadian Council of Animal Care, and all procedures were approved by the UBC Animal Care Committee.  2.2 Behavioural apparatus  All testing took place within 16 standard five-hole operant chambers (Figure 2.1), with each chamber enclosed in a ventilated, sound-attenuating cabinet (Med Associates Inc., Vermont, USA). Along one wall of each chamber, five response apertures were positioned 2cm above the floor. Each aperture was equipped with a stimulus light at the back of the hole, as well as a horizontal infrared beam to record nosepoke responses. The opposite wall contained a food magazine into which sucrose pellets could be delivered (45mg; Bioserv, New Jersey, USA) via an external pellet dispenser. The food tray was also equipped with its own light and infrared beam for nosepoke responses. Two retractable response levers were installed, one on each side of the food magazine, and above the magazine was a house light that could be used to illuminate the chamber.  19  2.3 Habituation and pre-task training  Two 30-minute habituation sessions preceded the training, wherein the response apertures and food magazine were baited with sucrose pellets. As per 5-choice serial reaction time task (5CSRTT) training, animals were taught to make a nosepoke response in an illuminated aperture within 5s to obtain reward. These illuminated stimuli were presented at pseudorandom across the 5 response apertures, in sessions that consisted of either 100 trials or 30 minutes. After 8-9 training sessions, all animals were completing the 100 trials, making ≥ 80% correct nosepoke responses, and ≤ 20% response omissions. Animals were subsequently trained to press the retractable levers for reward on a fixed ratio 1 schedule. Only one lever was presented per session. Once the animal was making >50 lever presses per session, the training was repeated on the other lever. The order in which the levers were presented (i.e. left versus right) was counterbalanced between subjects. This lever training phase lasted 2-4 sessions.  2.4 Task training  Task training began with a forced-choice version of the rCET, wherein only one of the levers was extended per trial. As training progressed, the stimulus light duration was incrementally decreased in a series of 15 stages, from 5s to 1s for LR trials and from 5s to 0.2s for HR trials. To progress to the next stage, rats were required to complete a session with < 20 choice omissions, ≥ 25 correct responses on HR trials, ≥ 70% accuracy on HR trials, and < 20% omissions on HR trials. Once rats reached the training stage in which the stimulus duration on HR trials was shorter than for LR trials (i.e. 0.9s versus 1s), 20  reward for successful HR trials was increased to 2 sugar pellets while LR trial rewards remained at 1 pellet.  At the outset of training, the levers were permanently designated to initiate either low-effort/low-reward (LR) or high-effort/high-reward (HR) trials, and these designations were evenly counterbalanced across subjects. Animals were trained 4-5 days per week in 30-minute sessions of no fixed trial number. Upon successful completion of all training stages (55-60 sessions), animals were placed in the free choice version of the rCET, wherein both levers extended and animals could choose between the LR and HR forms of the task by pressing the corresponding lever.  2.5 The rat Cognitive Effort Task (rCET)  Figure 2.2 depicts the rCET in schematic form. New rCET trials were available when the food tray light was illuminated. A nosepoke in the food tray extinguished the light and extended the levers. Animals would then press one of the levers, thereby choosing a LR or HR trial, and this would cause both levers to retract and a 5s inter-trial interval (ITI) to commence. After the ITI, one of the five stimulus lights briefly illuminated, with a stimulus duration of 1.0s for LR trials and 0.2s for HR trials. Animals were rewarded if they nosepoked the previously illuminated aperture within 5s (a correct response), and received 1 sugar pellet for a LR trial and 2 sugar pellets for a HR trial. Upon reward delivery, the tray light again illuminated, thus signalling the opportunity to begin the next trial.  Trials went unrewarded for a number of reasons: if animals failed to make a lever response within 10s (a choice omission); if animals nosepoked during the ITI (a 21  premature response); if animals nosepoked in any aperture other than the one that was illuminated (an incorrect response); and if animals failed to nosepoke at the array within 5s after stimulus-light illumination (a response omission). All such behaviours were punished with a 5s time-out period, accompanied by illumination of the house light. During the time-out, new trials could not be initiated and thus reward could not be earned. Following the time-out, the house light extinguished and the tray light illuminated to signal that the rat could begin the next trial.  Training continued until a stable level of baseline performance was observed (as defined below), 19-35 sessions.  2.6 Behavioural measures for the rCET  Percent choice, rather than the absolute number of choices, was used to determine preference for lever/trial type, in order to minimize the influence of variation in the number of trials completed. Percent choice was calculated as follows: (number of choices of a particular lever / total number of choices) * 100. When baseline performance on the rCET was deemed statistically stable (i.e. no effect of session on repeated-measures ANOVA for choice, accuracy, and premature responding over the last three sessions; see “Data analysis” below), the mean choice of the HR option was 70% for Experiment 1. Animals were grouped as “workers” if they chose HR for > 70% of trials and as “slackers” if they chose HR for ≤ 70% of trials. To maintain consistency when discussing individual differences and to avoid arbitrary categorization, we therefore held the worker/slacker distinction at 70% HR trials for all experiments in this dissertation. However, for future studies, examining only those individuals with extreme choice 22  profiles, i.e. the extreme slackers and workers of the first and last quartiles of rats and not Q2/Q3, may also be revealing.  The following variables were also analyzed separately for LR and HR trials: percent accuracy ((number of correct responses / number of total responses made) * 100); percent premature responses ((number of premature responses / total number of trials initiated) * 100); latency to choose between the LR and HR levers (lever choice latency); latency to correctly nosepoke in the illuminated aperture (correct latency); latency to collect reward (collection latency); percent response omissions ((number of trials omitted / number of correct, incorrect, and omitted trials) * 100). Failures to choose a lever at the beginning of the trial (choice omissions) and the total number of completed trials were also analyzed.  2.7 Statistical analysis  All data were analyzed in SPSS (version 16.0; SPSS/IBM, Chicago, IL, USA). All variables expressed as a percentage were arcsine transformed to minimize artificial ceiling effects (Zeeb et al., 2009). Baseline rCET data were analyzed using repeated-measures ANOVA with choice (two levels: LR or HR) and session (three levels: baseline sessions 1-5) as within-subjects factors. As discussed above, animals were categorized as workers and slackers at baseline, and group (two levels: worker or slacker) was therefore used as a between-subjects factor in all analyses. Groups proved extraordinarily stable across all experiments: at baseline, all saline conditions for drug challenges, and at control conditions during satiation manipulations in Experiment 1, workers continued to choose a significantly greater percentage of HR trials than slackers (group: all Fs > 8.93, 23  all p < 0.008). For Experiments 1 and 4, animals’ sessions were divided into quartiles, with equal numbers of trials in each quartile, and HR choice was examined across the session using quartile (four levels: Q1-Q4) as a within-subjects factor.  Pharmacological manipulations and temporary inactivations were again analyzed using repeated-measures ANOVA. For all drug challenges or inactivations, dose (four levels: saline plus three drug doses) or inactivation (two levels: saline and baclofen-muscimol) and choice were included as within-subjects factors, with group as a between-subjects factor. Any main effects of significance (p < 0.05) were further analyzed via post-hoc one-way ANOVA or paired-samples t-tests. Any p-values > 0.05 but < 0.07 were reported as a statistical trend.   24  2.8 Figures   Figure 2.1: The standard five-hole operant chamber used for the rCET.  (A) Side view of modular chamber, with five-hole stimulus array on the left and food tray on the right. (B) The five-hole stimulus array.   25   Figure 2.2: Schematic diagram showing the trial structure of the rCET.  Trials began when the food-tray light illuminated. A nosepoke response in the food tray extinguished the light and extended the levers. Each lever was permanently designated to initiate either low-effort/low-reward (LR) or high-effort/high-reward (HR) trials. When animals pressed one of the levers, both levers retracted and a 5s inter-trial interval (ITI) began. Following the ITI, one of the five stimulus lights briefly illuminated, 1.0s for a LR trial and 0.2s for a HR trial. If animals nosepoked in the previously illuminated aperture within 5s (a correct response), they were rewarded 1 sugar pellet for a LR and 2 sugar pellets for a HR trial. A number of behaviours led to a 5s time-out, signalled by house-light illumination: failure to make a lever response (choice omission); failure to withhold responding during the ITI (premature response); nosepoke in an unlit hole following the stimulus (incorrect response); failure to make a nosepoke response following the stimulus (response omission).   26  Chapter 3: Experiment 1  Sensitivity to cognitive effort mediates psychostimulant effects on a novel rodent cost/benefit decision-making task  3.1 Introduction  The degree of effort that we are willing to expend for a goal has far-reaching consequences for our economic and personal success. Animal models of such effort-based decision making have typically required animals to scale a barrier in a T-maze versus traverse a flat runway, or press an operant lever a greater number of times, to receive a larger reward (Salamone et al., 1994; Floresco et al., 2008b). These studies have revealed key neural and neurochemical substrates involved in recruiting physical effort (Walton et al., 2003a; Rudebeck et al., 2006b; Floresco and Ghods-Sharifi, 2007a; Salamone et al., 2007; Bardgett et al., 2009; Ghods-Sharifi et al., 2009). However, most of the crucial decisions that determine success in an industrialized society involve varying degrees of cognitive, rather than physical, effort. For example, when deciding whether to perform the bare minimum at the office or to exceed expectations in the hope of promotion, one of the key costs we are evaluating is the cognitive effort required for the potentially more lucrative task. In human laboratory studies aimed at investigating the neurobiological basis of effort, the difficulty level is most often varied by increased cognitive demands such as attention or executive control (Naccache et al., 2005; Botvinick et al., 2009; Croxson et al., 2009; but see Treadway et al., 2009a; McGuire and Botvinick, 2010). The nature of the effort exerted between most human and animal 27  paradigms is therefore fundamentally different, and it is unclear whether the same neurobiological processes underlie the two types of effort-based decision making (Kool et al., 2010; Kurniawan et al., 2011).  This is an important research question, as increases in mental fatigue, amotivational states, and insufficient recruitment of effort have been observed in a variety of clinical conditions, including depression (Cohen et al., 1982; Hammar et al., 2003; Hammar et al., 2011), mild traumatic brain injury (Stulemeijer et al., 2006; Stulemeijer et al., 2007), post-traumatic stress disorder (Shalev et al., 1990), attention-deficit/hyperactivity disorder (Egeland et al., 2010), and chronic fatigue syndrome (Lawrie et al., 1997; Wallman and Sacco, 2007). Such mental fatigue can produce physiological effects on the body (Wright et al., 2003; Mukherjee et al., 2011), negatively influence performance on unrelated cognitive processes (Wright et al., 2008), and time to recuperate is required following high cognitive effort (Smit et al., 2004; Barnes et al., 2009). The use of stimulants such as amphetamine and caffeine is a common strategy to overcome the mental fatigue associated with high attentional costs, such as sustained highway driving or university lecture attendance (Silber et al., 2006; Peeling and Dawson, 2007), although the extent of their beneficial effect is unclear (de Wit et al., 2002; Drummer et al., 2003). Conversely, depressants such as alcohol may have deleterious effects on cognitive tasks (de Wit et al., 2000; Field et al., 2010), but both stimulant and depressant effects can be strongly influenced by the individual differences between participants (Revelle et al., 1980; Holdstock et al., 2000; White et al., 2007).  To investigate whether rats are sensitive to differences in mental effort requirements when making decisions, we therefore designed a novel rat Cognitive Effort 28  Task (rCET) with costs more closely analogous to human studies, and conducted some pharmacological challenges to assess the task’s utility for studying the neurobiology underlying this process. The rCET is a modification of the well-validated 5-choice serial reaction-time task (5CSRTT), a rodent task originally modeled after the Continuous Performance Test but akin to human reaction-time tasks that assess attention and impulsivity in clinical populations (Robbins, 2002). Within each trial of the rCET, rats are given the choice between an easy or hard visuospatial discrimination, and successful hard trials are rewarded with double the number of sugar pellets. We then evaluated the effects of amphetamine, caffeine and alcohol on task performance to observe whether a) stimulants could increase, or alcohol decrease, animals’ willingness to choose the harder option, and b) whether such changes were affected by basal levels of effortful choice, in accordance with reports in human subjects.  3.2 Additional methods  The Yoked Control Task To determine whether choice in the rCET was driven by the allocation of cognitive effort or sensitivity to differential rates of reinforcement, a second cohort (n = 20) was trained to perform a control task in which the reinforcement probabilities on both the LR and HR trials depended on a master animal’s performance on the rCET. The average accuracy on LR and HR trials was calculated for each rat performing the rCET at baseline, and these accuracies were used to determine the reward schedule for a yoked control rat. The structure of the control task was identical to that of the rCET, with the exception that the stimulus light remained illuminated until the rat made a response.  29   As in the rCET, rats were trained to perform the yoked control task in 2 consecutive groups (group 1: n = 12; group 2: n = 8). The habituation and pre-training phases for the control task were identical to those used for the rCET. The animals were then placed on a forced choice variant of the yoked control task for 10 sessions, wherein only one of the two levers extended per trial, before moving onto the free choice version. Control sessions lasted 30 minutes. Training continued until a stable level of baseline performance was observed (29-35 sessions).  Pharmacological Challenges Once stable baseline behaviour had been established, drugs were administered in the following order: amphetamine (0, 0.3, 0.6, 1.0 mg/kg; Sun et al., 2012), ethanol (0, 0.3, 0.6, 1.0 g/kg; Bizarro et al., 2003), and caffeine (0, 5, 10, 20 mg/kg; Bizarro et al., 2004). d-amphetamine sulphate was purchased under a Health Canada exemption from Sigma-Aldrich UK (Dorset, England), caffeine was purchased from Sigma-Aldrich Canada (Oakville, ON, Canada), and ethyl alcohol was purchased from Fisher Scientific (Edmonton, AB, Canada). All drugs were administered via intraperitoneal injection. Amphetamine and caffeine were dissolved in 0.9% sterile saline and administered in a volume of 1 ml/kg, and alcohol was delivered as a 20% (w/v) solution. Animals were given a minimum of 1 week drug-free testing between compounds to minimize any carryover effects.  All drugs were prepared fresh daily, and administration adhered to a digram-balanced Latin Square design (for doses A-D: ABCD, BDAC, CABD, DCBA (p.329, Cardinal and Aitken, 2006)). The injection schedule followed a 3-day cycle starting with 30  a baseline session, followed by a drug or saline injection session, and then by a non-testing day. Injections were administered 10 minutes prior to behavioural testing for amphetamine and caffeine, and 30 minutes prior to behavioural testing for ethanol.  Acute and Chronic Satiation  Following 1 week of drug-free testing, half of the animals were given unlimited access to food for 2hr prior to testing (acute satiation, AS). The other half of the animals was tested under food restriction (FR) as per usual. A non-testing day and baseline (i.e. FR) session followed. During the next testing day, the other half of the animals underwent AS prior to testing while the first half remained FR. Immediately after this second session of AS, free feeding began for all animals, wherein food was provided ad libitum. Two non-testing days were followed by five daily sessions during this chronic satiation (CS) period.  3.3 Results  Baseline performance of the rCET. Choice behaviour, accuracy, and premature responses. When considered as a homogenous group, animals chose high-effort/high-reward (HR) trials more than low-effort/low-reward (LR) trials (choice: F1,17 = 82.06, p < 0.001), although there was clearly a continuum of individual variation across the cohort. When the animals were divided into two groups depending on whether they were above or below the mean choice of the HR option (see General Methods), individuals above the mean (“workers”) chose HR trials significantly more than individuals below the mean (“slackers”) (Figure 3.1a; 31  group: F1,17 = 27.94, p < 0.001). When the session was divided by quartiles of equal trial number, all animals chose more HR trials as the session progressed (Figure 3.2; quartile: F3,51 = 22.84, p < 0.001; quartile x group: F3,51 = 2.94, NS). All animals were significantly more accurate for LR trials than for HR trials (Figure 3.1b; choice: F1,17 = 167.28, p < 0.001), regardless of whether they were workers or slackers (choice x group: F1,17 = 0.20, NS). However, both workers and slackers performed equally well on LR and HR trials (group: F1,17 = 0.18, NS) despite workers choosing HR trials more often, suggesting that preference for the HR trials was not driven solely by attentional ability. Premature responding was higher during HR versus LR trials (Figure 3.1c; choice: F1,17 = 16.97, p = 0.001), but there were no significant differences in premature responding between workers and slackers (choice x group: F1,17 = 0.15, NS; group: F1,17 = 2.33, NS).  Other behavioural measures (see Appendix 1). Animals took the same amount of time to choose LR trials as they did for HR trials (choice: F1,17 = 1.27, NS), and there were no significant differences in this lever choice latency for workers versus slackers (choice x group: F1,17 = 0.56, NS; group: F1,17 = 1.77, NS). Perhaps unsurprisingly, given the shorter stimulus duration, animals were quicker to perform a correct response at the array during HR trials as compared to LR trials (choice: F1,17 = 20.96, p < 0.001) but this did not differ between workers and slackers (choice x group: F1,17 = 0.60, NS; group: F1,17 = 0.02, NS). Animals were quicker to collect reward on successful HR trials than for successful LR trials (choice: F1,17 = 5.55, p = 0.031; choice x group: F1,17 = 0.63, NS). However, a main effect of group was observed on this measure (group: F1,17 = 4.84, p = 0.042) and a closer examination showed that only slackers were faster to collect their reward on HR trials (choice -slackers only: F1,7 = 6.59, p = 0.037; -workers only: F1,10 = 32  1.16, NS). Furthermore, workers and slackers collected equally fast on LR trials (group -LR trials only: F1,17 = 2.52, NS) but slackers collected faster for HR trials as compared to workers (group -HR trials only: F1,17 = 5.62, p = 0.030). Altogether, these data suggest that slackers anticipated a larger reward following successful completion of more difficult (HR) trials, but still chose fewer of these trials than their worker counterparts. Both groups of rats made the same percentage of nosepoke response omissions for both LR and HR trials (choice: F1,17 = 0.01, NS; choice x group: F1,17 = 0.01, NS; group: F1,17 = 0.10, NS), the same number of choice omissions (group: F1,17 = 1.18, NS) and completed the same number of trials per session (group: F1,17 < 0.01, NS).  Effect of d-amphetamine administration on performance of the rCET. Choice behaviour, accuracy, and premature responses. Amphetamine differentially affected preference for HR trials in workers and slackers (Figure 3.3a; dose: F3,51 = 0.94, NS; dose x group: F3,51 = 3.76, p = 0.016). Workers chose significantly more HR trials than slackers when saline or 0.3 mg/kg amphetamine was administered (group -saline: F1,17 = 66.22, p < 0.001; -0.3 mg/kg: F1,17 = 14.03, p = 0.002) but choice behaviour for workers and slackers was indistinguishable after 0.6 and 1.0 mg/kg (group -0.6 mg/kg: F1,17 = 2.70, NS; -1.0 mg/kg: F1,17 = 0.056, NS), as the preference for HR trials decreased in workers and increased in slackers. Individuals’ HR choice in response to the three doses of amphetamine was plotted and from this their gradient of change was taken. These gradients of change were then plotted against their HR choice behaviour at saline condition (Figure 3.4) and revealed a linear, negative correlation (r2 = 0.32, p = 0.01). In other words, there was a gradual shift from increasing HR choice (i.e. for slackers) to 33  decreasing HR choice (i.e. for workers) with amphetamine challenge. Such a shift suggests that individual differences in HR choice may reflect different basal levels of monoaminergic function, and that when monoamine levels are increased via amphetamine, HR choice is correspondingly affected (Mattay et al., 2003).  Amphetamine also had some minor effects on accuracy of target detection (Figure 3.3b), slightly decreasing workers’ accuracy on HR trials at 0.6 mg/kg (choice x dose x group: F3,51 = 4.84, p = 0.005; workers only -choice x dose: F3,30 = 2.68, p = 0.065; dose -HR trials only: F3,30 = 4.97, p = 0.026; -saline vs 0.6 mg/kg: F1,10 = 6.16, p = 0.032). Amphetamine also tended to affect accuracy in slackers (slackers only -dose x choice: F3,21 = 3.07, p = 0.050), but when slackers’ LR and HR trials were considered independently, no effects on accuracy were observed (all Fs < 2.365, NS). In keeping with previous reports, amphetamine dose-dependently increased premature responding in all animals (Figure 3.3c; dose: F3,51 = 5.55, p = 0.008; group: F3,51 = 0.69, NS).  Other behavioural measures (see Appendix 2). Amphetamine had no effect on any response or reward collection latencies (all Fs < 2.29, NS) but dose-dependently increased the number of choice omissions (dose: F3,51 = 7.81, p < 0.001) and decreased the number of completed trials (dose: F3,51 = 6.65, p = 0.001). Response omissions were differentially affected by amphetamine (dose: F3,51 = 1.90, NS; dose x choice: F3,51 = 4.87, p = 0.012): the intermediate dose increased nosepoke omissions for HR trials whereas the highest dose decreased response omissions for LR trials (dose -LR trials only: F3,51 = 2.97, p = 0.065; -saline vs 1.0 mg/kg: F1,17 = 9.26, p = 0.007; -HR trials only: F3,51 = 3.13, p = 0.058; -saline vs 0.6 mg/kg: F1,17 = 7.32, p = 0.015) These effects were 34  not related to animals’ classification as workers or slackers (dose x group / dose x choice x group: all Fs < 0.88, NS).  Effect of ethanol administration on performance of the rCET. Choice behaviour, accuracy, and premature responses. Ethanol had no effects on choice behaviour, accuracy, or premature responses in either workers or slackers (Figure 3.3d-f; all Fs < 1.81, NS).  Other behavioural measures (see Appendix 3). There was a trend for ethanol to dose-dependently speed lever choice latency regardless of which choice the animals made or their relative preference for HR trials (dose: F3,51 = 3.00, p = 0.066; dose x choice: F3,51 = 0.68, NS; group: F3,51 = 0.43, NS). Ethanol also dose-dependently decreased the correct latency for HR trials in all rats (dose x choice: F3,51 = 3.93, p = 0.013; dose -HR trials only: F3,51 = 3.32, p = 0.027; -saline vs 0.6 g/kg: F1,17 = 4.60, p = 0.047). Ethanol had no effect on collection latency (dose: F3,51 = 0.81, NS) or choice omissions (dose: F3,51 = 3.13, NS) but decreased response omissions for LR trials (dose x choice: F3,51 = 6.92, p = 0.006; -LR trials only: F3,51 = 3.60, p = 0.020; -saline vs 0.3 g/kg: F1,17 = 5.62, p = 0.030; -saline vs 0.6 g/kg: F1,17 = 5.21, p = 0.036; dose -HR trials only: F3,51 = 2.32, NS) and increased the number of trials completed (dose: F3,51 = 3.81, p = 0.015; saline vs 0.6 mg/kg: F1,17 = 7.27, p = 0.015). The classification of animals as workers or slackers did not significantly interact with the effects of ethanol for any recorded measures (dose x group / dose x choice x group: all Fs < 2.54, NS).   35  Effect of caffeine administration on performance of the rCET. Choice behaviour, accuracy, and premature responses. When considered as a homogenous group, caffeine had no main effect on choice behaviour (dose: F3,51 = 2.24, NS), although group differences in choice remained (group: F1,17 = 10.30, p = 0.005). Subsequent inspection of the reaction to caffeine in each group revealed that the higher doses of caffeine decreased choice of HR trials in workers (Figure 3.3g; workers only -dose: F3,30 = 4.70, p = 0.008; -saline vs 10 mg/kg: F1,10 = 6.56, p = 0.028; -saline vs 20 mg/kg: F1,10 = 4.69, p = 0.056). In contrast, the drug had no effect on choice behaviour in slackers (slackers only -dose: F3,21 = 0.01, NS). Caffeine had no effects on accuracy in either group (Figure 3.3h; dose: F3,51 = 1.16, NS; dose x group: F3,51 = 0.35, NS) but dose-dependently increased premature responding (Figure 3.3i; dose: F3,51 = 3.26, p = 0.029). This effect tended to be more evident in slackers (dose x group: F3,51 = 2.51, p = 0.069; dose -workers only: F3,30 = 2.43, NS; -slackers only: F3,21 = 7.06, p = 0.033; dose x choice: F3,21 = 7.52, p = 0.001). In this group, the increase in impulsive responding was also most evident during LR trials (choice x dose: F3,21 = 7.52, p = 0.001; dose -LR trials only: F3,21 = 7.18, p = 0.002; -HR trials: F3,21 = 2.74, p = 0.069).  Other behavioural measures (see Appendix 4). Caffeine significantly speeded up choice of the LR option, without affecting the time taken to choose the HR lever (dose: F3,51 = 3.13, p = 0.034; dose x choice: F3,51 = 3.70, p = 0.017; dose -HR trials only: F3,51 = 0.95, NS; -LR trials only: F3,51 = 4.49, p = 0.007; -saline vs 5 mg/kg: F1,17 = 11.37, p = 0.004; -saline vs 20 mg/kg: F1,17 = 12.16, p = 0.003). In contrast, caffeine had no effect on either the correct latency, collection latency, or choice omissions (all Fs < 1.90, NS), but tended to increase response omissions for both trial types (dose: F3,51 = 2.57, p = 36  0.064) and decreased the number of trials completed (dose: F3,51 = 5.14, p = 0.004; -saline vs 20 mg/kg: F1,17 = 6.34, p = 0.022). No significant interactions with group were observed in any of these analyses (dose x group / dose x choice x group: all Fs < 2.06, NS).  Effect of satiation on performance of the rCET. Choice behaviour, accuracy, and premature responses. Satiation significantly decreased the choice of HR trials for all animals, regardless of whether they were workers or slackers (Figure 3.6a; condition: F2,34 = 13.58, p < 0.001; condition x group: F2,34 = 1.52, NS). As compared to food restriction (FR), choice of HR trials was significantly lower at chronic satiation (CS) but not for acute satiation (AS) (condition -FR vs CS: F1,17 = 29.08, p < 0.001; -FR vs AS: F1,17 = 2.97, NS). Similarly, animals chose HR trials significantly less for CS than for AS (condition -AS vs CS: F1,17 = 11.10, p = 0.004). However, workers and slackers maintained their categorical distinction, with workers choosing HR trials significantly more than slackers (group: F1,17 = 11.52, p = 0.003). Additionally, CS eliminated the progressive increase of HR trials across the session (quartile: F3,51 = 1.79, NS). Satiation decreased accuracy across all animals and trial types (Figure 3.6b; condition: F2,34 = 15.68, p < 0.001; condition x group:F2,34 = 0.82, NS; choice x condition: F2,34 = 0.20, NS), but did not affect premature responding (Figure 3.6c; condition: F2,34 = 2.39, NS; condition x group: F2,34 = 0.27, NS).  Other behavioural measures (see Appendix 5). All response and collection latencies were increased by satiation (condition: all Fs > 6.04, all p < 0.006). While correct latency was increased for both trial types (condition -LR trials only: F2,34 = 15.98, 37  p < 0.001; -HR trials only: F2,34 = 9.03, p = 0.002), this latency was longer for LR versus HR trials at AS (LR vs HR, AS: t = 2.89, d.f.=18, p = 0.010) and CS (LR vs HR, CS: t = 2.94,d.f. = 18, p = 0.009) but not at FR (LR vs HR, FR: t = 1.08, d.f. = 18, NS). Satiation increased choice omissions (condition: F2,34 = 5.75, p = 0.007), increased response omissions (condition: F2,34 = 62.58, p < 0.001), and decreased the number of completed trials (condition: F2,34 = 89.43, p < 0.001) for all animals. No interaction with group was observed for any of the above recorded measures (condition x group / condition x choice x group: all Fs < 1.56, NS).  Baseline performance of the control task. Choice behaviour, accuracy, and premature responses. When considered as a homogenous group, control animals chose lower-probability/high-reward (HR) trials more than higher-probability/low-reward (LR) trials (Figure 3.1d; choice: F1,17 = 41.20, p < 0.001). There was no significant difference between the choice behaviour of animals yoked to workers (yoked-workers) and those yoked to slackers (yoked-slackers) (group: F1,17 = 2.06, NS). In contrast to rCET animals, control animals showed only a trend to select fewer HR trials at the beginning and ending of the session (quartile: F3,51 = 2.93, p = 0.074; quartile x group: F3,51 = 0.05, NS). Animals were nominally more accurate for LR versus HR trials (Figure 3.1e; choice: F1,17 = 4.56, p = 0.048) although all accuracies fell between 97 and 98.5% for the control task. There was no difference in accuracy between yoked-workers and yoked-slackers (group: F1,17 = 0.02, NS). Premature responding was higher during HR trials as compared to LR trials, regardless of animals’ 38  yoked groups (Figure 3.1f; choice: F1,17 = 6.97, p = 0.017; choice x group: F1,17 = 0.65, NS; group: F1,17 = 0.01, NS).  Other behavioural measures (see Appendix 6). Lever choice latency and reward collection latency were shorter for HR versus LR trials (choice: all Fs > 5.16, all p < 0.036). All latencies, choice omissions, and completed trials were the same for both yoked-workers and yoked-slackers (group / choice x group: all Fs < 1.83, NS).  Effect of d-amphetamine administration on performance of the control task. Choice behaviour, accuracy, and premature responses. Amphetamine dose-dependently decreased choice of HR trials for all control animals, regardless of yoked grouping (Figure 3.5a; dose: F3,51 = 3.12, p = 0.034; dose -saline vs 1.0 mg/kg: F1,17 = 4.93, p = 0.040; dose x group: F3,51 = 1.77, NS). Amphetamine caused a modest but significant decrease in accuracy of the control task, from 98 to 95%, but there were no significant group effects (dose: F3,51 = 6.92, p = 0.004; dose x group: F3,51 = 3.16, p = 0.059; group: F1,17 = 0.04, NS). Unsurprisingly, control animals also exhibited a dose-dependent increase in premature responding during amphetamine administration, unrelated to animals’ yoked groups (dose: F3,51 = 10.90, p < 0.001; dose x group: F3,51 = 0.20, NS; group: F1,17 = 0.11, NS).  Other behavioural measures (see Appendix 7). Amphetamine had no effects on response or collection latencies, choice omissions, or completed trials for control animals (all Fs < 2.81, NS).   39  Effect of ethanol administration on performance of the control task. Choice behaviour, accuracy, and premature responses. Ethanol tended to reduce choice of HR trials for all control animals, especially at the highest dose (Figure 3.5b; dose: F3,51 = 2.75, p = 0.052; dose -saline vs 1.0 g/kg: F1,17 = 8.29, p = 0.010). Ethanol had no effects on accuracy and premature responding for control animals and no group interactions were observed for any of the above measures (all Fs < 1.55, NS).  Other behavioural measures (see Appendix 8). In all animals, ethanol reduced the latency to choose the LR lever but had no effect on the HR option (dose: F3,51 = 4.31, p = 0.022; choice x dose: F3,51 = 4.41, p = 0.008; dose -LR trials only: F3,51 = 5.42, p = 0.003; -saline vs 0.3 g/kg: F1,17 = 8.38, p = 0.010; -HR trials only: F3,51 = 1.68, NS). Ethanol did not affect the correct response latency for control animals (dose: F3,51 = 0.72, NS; choice x dose: F3,51 = 0.71, NS) but increased the latency to collect reward for all trial types (dose: F3,51 = 3.22, p = 0.030; choice x dose: F3,51 = 2.35, NS). Ethanol did not affect choice omissions (dose: F3,51 = 0.95, NS) but tended to dose-dependently decrease the number of completed trials (dose: F3,51 = 2.65, p = 0.059). No interactions with yoked grouping were observed for the above measures (dose x group / dose x choice x group: all Fs < 2.41, NS).  Effect of caffeine administration on performance of the control task. Choice behaviour, accuracy, and premature responses. Caffeine had no effects on the choice behaviour and accuracy of control animals (Figure 3.5c; dose / dose x choice: all Fs < 2.19, NS). Caffeine dose-dependently increased premature responding during both trial types (dose: F3,51 = 3.30, p = 0.028; dose x choice: F3,51 = 3.61, p = 0.020; dose -LR 40  trials only: F3,51 = 2.89, p = 0.045; -saline vs 20 mg/kg: F1,17 = 5.84, p = 0.028; -HR trials only: F3,51 = 4.00, 0.013; -5 mg/kg: F1,16 = 7.24, p = 0.016; -10 mg/kg: F1,16 = 5.76, p = 0.029). No yoked group interactions were observed (all Fs < 0.42, NS).  Other behavioural measures (see Appendix 9). Caffeine had no effect on response or collection latencies, choice omissions, or completed trials, and no group interactions were observed (all Fs < 2.02, NS).  Effect of satiation on performance of the control task. Choice behaviour, accuracy, and premature responses. Satiation progressively decreased choice of HR trials across all control animals (Figure 3.6d; condition: F2,34 = 11.38, p < 0.001; condition x group: F2,34 = 0.14, NS; condition -FR vs AS: F1,17 = 4.76, p = 0.044; -FR vs CS: F1,17 = 17.21, p = 0.001; -AS vs CS: F1,17 = 9.42, p = 0.007). Yoked-workers and yoked-slackers remained indistinguishable in their choice behaviour (group: F1,17 = 2.10, NS). Satiation had no effect on accuracy or premature responding for control animals (Figure 3.6e-f; all Fs < 1.57, NS).  Other behavioural measurements (see Appendix 10). Satiation increased all response and collection latencies for control animals (condition: all Fs > 19.64, all p < 0.001). Lever choice latency and reward collection latency increased for both LR and HR trials (all Fs > 5.41, all p < 0.020). There was also a significant condition by yoked-group interaction for lever choice latency but further analysis showed significant slowing for both yoked-workers and yoked-slackers (condition x group: F2,34 = 3.81, p = 0.036; -yoked-workers only: F2,20 = 51.47, p < 0.001; -yoked-slackers only: F2,14 = 28.27, p < 0.001). Satiation also increased the number of choice omissions (condition: F2,34 = 24.70, 41  p < 0.001; -FR vs AS: F1,17 = 24.37, p < 0.001; -FR vs CS: F1,17 = 81.81, p < 0.001) and decreased the number of trials completed by control animals (condition: F2,34 = 111.49, p < 0.001; -FR vs AS: F1,17 = 55.56, p < 0.001; -FR vs CS: F1,17 = 236.40, p < 0.001; -AS vs CS: F1,17 = 52.79, p < 0.001). For all measures except lever choice latency, no yoked group effects or interactions were observed (all Fs < 1.83, NS).  Cognitive effort versus control animals: comparison of choice behaviour. Baseline choice behaviour of cognitive effort and control animals was directly compared. There was a significant difference in choice between the two tasks, and a group by task interaction indicated that the worker/slacker distinction did not hold for control animals (task: F1,34 = 5.09, p = 0.031; group x task: F1,34 = 11.78, p = 0.002). There was no difference between the choice behaviour of workers and yoked-workers but yoked-slackers chose HR trials significantly more than slackers (task -workers vs yoked-workers: F1,20 = 0.97, NS; -slackers vs yoked-slackers: F1,14 = 11.43, p = 0.004). Taken together, these data suggest that the differences between the two tasks (i.e. the cognitive effort component) may in part underlie the differences in choice behaviour of experimental and control animals.  3.4 Discussion  Here we show for the first time that rats are sensitive to cognitive effort when making decisions. Similar to human subjects (McGuire and Botvinick, 2010), considerable individual variation in choice was observed when rats chose between a cognitively easy, low-reward (LR) trial and a cognitively difficult, high-reward (HR) trial on a cost/benefit 42  decision-making task. “Workers” chose HR trials significantly more than their “slacker” counterparts, and these choice preferences also influenced the effects of amphetamine and caffeine: workers “slacked off” in response to both of these stimulants, whereas slackers “worked harder” under amphetamine, though not when given caffeine. Baseline individual differences can likewise determine the impact of stimulants on decision-making behaviour in human subjects (Revelle et al., 1980; Anderson and Revelle, 1994; White et al., 2007), hence our model could be useful for exploring the biological basis of these drug effects. Finally, we demonstrated that choice of the more difficult option was sensitive to changes in reward value, as satiation decreased choice of HR trials.  Given that the chances of receiving reward on both easy and hard trials varied according to the animals’ accuracy, one important consideration was the degree to which effort costs, rather than reward probability, influenced decision-making. The performance of a yoked control group demonstrated that choice behaviour on the rCET could not be sufficiently explained by probability of reinforcement for LR and HR trials: control animals were yoked to the same reinforcement probabilities as master rats performing the rCET, but their choice preferences were significantly different. In particular, whereas all yoked-workers and yoked-slackers greatly preferred HR to LR trials, slackers chose significantly fewer HR trials than workers on the rCET, despite equal probabilities of reinforcement. Additionally, both amphetamine and ethanol decreased choice of HR trials for all control animals, shifting to the higher probability but lower reward option; in contrast, amphetamine had opposing effects on workers and slackers and ethanol had no effects on choice in the rCET. As the rCET and control tasks differed only in the 43  cognitive effort component (or lack thereof), it is reasonable to infer that animals’ behaviour on the rCET was critically influenced by the effort costs.  Manipulating satiety levels led to a decrease in choice of HR trials in all animals. The corresponding increased latencies, decreased accuracy, and decreased number of completed trials suggest that the previously food-restricted animals were less motivated for sugar reward following free access to food. This universal decline in performance has also been observed in animals performing a 2-choice reaction task, following their switch from food-restriction to free-feeding (Nakamura and Kurasawa, 2001). In animals performing a physical effort discounting task, acute satiation after food restriction led to decreased choice of HR trials and decreased motor activity (Floresco et al., 2008a), similar to the rCET; however, there was no difference between choice behaviour during food restriction and chronic satiation, unlike in the present study. This is likely due to the much greater number of trials completed in the rCET, which may make the task more sensitive to satiety manipulations. Taken together, the satiation data suggest that the performance of the rCET continues to be goal directed even after extensive training, and that the degree to which rats are willing to exert cognitive effort does depend somewhat on the value of the reward for which they are working.  It is worth noting that all experimental and control rats preferred the HR option at baseline, and did not appear to follow strict matching law (Herrnstein, 1970). However, with a mean probability of reinforcement well above chance for HR trials, this was the normatively favourable choice for all animals. Therefore, it is perhaps surprising that slackers chose the low-effort/LR option twice as often as workers on the cognitive effort task. A number of interpretations can be discredited. As slackers and workers exhibited 44  equal accuracy, differences in attentional performance cannot account for the variation in choice. Similarly, the ability to sustain attention across the 30-minute session cannot account for differences in choice: slackers did not decrease choice of HR trials across the session, but rather all animals increased their HR choice as the session progressed. Workers and slackers also did not differ in levels of motivation or motor impulsivity, as measured by completed trials or premature responding respectively. It is also unlikely that slackers failed to understand the consequences of lever/trial-type choice at the beginning of each trial, as slackers demonstrated a higher level of premature responding and shorter response latencies on HR versus LR trials, identical to workers. This pattern of data suggests some anticipation that the stimulus duration would be shorter on HR trials, eliciting a more rapid response. Another suggestion is that slackers were indifferent to the variation in reward magnitude between LR and HR trials, but this too is unlikely as slackers collected reward faster following HR versus LR trials, and were also quicker to do so than workers on HR trials.  Two hypotheses remain that could explain why slackers chose more LR trials than workers. The first is that slackers may be more sensitive to the probability of reward or punishment. On decision-making tasks involving risk or ambiguity, amphetamine challenge has been shown to shift choice preference in favour of smaller rewards delivered with a higher probability, even at a normative cost across the session (Zeeb et al., 2009; Mitchell et al., 2011), in effect increasing animals’ sensitivity to reward/punishment probability (but see St Onge et al., 2010). Workers and all control animals demonstrated a similar response to amphetamine, increasing choice of the higher accuracy/probability LR trials. However, slackers shifted their preference away from LR 45  trials following amphetamine administration, demonstrating an increased tolerance to lower probabilities of reinforcement. Therefore, it seems unlikely that slackers are more sensitive to reward/punishment probability at baseline, as arguably amphetamine should then have exacerbated this pattern of choice or perhaps failed to alter decision making in these animals due to a floor effect.   A second interpretation is that slackers may simply be more sensitive to the higher cognitive effort costs that accompany HR trials. This possibility is supported by at least one functional imaging study of humans performing a cognitive switching task (McGuire and Botvinick, 2010), wherein “high-avoidant” individuals felt a greater subjective cost for the high-effort trials. These individuals also showed greater activation of brain regions related to the effort costs in this task and subsequently chose fewer high-effort trials. While slackers may experience a greater sense of effort, akin to the high-avoidant participants (McGuire and Botvinick, 2010), it may also be that workers are employing different cognitive strategies to reduce effort costs while maintaining sufficient levels of accuracy (Shah and Oppenheimer, 2008). Moreover, this difference in strategic approach may explain why amphetamine and caffeine caused disparate effects on choice in workers versus slackers, while simultaneously affecting impulsivity and motivation in a similar manner for all animals.  Indeed, the pharmacological manipulations in this study support a dissociation between cognitive effort, visuospatial attention, and impulsivity. Amphetamine and caffeine increased premature responding, a well-validated measure of impulsive action, while leaving accuracy on the task virtually intact; this parallels previous reports in which amphetamine or caffeine administration increased impulsivity without affecting 46  visuospatial attention on the 5CSRTT (Cole and Robbins, 1987; Higgins et al., 2007; Paterson et al., 2011). Similarly, ethanol invigorated behaviour on the rCET, as measured by decreased latencies and increased number of completed trials, but did not affect choice behaviour; this increased motivation corresponds well with both the effects of ethanol administration reported in a 2-choice reaction task (Givens, 1997), and with the lack of effects on accuracy or premature responding using similar doses on the 5CSRTT (Bizarro et al., 2003). In the present study’s control task, where differences in effort costs were removed, ethanol decreased choice of the lower probability (HR) trials, and this is in line with rats’ decreased choice of risky options in a probability-discounting task following ethanol administration (Mitchell et al., 2011). These latter findings support the suggestion that effort and probability discounting may be dissociated at the pharmacological level, a conclusion that is further emphasized by the differential impact of stimulants on the choice behaviour of slackers versus yoked-slackers. Altogether, these data demonstrate that the psychomotor effects of these drugs can be separated from their effects on decision making, as has been previously postulated (Floresco et al., 2008a), and that choice based on cognitive effort may be somewhat unique in its neurochemical regulation.  The adenosine receptor antagonist caffeine decreased choice of HR trials in workers but did not affect decision making in slackers or control animals. Adenosine A2A and dopamine D2 receptors are co-localized within the striatum (Fink et al., 1992) and can have opposing effects on neuronal function (Ongini and Fredholm, 1996), which may in part explain why caffeine and amphetamine displayed some overlapping effects. While A2A receptor antagonists have recently been reported to attenuate decrements in physical 47  effort caused by the D2 antagonist haloperidol (Collins et al., 2011), other findings indicated that A2A agonists have a distinct behavioural profile from D2 antagonists on lever pressing (Jones-Cage et al., 2011). In combination with the unaffected choice behaviour of control animals under caffeine, this suggests that caffeine’s effects were due to changes in arousal or allocation of attention rather than motivational processes per se. Although striatal A2A receptors have been implicated in the arousing effects of caffeine (Huang et al., 2005; Lazarus et al., 2011), adenosine A1 and A2A receptor-mediated acetylcholine efflux in the cortex may also underlie this arousal (Van Dort et al., 2009). Attentional performance on the 5CSRTT is sensitive to cholinergic manipulation (McGaughy et al., 2002; Chudasama et al., 2004; Dalley et al., 2004), and in contrast to amphetamine and caffeine from the present study, acute administration of the acetylcholine agonist nicotine can sometimes improve performance on the 5CSRTT (Bizarro et al., 2004; Stolerman et al., 2009). Furthermore, increases in cortical cholinergic tone accompany sustained attention in rats (Passetti et al., 2000), and effort-reducing strategies decrease this acetylcholine efflux, as measured by microdialysis (Himmelheber et al., 2000; Dalley et al., 2001). At least one human study has demonstrated that caffeine can impair visuospatial orientation while leaving other aspects of attention intact (Brunye et al., 2010). As such, caffeine-mediated increases in cortical acetylcholine may have artificially inflated the sense of attentional effort in the current task, driving HR-preferring workers toward the easier LR trials while leaving slackers and control animals unaffected.  Differences between the present rCET data and previous physical effort literature also provide evidence that effort may not be a unitary construct. Physical effort studies 48  have highlighted the importance of dopaminergic outputs from the ventral tegmental area in signalling effort costs, and disruptions to midbrain dopamine signalling will shift animals’ behaviour toward options with lower effort requirements (Cousins et al., 1994; Salamone et al., 1994; Salamone et al., 2007). Conversely, systemic amphetamine (0.7 mg/kg) increased animals’ preference to scale a barrier for greater reward in a T-maze task (Bardgett et al., 2009). Dopaminergic antagonists counteracted these effects, suggesting that amphetamine’s actions on this physical effort task were mediated in part via dopamine (Bardgett et al., 2009). Similarly, the general dopamine antagonist flupenthixol shifted animals’ choice away from HR trials on the operant effort-discounting task, which requires animals to press the HR lever a greater number of times for a larger reward (Floresco et al., 2008a). However, in contrast to the T-maze task, low doses of amphetamine increased choice of the high-effort option while a higher dose (0.5 mg/kg) decreased choice of HR trials (Floresco et al., 2008a). As to why somewhat divergent effects of amphetamine are observed on the two physical effort paradigms, it is worth noting that disruptions of dopamine exert a greater influence on tasks that have higher physical work requirements (Salamone et al., 2007). As such, the physical costs of scaling a large barrier in a T-maze may be greater than pressing an operant lever multiple times, hence the greater impairments on the maze-based task. Moreover, this could also explain why we found no main effect of amphetamine on choice for the cognitive effort task when the animals were considered as a homogenous sample: the physical effort required for the rCET is very low and equal for LR versus HR trials, and thus minimally affected by dopaminergic manipulations. Consequently, amphetamine’s actions on choice behaviour in the rCET may not be primarily dopamine-dependent, but perhaps due to 49  amphetamine’s effect on other catecholamines, acetylcholine, or the downstream targets of these neuromodulators.  This theme of overlapping-yet-distinct neurochemistry may also extend to neurocircuitry when considering cognitive versus physical effort. A substantial body of literature implicates regions within the cortico-limbic-striatal loop in decision-making based on physical effort requirements, including the nucleus accumbens (Salamone et al., 1994; Cousins et al., 1996), anterior cingulate cortex (Walton et al., 2003a; Rudebeck et al., 2006b), and basolateral amygdala (Floresco and Ghods-Sharifi, 2007b; Ghods-Sharifi et al., 2009). Lesions or inactivations of these regions shift animals’ choice behaviour away from high-effort options. Although some functional imaging studies of cognitive effort have shown overlap with these regions (Botvinick et al., 2009), others have shown associations with lateral prefrontal cortex and superior parietal lobule (McGuire and Botvinick, 2010), regions involved in executive control and visuospatial attention, respectively (Miller and Cohen, 2001; Cavanna and Trimble, 2006). A number of studies have also emphasized concurrent or globally synchronized activity across the brain for higher cognitive effort (Barnes et al., 2009; Kitzbichler et al., 2011). From a theoretical perspective, the currency of the costs associated with each task (i.e. physical/motor versus cognitive/attentional) may account for some distinct regional activity within the brain.  In summary, the data presented here demonstrate that rats can discriminate between two options that vary in the amount of cognitive effort required, and that stable baseline differences are observed in terms of the degree of preference for more effortful trials. This choice behaviour can be pharmacologically manipulated without impairing 50  task performance and appears to be independent of other cognitive processes such as impulsivity. Furthermore, drugs may have opposing effects on choice behaviour for “workers” versus “slackers”, perhaps indicating that these two groups are using different strategies when performing the task, which could result in different patterns of neural activation. The rCET may therefore offer insights into the underlying neurobiology of individual differences in effortful choice; in turn, such research may suggest novel therapeutic targets for the many disorders that are associated with impairments in effort-based decision making.   51  3.5 Figures  Figure 3.1. Baseline behaviour on the rCET and control task.  (A) All animals showed a preference for high-effort/high-reward (HR) trials (choice: p < 0.001), but there was considerable individual variation in choice: animals above the mean for HR choice (“workers”) chose HR trials significantly more than animals below the mean (“slackers”)(group: p < 0.001). (B) Despite differences in choice, workers and slackers performed equally well on both low-effort/low-reward (LR) and HR trials. Accuracy was significantly lower for HR versus LR trials (choice: p < 0.001). (C) All animals exhibited greater premature responding for HR versus LR trials (choice: p = 0.001), and no significant differences between workers and slackers were observed for this measure of motor impulsivity. (D) When cognitive effort costs were removed and 52  control animals were yoked to experimental animals’ rates of reinforcement (i.e. accuracy), control animals preferred the normatively favourable HR trials (choice: p < 0.001). Unlike the rCET, however, no significant differences in choice were observed for yoked-workers versus yoked-slackers. (E) All control animals were nominally more accurate for LR versus HR trials (choice: p = 0.048), although accuracy was above 97% for both trial types. (F) For all control animals, premature responding was higher for HR versus LR trials (choice: p = 0.017). Data are shown as the mean percent for each option (± SEM).   Figure 3.2. HR choice across trials in baseline sessions, as divided by quartiles.  When baseline sessions were divided into quartiles, with equal numbers of trials in each quartile, all animals exhibited a progressive increase in choice of HR trials across the session (quartile: p < 0.001). Data are shown as the mean percent choice for each option (± SEM).  53   Figure 3.3. Pharmacological manipulation of cognitive effort is dissociated from visuospatial attention and motor impulsivity.  (A) d-amphetamine significantly attenuated the heterogeneity of choice behaviour, making workers “slack off” and slackers “work harder” (dose x group: p = 0.016). Choice 54  behaviour for workers and slackers was significantly different at saline and 0.3 mg/kg (group: all p < 0.002) but indistinguishable after 0.6 and 1.0 mg/kg. (B) Although mild impairments of accuracy were observed for workers’ high-effort/high-reward (HR) trials at 0.6 mg/kg (p = 0.032), amphetamine had no main effect on accuracy for all animals. (C) Despite amphetamine’s opposing effects on the choice behaviour of workers and slackers, the psychostimulant significantly increased motor impulsivity for all animals in both trial types (dose: p = 0.008). (D-F) Ethanol had no effects on choice, accuracy, or premature responding for all animals. (G) Caffeine reduced workers’ choice of HR trials (dose: p = 0.008) but had no effect on slackers’ choice behaviour. (H) Caffeine had no effects on accuracy for all animals. (I) Caffeine modestly increased premature responding for all animals when both trial types were considered together (dose: p = 0.029). Data are shown as the mean percent for each option (± SEM).   55   Figure 3.4. Gradients of change for HR choice in response to amphetamine challenge on the rCET.  Individuals’ responses to the three doses of amphetamine were plotted and from this their gradients of change were derived. These gradients of change were then plotted against HR choice under saline condition, and a negative linear correlation was observed (r2 = 0.32, p = 0.01). This indicates a progressive shift from positive gradients in slackers, wherein amphetamine increases HR choice, to negative gradients in workers, wherein amphetamine decreases HR choice.  56   Figure 3.5. Pharmacological manipulation of choice behaviour on the control task.  (A) Amphetamine decreased choice of lower-probability/high-reward (HR) trials for all animals on the yoked control task, where effort costs were removed (dose: p = 0.034; saline vs 1.0 mg/kg: p = 0.040). (B) Ethanol caused a modest decrease in the choice of HR trials when all control animals were considered together (dose: p = 0.052; saline vs 1.0 g/kg: p = 0.010). (C) Caffeine had no effects on choice in the control task. Data are shown as the mean percent for each option (± SEM).  57   Figure 3.6. Satiation decreases motivation on the rCET.  (A) Satiation (acute, AS, and chronic, CS) reduced choice of high-effort/high-reward (HR) trials on the rCET, as compared to food restriction (FR), for both workers and slackers (condition: p < 0.001; FR vs CS: p < 0.001; AS vs CS: p = 0.004). (B) This decrease in choice of HR trials was accompanied by general impairments in accuracy on the rCET (condition: p < 0.001). (C) Motor impulsivity was not affected by satiety. (D) In the control task, where cognitive effort costs were eliminated, all yoked animals also reduced choice of the lower probability HR trials in response to satiation (condition: p < 0.001). (E-F) Satiety had no effect on accuracy or premature responding on the control task. Data are shown as the mean percent for each option (± SEM).  58  Chapter 4: Experiment 2  Dopamine antagonism decreases willingness to expend physical, but not cognitive, effort: a comparison of two rodent cost/benefit decision-making tasks  4.1 Introduction  Critical decisions in life often require weighing a given option’s costs against its associated benefits, and virtually every severe mental illness is associated with difficulties in such cost/benefit decision making (Caceda et al., 2014; Goschke, 2014). For one such cost, the effort to obtain a reward, a number of animal models have been developed: rats are given the option to climb a barrier in a T-maze in one task, or to make a greater number of responses on a lever in another, to obtain a larger food reward (Salamone et al., 1994; Ghods-Sharifi et al., 2009).  Overwhelmingly these studies implicate mesolimbic dopamine in our willingness to exert effort (Salamone, 2009). Dopamine antagonism reliably decreases animals’ choice of high-effort/high-reward (HR) options, while the psychostimulant amphetamine, which facilitates dopamine transmission, typically increases choice of HR options (Denk et al., 2005; Floresco et al., 2008a; Bardgett et al., 2009). Dopaminergic projections to brain regions implicated in decision making also play a role in effortful choice (Cousins et al., 1996; but see Walton et al., 2005; Schweimer and Hauber, 2006).  Until recently, however, animal models have invariably manipulated the degree of physical effort, whereas human studies of effort have relied on cognitive costs (Naccache et al., 2005; Kool et al., 2010). Broadly, cognitive or mental effort costs are those that are 59  non-physical in nature, tax limited neurobiological resources, and are reflected in psychological constructs such as attention, response inhibition, and working memory; perhaps unsurprisingly, the underlying circuitry for cognitive versus physical effort appears in part distinct (Schmidt et al., 2012; Hosking et al., 2014). To account for the discrepancy in the literature, human studies have begun to incorporate physical costs in decision-making paradigms (Treadway et al., 2009b) and shown a similar involvement of dopamine in human decision making involving physical effort (Wardle et al., 2011; Treadway et al., 2012b).  The converse approach, applying cognitive effort costs to animal models, allows for examination of mental effort in ways inaccessible to human studies. Our group has recently validated a rodent Cognitive Effort Task (rCET), wherein animals can choose to allocate greater visuospatial attention for a greater reward, and shown that amphetamine’s effects on the task are mediated by animals’ individual sensitivity to the effort costs (Experiment 1). This finding is distinct from the physical effort literature, and while it may be dopaminergic in origin, amphetamine also facilitates transmission of other neuromodulators such as norepinephrine, serotonin, and acetylcholine (Mandel et al., 1994). To the best of our knowledge, dopamine’s relationship to cognitive effort has not been directly examined, nor has the relationship between individuals’ willingness to expend cognitive versus physical effort; it is unclear whether willingness to work hard in one domain corresponds to willingness in the other. These are important research questions, as effort costs in industrialized society are predominantly cognitive in nature, and thus societally relevant to novel therapeutic interventions. 60   The goal of this study was therefore twofold: to compare animals’ behaviour on the rCET to a well-established task of physical effort (Floresco et al., 2008a), and to examine dopaminergic and noradrenergic contributions to cognitive versus physical effort.  4.2 Additional methods   The (physical) Effort-Discounting Task (EDT)  The experimental timeline is presented in Figure 4.1a. The cohort was divided in half once baseline behaviour on the rCET had stabilized (30-35 free-choice sessions); 27 animals were switched to the EDT (workers: n = 20; slackers: n = 7), a physical-effort decision-making task that has been well-described elsewhere (e.g. Floresco et al., 2008a), and is presented in Figure 4.1c. Within the EDT, animals received 40 free-choice trials per 32min session, divided equally into four blocks. New trials were presented every 40s with illumination of the tray light, followed by the extension of the levers. Lever contingencies (LR or HR) were reversed from the rCET to avoid the confound of perseverative responding from one task to the other. If animals responded on the LR lever, both levers retracted and the animal immediately received 2 sugar pellets; this cost (i.e. a single lever press, FR1) remained constant for LR trials across the session. If animals responded on the HR lever, the LR lever retracted and animals were given 25s to complete a higher number of presses for 4 sugar pellets. The HR costs increased across the session, beginning with FR2 in the first block, followed by FR5, FR10, and FR20. 61   Animals did not receive reward if they did not make a choice within 25 sec of lever insertion (choice omission) or if they failed to complete the required number of lever presses for a HR trial (incomplete HR response). As animals were experienced in lever pressing to obtain reward, choice omissions and incomplete HR responses occurred less than once per session per animal from the outset, and were virtually absent by the end of baseline EDT (15 sessions).  Behavioural measurements for the EDT  Percent choice was used for LR or HR options/levers in each block. Average latency to complete HR choices (choice latency), choice omissions, and incomplete HR responses were also measured.  Pharmacological challenges  Upon stable baseline behaviour in each respective task, drugs were administered in the following order: the dopamine D2 antagonist eticlopride (0, 0.01, 0.03, 0.06mg/kg), dopamine D1 antagonist SCH23390 (0, 0.001, 0.003, 0.01mg/kg), the α2-adrenergic receptor antagonist yohimbine (0, 1, 2, 5mg/kg), and the selective norepinephrine reuptake inhibitor atomoxetine (0, 0.1, 0.3, 1.0mg/kg). S-(−)-Eticlopride hydrochloride, R(+)-SCH-23390 hydrochloride, and yohimbine hydrochloride were purchased from Sigma-Aldrich (Oakville, ON, Canada); tomoxetine hydrochloride was purchased from Tocris (Minneapolis, MN, USA). Eticlopride, SCH23390, and atomoxetine were dissolved in 0.9% sterile saline, and yohimbine was dissolved in distilled water. All were prepared fresh daily and administered in a volume of 1ml/kg via intraperitoneal injection, 62  adhering to a digram-balanced Latin Square design (Cardinal and Aitken, 2006). The three-day injection schedule started with a baseline session, followed by a drug or saline injection session, and then by a non-testing day. Injections for eticlopride, SCH23390, and yohimbine were administered 10min before behavioural testing; atomoxetine injections were administered 30 minutes beforehand. Animals were given one week of drug-free testing between compounds to minimize carryover effects.  Reward-magnitude manipulations  Following one week of drug-free testing, reward magnitudes were manipulated for the subgroup of animals that remained on the rCET. At standard conditions, successful LR trials earned animals 1 sugar pellet, while successful HR trials earned 2 sugar pellets (1v2); in the following sessions, both LR and HR trials earned animals 2 sugar pellets when completed successfully (2v2); next, reward size was decreased to 1 sugar pellet for both options (1v1); and finally, the LR/HR distinction was magnified, with successful HR trials earning animals 4 sugar pellets (1v4). Percent choice was averaged across three sessions for each of these manipulations.  4.3 Results  rCET: Eticlopride administration Following the acquisition of stable behaviour on the rCET, half of the animals were switched to the EDT, while the other half (n = 28) were given the following pharmacological challenges on the rCET. 63   Baseline behaviour for the rCET has been previously discussed at length (Cocker et al., 2012a; Hosking et al., 2014), and as such will only be cursorily addressed here. As per previous cohorts, animals chose high-effort/high-reward (HR) trials more than low-effort/low-reward (LR) trials following saline injection (saline only—choice: F1,26 = 13.461, p = 0.001), and workers chose a significantly higher proportion of HR trials than slackers (group: F1,26 = 40.814, p < 0.001). The dopamine D2 receptor antagonist eticlopride had no effect on animals’ choice (Figure 4.2a; dose: F3,78 = 1.222, NS).  Animals were more accurate (i.e. demonstrated better performance) on LR versus HR trials (saline only—choice: F1,26 = 21.657, p < 0.001). As per previous cohorts, workers and slackers performed the rCET equally well (saline only—group / choice x group: all Fs < 1.350, NS). This reiterates that choice preferences were not driven solely by individuals’ ability to perform the task. Eticlopride had no effect on animals’ accuracy (Figure 4.2b; dose / dose x group / choice x dose / choice x dose x group: all Fs < 2.230, NS).  In general, premature responding was higher for HR versus LR trials (choice: F1,26 = 4.511, p = 0.043) but there were no differences in premature responding between workers and slackers (group / choice x group: all Fs < 0.809, NS), indicating that choice preferences were not driven by individuals’ motor impulsivity. Eticlopride had no effect on animals’ rates of premature responding (Figure 4.2c; dose / dose x group / choice x dose / choice x dose x group: all Fs < 1.489, NS).  Other behavioural measures. Eticlopride had no effect on the amount of time animals took to choose between LR and HR levers (dose / choice x dose: all Fs < 1.525, NS), and no differences in this choice latency were observed between workers and 64  slackers (group / dose x group / choice x dose x group: all Fs < 0.521, NS). Animals took equally long to choose between LR and HR options (saline only—choice: F1,26 = 0.120), although there was a trend for animals to choose their preference (e.g. HR for workers) faster (saline only—choice x group: F1,26 = 4.039, p = 0.055; —workers only / —slackers only—choice: all Fs < 3.460, NS). Eticlopride significantly increased the time taken to make a correct nosepoke response, for all animals across both trial types (dose: F3,78 = 3.030, p = 0.034; dose x group / choice x dose / choice x dose x group / group: all Fs < 1.243, NS). Correct responses were equally fast for LR versus HR trials, for workers and slackers (saline only—choice / choice x group / group: all Fs < 2.441, NS). As previously reported, all animals collect reward faster following HR trials versus LR trials (saline only—choice: F1,26 = 41.959; choice x group: F1,26 = 2.229, NS), with a trend for slackers to collect reward faster than workers (group: F1,26 = 3.988, p = 0.056); as previously discussed (Experiment 1), this suggests that slackers understand the contingencies of the task and are not indifferent to reward magnitude, despite their reduced preference for high-effort trials. Eticlopride had no main effect on this collection latency (dose / dose x group / choice x dose: all Fs < 2.514, NS; choice x dose x group: F3,78 = 4.079, p = 0.028; workers only—LR / HR, slackers only—LR / HR —dose: all Fs < 1.885, NS). All animals failed to respond by nosepoke equally for LR versus HR (saline only—choice / choice x group / group: all Fs < 0.773, NS). Eticlopride increased these response omissions for all animals across all trial types (dose: F3,78 = 6.300, p = 0.003; dose x group / choice x dose / choice x dose x group: all Fs < 2.150, NS). Eticlopride also dose-dependently increased the number of lever (choice) omissions for all animals (dose: F3,78 = 4.201, p = 0.038; dose x group / group: all Fs < 0.394, NS) and decreased the number 65  of completed trials for all animals (dose: F3,78 = 9.358, p = 0.002; dose x group / group: all Fs < 0.598, NS).  rCET: SCH23390 administration The dopamine D1 receptor antagonist SCH23390 had no effect on choice, accuracy, or premature responding for the rCET (Figure 4.2d-f; dose / dose x group / choice x dose / choice x dose x group: all Fs < 2.132, NS).  Other behavioural measures. For all animals, SCH23390 increased the latency to choose between LR and HR levers/options (dose: F3,78 = 5.245, p = 0.002; dose x group / choice x dose / choice x dose x group: all Fs < 1.744, NS). In general, SCH23390 had an U-shaped effect on correct nosepoke responding: the lowest dose shortened correct latency, while the highest dose lengthened it (dose: F3,78 = 6.186, p = 0.009; dose x group: F3,78 = 6.367, p = 0.001; choice x dose: F3,78 = 5.059, p = 0.019; choice x dose x group: F3,78 = 3.242, p = 0.026; workers only—dose: F3,57 = 5.273, p = 0.003; —saline vs low—dose: F1,19 =16.311, p = 0.001; —saline vs med—dose: F1,19 = 2.972, NS; —saline vs high—dose: F1,19 = 4.535, p = 0.047; slackers only—dose: F3,21 = 2.803, NS). SCH23390 had no effect on the latency to collect reward following a successful trial (dose / dose x group / choice x dose / choice x dose x group: all Fs < 0.302, NS). SCH23390 also increased nosepoke response omissions for all animals across both trial types (dose: F3,78 = 4.447, p = 0.024; dose x group / choice x dose / choice x dose x group: all Fs < 1.628, NS) but had no effect on lever (choice) omissions (dose / dose x group: all Fs < 0.675, NS). Finally, SCH23390 decreased the number of completed trials for all animals (dose: F3,78 = 10.864, p = 0.002; dose x group: F3,78 = 0.390, NS). 66   rCET: Yohimbine administration The α2-adrenergic receptor antagonist yohimbine did not affect animals’ choice behaviour (Figure 4.2g) or premature responding (Figure 4.2i; dose / dose x group: all Fs < 0.978, NS). For all animals across both trial types, however, yohimbine dose-dependently decreased accuracy, an effect that achieved significance at the highest dose (Figure 4.2h; dose: F3,78 = 7.314, p = 0.006; dose x group / choice x dose / choice x dose x group: all Fs < 2.276, NS; saline vs low—dose: F1,26 = 2.948, NS; saline vs med—dose: F1,26 = 3.665, p = 0.067; saline vs high—dose: F1,26 = 13.640, p = 0.001).  Other behavioural measures. Yohimbine had a U-shaped effect on the time taken by all animals to choose between LR and HR levers: the low and intermediate doses shortened choice latency, while the highest dose did not differ from saline (dose: F3,78 = 11.434, p < 0.001; dose x group / choice x dose: all Fs < 1.344, NS; choice x dose x group: F3,78 = 3.210, p = 0.053; saline vs low—dose: F1,26 = 35.029, p < 0.001; saline vs med—dose: F1,26 = 8.740, p = 0.007; saline vs high—dose: F1,26 = 1.162, NS). Latency to make a correct nosepoke response demonstrated a similar U-shaped effect to yohimbine, with low and intermediate doses shortening correct latency, while the high dose did not differ from saline (dose: F3,78 = 4.546, p = 0.035; dose x group / choice x dose / choice x dose x group: all Fs < 1.761, NS; saline vs low—dose: F1,26 = 8.682, p = 0.007; saline vs med—dose: F1,26 = 8.709, p = 0.007; saline vs high—dose: F1,26 = 2.241, NS). For all animals on both trial types, yohimbine also speeded latency to collect reward following successful trials (dose: F3,78 = 5.021, p = 0.010; dose x group / choice x dose / choice x dose x group: all Fs < 0.640, NS). Yohimbine modestly decreased nosepoke response 67  omissions at the lowest dose, and dramatically increased response omissions at the highest dose (dose: F3,78 = 19.749, p < 0.001; dose x group / choice x dose x group: all Fs < 2.353, NS; choice x dose: F3,78 = 3.008, p = 0.059; saline vs low—dose: F1,26 = 4.313, p = 0.048; saline vs med—dose: F1,26 = 0.074, NS; saline vs high—dose: F1,26 = 20.621, p < 0.001). Similarly, the highest dose of yohimbine increased lever (choice) omissions for all animals, while the low and intermediate doses had no effect (dose: F3,78 = 15.428, p < 0.001; dose x group: F3,78 = 0.027, NS; saline vs low / saline vs med—dose: all Fs < 1.189, NS; saline vs high—dose: F1,26 = 15.269, p = 0.001). Interestingly, the low and intermediate doses of yohimbine increased the number of completed trials for all animals, while the highest dose of yohimbine greatly decreased trials (dose: F3,78 = 36.211, p < 0.001; dose x group: F3,78 = 1.341, NS; saline vs low—dose: F1,26 = 27.365, p < 0.001; saline vs med—dose: F1,26 = 6.269, p = 0.019; saline vs high—dose: F1,26 = 29.316, p < 0.001).  rCET: Atomoxetine administration Choice behaviour, accuracy, and premature responses. The selective norepinephrine reuptake inhibitor atomoxetine had no effect on animals’ choice (Figure 4.2j) and premature responding (Figure 4.2l; dose / dose x group / choice x dose / choice x dose x group: all Fs < 1.680, NS) and virtually no effect on accuracy, with only a trend to decrease workers’ performance on LR trials (Figure 4.2k; dose / dose x group / choice x dose: all Fs < 2.172, NS; choice x dose x group: F3,78 = 3.124, p = 0.031; workers only—LR only—dose: F3,57 = 3.141, p = 0.066; workers only—HR only / slackers only—LR / HR—dose: all Fs < 1.252, NS). 68   Other behavioural measures. For all animals across both trial types, atomoxetine increased the time needed to choose between the LR and HR levers/options (dose: F3,78 = 4.400, p = 0.007; dose x group / choice x dose / choice x dose x group: all Fs < 0.992, NS). Atomoxetine did not affect the latency to make a correct nosepoke response, save for a trend increase slackers’ HR correct response latency at the lowest dose (dose / dose x group / choice x dose: all Fs < 0.716, NS; choice x dose x group: F3,78 = 3.554, p = 0.018; workers only—LR / HR / slackers only—LR—dose: all Fs < 1.813, NS; slackers only—HR only—dose: F3,21 = 2.741, p = 0.069). Atomoxetine also had no effect on collection latency (dose / dose x group / choice x dose / choice x dose x group: all Fs <1.603, NS) or response omissions (dose / dose x group / choice x dose / choice x dose x group: all Fs < 1.896, NS) but modestly increased lever (choice) omissions (dose: F3,78 = 3.779, p = 0.050; dose x group: F3,78 = 0.859, NS) and decreased the number of completed trials (dose: F3,78 = 11.803, p < 0.001; dose x group: F3,78 = 0.231, NS).  EDT: baseline behaviour and comparison to rCET As discussed above, half of the rats (n = 27) were switched from baseline rCET to the EDT prior to any drug challenges. Upon switching to the EDT, animals demonstrated high performance during the first three sessions, with less than one incomplete HR trial per animal per session, on average, and virtually zero choice omissions. Furthermore, while choice behaviour was not yet stable (session: F2,50 = 10.628, p = 0.001), all animals demonstrated sensitivity to the physical effort costs: choice of HR decreased across blocks as the costs increased (Figure 4.3a; block: F3,75 = 8.333, p = 0.001; block x group: F3,75 = 0.510, NS). Remarkably, and despite reversing the lever/reward contingencies 69  from the rCET to the EDT, the worker/slacker distinction held during these early sessions of the EDT: animals that had been deemed “workers” for the rCET remained workers for the EDT, and likewise for slackers (group: F1,25 = 6.351, p = 0.018). Baseline choice behaviour on the rCET was linearly correlated with choice behaviour on sessions 1-3 of the EDT (Figure 4.3b; adjusted r2 = 0.358, p = 0.001).  However, upon reaching stability at sessions 13-15 (session / session x block / session x block x group: all Fs < 1.359, NS), the worker/slacker distinction was no longer valid for the EDT (Figure 4.3c; group: F1,25 = 1.273, NS), with no correlation to baseline behaviour on the rCET (Figure 4.3d; adjusted r2 = 0.039, NS), although animals were still sensitive to the increasing physical effort costs, overall (block: F3,75 = 4.607, p = 0.005).  EDT: Eticlopride administration Following the establishment of baseline behaviour, four animals no longer sampled from both options/levers, instead pressing the LR lever exclusively. Due to the inflexibility of this behaviour, drug challenge data from these animals was not included in analyses. Furthermore, one animal was removed from the study due to unexpected, unrelated health complications, leaving a total of 22 animals in this subgroup (workers: n = 16; slackers: n = 6).   The dopamine D2 receptor antagonist eticlopride dose-dependently decreased all animals’ choice of HR trials across all blocks (Figure 4a; dose: F3,63 = 2.975, p = 0.038; dose x group / dose x block / dose x block x group: all Fs < 1.395, NS; saline vs high—dose: F1,21 = 3.900, p = 0.062; saline vs low / saline vs med—dose: all Fs < 0.293, NS). Eticlopride also increased the time needed to complete a HR choice, especially at the 70  highest dose and for the highest effort (i.e. FR20) block (dose: F3,30 = 3.296, NS; dose x block: F9,90 = 4.105, p = 0.039; dose x group / dose x block x group: all Fs < 0.382; saline vs high—dose: F1,10 = 5.261, p = 0.045; saline vs low / saline vs med—dose: all Fs < 2.178, NS; FR20 only—dose: F3,33 = 4.599, p = 0.033; FR2 / FR5 / FR10 only—dose: all Fs < 3.024, NS). Eticlopride had no effect on the number of incomplete HR responses (F3,63 = 2.067, NS) but modestly increased the number of choice omissions, although choice omissions remained well below a single instance per animal per session even at the highest dose (dose: F3,63 = 3.165, p = 0.030; saline vs low / saline vs med / saline vs high—dose: all Fs < 3.424, NS).  EDT: SCH23390 administration The dopamine D1 receptor antagonist SCH23390 decreased HR choice at the highest effort block (Figure 4b; dose x block: F3,63 = 3.316, p = 0.009; FR20 only—dose: F3,63 = 5.165, p = 0.003; FR2 / FR5 / FR10 only—dose: all Fs < 1.783, NS; dose / dose x group / dose x block x group: all Fs < 1.986, NS). SCH23390 also modestly increased the latency to complete HR trials (dose: F3,39 = 3.773, p = 0.018; dose x group / dose x block / dose x block x group: all Fs < 1.935, NS). SCH23390 had no effect on incomplete HR responses (dose / dose x group: all Fs < 0.342, NS) but increased the number of choice omissions, again remaining below a single instance per animal at the highest dose (dose: F3,63 = 3.275, p = 0.027; dose x group: F3,63 = 1.445, NS).    71  EDT: Yohimbine administration The α2-adrenergic receptor antagonist yohimbine appeared to have some minor effects on choice behaviour, decreasing choice of the HR lever during the first two blocks, but this effect was not robust, as evidenced by the lack of a dose x block effect (Figure 4c; dose: F3,60 = 2.506, p = 0.067; dose x group / dose x block / dose x block x group: all Fs < 1.641, NS; FR2 only—dose: F3,60 = 3.570, p = 0.019; FR5 only—dose: F3,60 = 3.150, p = 0.031; FR10 / FR20 only—dose: 1.434, NS). Yohimbine also lengthened the latency to complete HR trials for each block (dose: F3,39 = 9.147, p < 0.001; dose x block: F9,117 = 3.257, p = 0.001; FR2 only—dose: F3,48 = 4.219, p = 0.010; FR5 only—dose: F3,48 = 9.496, p < 0.001; FR10 only—dose: F3,48 = 3.705, p = 0.018; FR20 only—dose: F3,48 = 3.131, p = 0.034; dose x group / dose x block x group: all Fs < 1.567, NS). Incomplete HR responses and choice omissions remained at zero for all animals at all doses.  EDT: Atomoxetine administration The selective norepinephrine reuptake inhibitor atomoxetine had no effect on any behavioural measures of the EDT (Figure 4d; dose / dose x group / dose x block / dose x block x group: all Fs < 2.909, NS).  rCET: Reward magnitude manipulations Following all drug challenges on the rCET, reward magnitudes were manipulated for LR and HR options. All animals demonstrated sensitivity to the reward magnitudes, choosing fewer HR trials as the HR option was disincentivized (Figure 4.5; condition: F3,78 = 9.861, p < 0.001; condition x group: F3,78 = 0.785, NS); at 1 pellet for LR versus 4 pellets 72  for HR (1v4) and baseline conditions (1v2), animals chose HR significantly more often than at 1v1 (1v4 vs 1v1—condition: F1,26 = 19.144, p < 0.001; 1v2 vs 1v1—condition: F1,26 = 12.002, p = 0.002; 1v4 vs 1v2 / 1v4 vs 2v2 / 1v2 vs 2v2—condition: all Fs < 2.862, NS). Animals demonstrated further sensitivity to the reward by increasing the number of completed trials as HR rewards diminished (condition: F3,78 = 12.951 p < 0.001; 1v4 vs 1v2: F1,26 = 8.347, p = 0.008; 1v4 vs 1v1: F1,26 = 22.913, p < 0.001; 1v4 vs 2v2: F1,26 = 0.465, NS), and unsurprisingly, manipulating reward magnitude had a greater impact on the number of completed trials for workers versus slackers (condition x group: F3,78 = 3.332, p = 0.024; workers only—condition: F3,57 = 17.905, p < 0.001; slackers only—condition: F3,21 = 4.797, p = 0.011). While disincentivizing the HR option decreased all animals’ choice of HR, the worker/slacker distinction remained at all conditions (1v4—group: F1,26 = 32.156, p < 0.001; 1v2—group: F1,26 = 34.590, p < 0.001; 2v2—group: F1,26 = 35.393, p < 0.001; 1v1—group: F1,26 = 27.802, p < 0.001).  It should be noted that animals’ shifts due to these reward magnitude manipulations are not as profound as those seen in some manipulations of effort paradigms (Rudebeck et al., 2006b). This is likely explained by a much larger number of rewarded trials on the rCET, and thus a rationally slow shift away from a previously optimal option (Bouton et al., 2014), although future experiments should hold reward magnitude manipulations for longer than the current three-session block to be certain.  4.4 Discussion  Here we show for the first time that antagonism of either D1-family or D2-family dopamine receptors, as well as norepinephrine facilitation, had no discernible effect on 73  animals’ willingness to expend mental effort. Although these drugs had observable effects on other behavioural measures (e.g. increased latencies, decreased trials), dopamine antagonism did not decrease choice of high-effort/high-reward (HR) options, in contrast with observations in physical effort paradigms (Bardgett et al., 2009; Nunes et al., 2010). Indeed, here we show that both D1-family and D2-family antagonism, as well as the pharmacological stressor yohimbine, decrease choice of HR on a previously established task of physical effort, the EDT; these data are novel, as previous pharmacological EDT studies have only utilized the nonspecific dopamine antagonist flupenthixol (Floresco et al., 2008a) and have not addressed norepinephrine specifically. In addition to the pharmacological challenges, a transient correlation was demonstrated between choice on the two tasks. As such, willingness to expend physical effort does appear to be at least partially associated with willingness to expend cognitive effort. However, unlike choices based on physical effort costs, decision making with respect to this form of cognitive effort at baseline is not dopamine dependent. One alternative possibility is that the effort expenditure required in rCET HR versus LR trials may not be large enough to recruit neural circuits involved in differential effort cost calculations. However, rCET accuracy/performance for LR trials is significantly higher than for HR, suggesting that a 1.0s stimulus is much easier to correctly identify than a brief 0.2s stimulus. In addition, some experimental manipulations that affect physical effort decision-making paradigms also affect choice on the rCET, suggesting not only overlap in the neural loci involved but also some conceptual unity across the subtypes of effort-based choice (Hosking et al., 2014). Taken 74  together, the rCET therefore appears to successfully model decision making with differing mental effort costs.  Nevertheless, disparities in the response to drug challenges across cognitive versus physical effort tasks may reflect differences in task design rather than differences in effort costs per se. These differences in trials, blocking, and reward size are not trivial and may in part explain the drift in choice behaviour when animals were switched from rCET to EDT. However, it is unlikely that they can fully explain the differential drug effects. Animals completed three times as many trials for the rCET versus the EDT, suggesting greater total effort expenditure per session; if dopamine antagonism were to affect all forms of effort equally, then one would expect a greater attenuation of rCET HR than EDT HR. In fact, the opposite is observed. Furthermore, the current doses fall near those established in previous decision-making studies (St Onge and Floresco, 2009; Zeeb et al., 2009) and affect other behavioural measures in both tasks. It is thus parsimonious to conclude that dopamine antagonism decreased choice of HR on the EDT and not the rCET because dopamine function is related to decision making with physical, but not cognitive, effort costs.  Considerable attention has been paid to dopaminergic signalling underlying the satisfaction or subversion of expectation, with dopaminergic signals shifting from rewards themselves to their predictive cues as the association is learned (Schultz et al., 1997). Such prediction errors also appear useful in the encoding of subjective value for a given option, thus guiding individuals’ choice preferences during the learning and updating of contingencies (Lak et al., 2014). When such contingencies are well learned and fixed, as is the case for the rCET, dopamine’s error-prediction signalling may be of 75  less utility (Kilpatrick et al., 2000; Wickens et al., 2003; Murray et al., 2012). Of course, dopamine also plays a critical role in the generation, maintenance, and cessation of motor behaviour via its influence on basal ganglia output (Freeze et al., 2013); perturbations of the dopamine system can tremendously and adversely impact individuals’ motor functioning and quality of life (Claassen et al., 2011). While researchers have long separated dopamine’s reward and motor aspects into “ventral” and “dorsal” anatomical components, respectively, it is likely that these reward and motor facets are more functionally and anatomically integrated than previously suggested (Kravitz et al., 2012). Put another way, reward learning and motor learning appear by necessity interconnected. It should perhaps be no surprise, then, that dopamine function is necessary for selecting, initiating and maintaining behaviours with a larger motor component, i.e. higher physical effort, in order to obtain a larger reward (Salamone et al., 2009).  As is the case for the rCET, however, options that vary only by their cognitive effort requirements place equivalent demands on the motor component of dopaminergic signalling. Indeed, researchers have known for some time that mental and physical effort differ in their systemic catecholamine profiles (Fibiger et al., 1984) and in their lasting effects on subsequent task performance (Smit et al., 2005). Critically, at least one study has dissociated regions in humans that process different types of effort costs, with physical effort exertion encoded by motor cortex, and cognitive effort exertion encoded by dorsolateral prefrontal and inferior parietal cortices (Schmidt et al., 2012), targets of future rCET studies. While dopamine likely plays a role in learning the rCET’s contingencies, and thus in guiding goal-directed behaviour during training, its contribution at baseline (when task parameters are extremely familiar and fixed) appears 76  only necessary insofar as to elicit food-seeking behaviour and successfully navigate the operant chamber. Such an interpretation explains why general motor impairments were observed during dopamine antagonism without any concomitant changes to rCET choice.  It would, however, be overextending the current data set to say that willingness to expend any type of cognitive effort is not dopamine dependent at baseline. Other types of cognitive effort (e.g. working memory) may involve the dopamine system, but for willingness to expend the visuospatial attention utilized in the rCET, dopamine does not appear to be involved with choice at baseline. Dopamine modulation is neither necessary nor sufficient for the control of all cognitively effortful processes, yet it does always seem to be involved in all the physically demanding tasks to date. In the rCET’s precursor, the 5CSRTT, neither systemic dopamine antagonism nor dopamine depletions have major effects on accuracy, impairing animals only at very high doses when task completion plummets (Robbins, 2002). Perhaps if the underlying costs of the task were dopamine dependent, then we would observe dopamine dependent effects on choice in a cognitive effort task. However, one might then predict that drug-related changes to choice behaviour may be driven by impairments to task performance rather than the decision-making process per se.  In contrast to the null effects observed here with dopamine antagonists, systemic amphetamine caused workers to “slack off” and slackers to “work harder” on the rCET (Cocker et al., 2012a). Although amphetamine has powerful effects on the DA system, this psychostimulant also potentiates other monoamine neuromodulators, including norepinephrine (Robertson et al., 2009). However, atomoxetine—a selective norepinephrine reuptake inhibitor—did not affect choice behaviour, nor did the α2-77  adrenergic receptor antagonist yohimbine, suggesting the noradrenergic contribution to amphetamine’s effects, and rCET performance in general, is weak.  Unlike a number of studies using the 5-Choice Serial Reaction-Time Task (5CSRTT), no change in premature responding was observed on the rCET following administration of either noradrenergic compound, despite the similarities in trial structure between the two tasks (Navarra et al., 2008; Robinson et al., 2008; Sun et al., 2010). However, the flow of events in the 5CSRTT is rapid and continuous: the response the animal makes to collect reward instantly begins the next trial, encouraging a constant cycle of activity that may facilitate premature responding and loss of stimulus control (Robbins, 2002). In contrast, such behavioural momentum is checked at the start of each rCET trial, as the animal must signal its preference for a hard or easy trial via a lever-press response prior to spinning around and facing the array again. This choice point may act as something of a brake in an otherwise iterative motor sequence, thereby decreasing impulsivity; certainly premature responses are less frequent in the rCET (Robbins, 2002), though this could also arise from more extensive training. Whether this additional step also renders premature responding insensitive to noradrenergic manipulation, by limiting impulsive responses that result from cyclical responding, remains an intriguing possibility that may be worthy of further investigation. In addition to increasing monoamine transmission, amphetamine also potentiates acetylcholine transmission across the cortex (Day et al., 1994), an effect independent of dopaminergic efflux at the basal forebrain (Arnold et al., 2001). Sustained attention increases prefrontal cortical cholinergic tone (Passetti et al., 2000), and strategies to reduce this cognitive effort also reduce prefrontal cortical acetylcholine (Dalley et al., 78  2001). A number of studies suggest that whereas norepinephrine signalling accompanies unexpected uncertainty (where contingencies are changed or reversed without warning to the individual), acetylcholine signalling accompanies expected uncertainty (where probabilities are known by the individual, as in the case of animals’ accuracy on the rCET; Yu and Dayan, 2005; Robbins and Roberts, 2007). Acetylcholine therefore remains a strong candidate for neuromodulatory influence over decision making with cognitive effort costs. Regardless of whether these costs are mental or physical, higher effort demands induce a negative emotional reaction (Morsella et al., 2011) and, all other things being equal, individuals will avoid the option with higher effort (Walton et al., 2002b; Kool et al., 2010). Furthermore, at least one study suggests that these costs share common neuroanatomical nodes, such as the ventral striatum (Schmidt et al., 2012). It seems appropriate, then, to consider both mental and physical to be “effort”. With overlapping-but-distinct neurobiological underpinnings, however, therapeutic interventions may need to be specific in their targeting; drugs that prove efficacious in one effort domain may not be beneficial in another.   79  4.5 Figures   Figure 4.1. Experimental timeline and task schematics.  (A) Timeline for the experiment. After establishing baseline behaviour on the rat Cognitive Effort Task (rCET), half of the cohort remained on the rCET and half were switched to the physical Effort Discounting Task (EDT). (B) Trial structure for the rCET. (C) Trial structure for the EDT. New trials were presented every 40s with illumination of the tray light, followed by the extension of the levers. If animals responded on the LR 80  lever, both levers retracted and the animal immediately received 2 sugar pellets; this cost (i.e. a single lever press, FR1) remained constant for LR trials across the session. If animals responded on the HR lever, the LR lever retracted and animals were given 25s to complete a higher number of presses for 4 sugar pellets. The HR costs increased across the session, beginning with FR2 in the first block, followed by FR5, FR10, and FR20. Animals did not receive reward if they failed to make a lever response (choice omission) or if they failed to complete the required number of lever presses for a HR trial (incomplete HR response), although these occurred less than once per session per animal from the outset. Modified with permission from Floresco et al (2008a).  81   Figure 4.2. Dopamine and norepinephrine pharmacology on the rCET.  (A-F) Neither the dopamine D2-family receptor antagonist eticlopride nor the D2-family receptor antagonist SCH23390 affected animals’ choice, accuracy, or premature responding. (G-I) While the α2-adrenergic receptor antagonist yohimbine did not affect 82  animals’ choice (G) or premature responding (I), it significantly decreased accuracy at the highest dose for all trial types (H; dose: F3,78 = 7.314, p = 0.006; dose x group / choice x dose / choice x dose x group: all Fs < 2.276, NS; saline vs low—dose: F1,26 = 2.948, NS; saline vs med—dose: F1,26 = 3.665, p = 0.067; saline vs high—dose: F1,26 = 13.640, p = 0.001). (J-L) The selective norepinephrine reuptake inhibitor atomoxetine had no effect on animals’ choice (J) and premature responding (L) and virtually no effect on accuracy, with only a trend to decrease workers’ performance on LR trials (K; dose / dose x group / choice x dose: all Fs < 2.172, NS; choice x dose x group: F3,78 = 3.124, p = 0.031; workers only—LR only—dose: F3,57 = 3.141, p = 0.066; workers only—HR only / slackers only—LR / HR—dose: all Fs < 1.252, NS). Data are shown as the mean percent for each variable (± standard error of the mean SEM).   83   Figure 4.3. Baseline behaviour on the EDT versus rCET.  (A) During the first three sessions, all animals demonstrated sensitivity to the physical effort costs: choice of HR decreased across blocks as the costs increased (block: F3,75 = 8.333, p = 0.001; block x group: F3,75 = 0.510, NS). Remarkably, and despite reversing the lever/reward contingencies from the rCET to the EDT, the worker/slacker distinction held during these early sessions of the EDT: animals that had been deemed “workers” for the rCET remained workers for the EDT, and likewise for slackers (group: F1,25 = 6.351, p = 0.018). (B) Baseline choice behaviour on the rCET was linearly correlated with choice behaviour on sessions 1-3 of the EDT (adjusted r2 = 0.358, p = 0.001). (C) However, upon reaching stability at sessions 13-15, the worker/slacker distinction was no longer valid for the EDT (group: F1,25 = 1.273, NS), although animals were still sensitive to the increasing physical effort costs, overall (block: F3,75 = 4.607, p = 0.005). (D) No 84  correlation to baseline behaviour on the rCET was observed for sessions 13-15 (adjusted r2 = 0.039, NS). Data (A, C) are shown as the mean percent (± SEM).   Figure 4.4. Dopamine and norepinephrine pharmacology on the EDT.  (A) Eticlopride dose-dependently decreased all animals’ choice of HR trials across all blocks (dose: F3,63 = 0.038, p = 0.038; dose x group / dose x block / dose x block x group: all Fs < 1.395, NS; saline vs high—dose: F1,21 = 3.900, p = 0.062; saline vs low / saline vs med—dose: all Fs < 0.293, NS). (B) SCH23390 decreased choice of HR at the highest effort block (dose x block: F3,63 = 3.316, p = 0.009; FR20 only—dose: F3,63 = 5.165, p = 0.003; FR2 / FR5 / FR10 only—dose: all Fs < 1.783, NS; dose / dose x group / dose x block x group: all Fs < 1.986, NS). (C) Yohimbine appeared to have some minor effects on choice behaviour, decreasing choice of the HR lever during the first two blocks, but 85  this effect was not robust, as evidenced by the lack of a dose x block effect (dose: F3,60 = 2.506, p = 0.067; dose x group / dose x block / dose x block x group: all Fs < 1.641, NS; FR2 only—dose: F3,60 = 3.570, p = 0.019; FR5 only—dose: F3,60 = 3.150, p = 0.031; FR10 / FR20 only—dose: 1.434, NS). (D) Atomoxetine did not affect choice on the EDT. Data are shown as the mean percent for each variable (± SEM).   Figure 4.5. Reward magnitude manipulations on the rCET.  (A) All animals demonstrated sensitivity to the reward magnitudes, choosing fewer HR trials as the HR option was disincentivized (condition: F3,78 = 9.861, p < 0.001; condition x group: F3,78 = 0.785, NS); at 1 pellet for LR versus 4 pellets for HR (1v4) and baseline conditions (1v2), animals chose HR significantly more often than at 1v1 (1v4 vs 1v1—condition: F1,26 = 19.144, p < 0.001; 1v2 vs 1v1—condition: F1,26 = 12.002, p = 0.002; 1v4 vs 1v2 / 1v4 vs 2v2 / 1v2 vs 2v2—condition: all Fs < 2.862, NS). The worker/slacker distinction remained at all conditions (1v4—group: F1,26 = 32.156, p < 0.001; 1v2—group: F1,26 = 34.590, p < 0.001; 2v2—group: F1,26 = 35.393, p < 0.001; 1v1—group: F1,26 = 27.802, p < 0.001). (B) Animals demonstrated further sensitivity to the reward by 86  increasing the number of completed trials as HR rewards diminished (condition: F3,78 = 12.951 p < 0.001; 1v4 vs 1v2: F1,26 = 8.347, p = 0.008; 1v4 vs 1v1: F1,26 = 22.913, p < 0.001; 1v4 vs 2v2: F1,26 = 0.465, NS), and unsurprisingly, manipulating reward magnitude had a greater impact on the number of completed trials for workers versus slackers (condition x group: F3,78 = 3.332, p = 0.024; workers only—condition: F3,57 = 17.905, p < 0.001; slackers only—condition: F3,21 = 4.797, p = 0.011).  87  Chapter 5: Experiment 3  Nicotine increases impulsivity and decreases willingness to exert cognitive effort despite improving attention in “slacker” rats: insights into cholinergic regulation of cost/benefit decision making  5.1 Introduction  Alterations in central cholinergic function underlie both the aetiology and treatment of a number of illnesses in which decision making is perturbed, including Alzheimer’s disease, attention-deficit/hyperactivity disorder, and schizophrenia (Kuhl et al., 1996; Wilens and Decker, 2007; Williams et al., 2012; Bracco et al., 2014; Mackowick et al., 2014). Interestingly, the most commonly reported cholinergic-driven improvements are within the attentional domain, a cognitive process long associated with central acetylcholine (Klinkenberg et al., 2011). While recent studies have examined cholinergic contributions to decision making under risk and delay (Mendez et al., 2012; Mendez et al., 2013), whether acetylcholine regulates decision making with attentional costs has yet to be investigated. As such, it is unclear whether treatments that have a beneficial effect on attention per se (e.g. nicotine; Muir et al., 1995) would also have a beneficial effect on choices related to those demand costs. Relatedly, cigarette smokers often claim that nicotine enhances their mental focus and performance, but such effects may be limited to specific cognitive domains or relevant only to a subsection of individuals (Harrell and Juliano, 2012).  88   Our group has recently validated a rodent Cognitive Effort Task (rCET), wherein animals can choose to allocate greater visuospatial attention for a greater reward, and this task provides measures of both attentional performance and choice based on attentional demand. Previous work with this task indicates that the neurochemical regulation of willingness to work can be dissociated from ability, and that baseline differences in the degree to which animals choose to apply cognitive effort to earn greater rewards is a key determinant of drug response. The rCET is thus uniquely situated to dissociate acetylcholine’s influence on decision making under attentional costs from acetylcholine’s impact on attentional performance.   The goal of this study was therefore to examine how nicotinic and muscarinic acetylcholine receptor agonists and antagonists affected animals’ choice versus their attentional performance on the rCET, paying special consideration to these drugs’ interactions with animals’ existing choice preferences.  5.2 Additional methods  Pharmacological challenges  Drug doses were based on previous reports (Mendez et al., 2012). Upon stable baseline behaviour, drugs were administered in the following order: the nicotinic acetylcholine receptor (nAChR) agonist nicotine (0, 0.1, 0.3, 1.0mg/kg), the nAChR antagonist mecamylamine (0, 0.5, 1.0, 2.0mg/kg), the muscarinic acetylcholine (mAChR) antagonist scopolamine (0, 0.03, 0.1, 0.3mg/kg), and the mAChR agonist oxotremorine (0, 0.01, 0.03, 0.1mg/kg). Nicotine and mecamylamine were purchased from Sigma-Aldrich 89  Canada (Oakville, ON, Canada), whereas scopolamine and oxotremorine were purchased from Tocris (Minneapolis, MN, USA). All drugs were dissolved in 0.9% sterile saline and administered in a volume of 1ml/kg via intraperitoneal injection.  All drugs were prepared fresh daily, and administration adhered to a digram-balanced Latin Square design (for doses A-D: ABCD, BDAC, CABD, DCBA (p. 329, Cardinal and Aitken, 2006)). The three-day injection schedule started with a baseline session, followed by a drug or saline injection session, and then by a non-testing day. Injections for nicotine and mecamylamine were administered 10min before behavioural testing; scopolamine injections were administered immediately before testing; and oxotremorine injections were administered 15min before testing. Animals were given a minimum of one week drug-free testing between compounds to minimize any carryover effects.  5.3 Results  Nicotine administration Choice behaviour, accuracy, and premature responses. Baseline behaviour has been discussed at length elsewhere, and as such will only be briefly addressed here. As demonstrated previously, animals chose high-effort/high-reward (HR) trials more than low-effort/low-reward (LR) trials (saline only—choice: F1,22 = 71.338, p < 0.001), and workers continued to choose a significantly higher proportion of HR than slackers (saline only—group: F1,22 = 28.445, p < 0.001). The nicotinic acetylcholine receptor (nAChR) agonist nicotine differentially affected choice of HR for workers and slackers (Figure 5.1a; dose: F3,66 = 0.377, NS; dose x group: F3.66 = 3.446, p = 0.022), further decreasing 90  choice of HR for slackers but having no effect on workers (slackers only—dose: F3,36 = 4.300, p = 0.011; —saline vs 1.0mg/kg—dose: F1,12 = 5.376, p = 0.039; —saline vs 0.1mg/kg / —saline vs 0.3mg/kg / workers only: all Fs < 1.285, NS).  As expected, animals displayed higher accuracy on LR versus HR trials (saline only—choice: F1,22 = 62.446, p < 0.001), indicating that HR trials were indeed more cognitively demanding. As seen in previous cohorts, workers and slackers performed the task equally well (saline only—choice x group / group: all Fs < 0.499, NS), despite workers choosing HR proportionately more, and thus suggesting that differences in choice preference were not a direct result of differences in animals’ visuospatial attentional ability. Despite decreasing choice of HR for slackers, nicotine increased accuracy of HR for slackers, but had no effect on workers’ HR, or LR for all animals (Figure 5.1b; choice: F1,22 = 142.371, p < 0.001; dose / dose x group / choice x dose / choice x dose x group: all Fs < 0.841, NS; HR only—dose: F3,66 = 1.739, NS; —dose x group: F3.66 = 2.853, p = 0.044; —slackers only—dose: F3,36 = 3.208, p = 0.034; —saline vs 1.0mg/kg: F1,12 = 8.388, p = 0.013; —saline vs 0.1mg/kg / —saline vs 0.3mg/kg: all Fs < 1.007, NS; HR—workers only / LR: all Fs < 1.064, NS).  As seen in previous cohorts, premature responding was higher for HR versus LR trials (saline only—choice: F1,22 = 7.384, p = 0.013). There were no differences in the level of premature responding between workers and slackers (saline only—choice x group / group: all Fs < 0.377, NS), indicating that choice preference was not guided by motor impulsivity. Nicotine increased premature responding for all animals across both trial types (Figure 5.1c; dose: F3,66 = 5.287, p = 0.003; dose x group / choice x dose / choice x dose x group: all Fs < 0.894, NS). 91   Other behavioural measures. Nicotine had no effect on the latency to choose a lever, nosepoke at the array or collect reward (dose / dose x group / choice x dose / choice x dose x group: all Fs < 2.297, NS). As consistently seen with the rCET, both workers and slackers collected reward faster following a successful HR versus LR trial (choice: F1,22 = 4.393, p = 0.048; choice x group / group: all Fs < 1.503, NS), suggesting that all animals anticipated a larger reward following the successful completion of HR, but slackers still chose proportionately fewer of these trials than workers. Nicotine dose-dependently increased choice omissions (dose: F3,66 = 14.429, p < 0.001) but decreased response omissions (dose: F3,66 = 6.912, p < 0.001) for all animals across both trial types (dose x group / choice x dose / choice x dose x group: all Fs < 2.146, NS), and decreased the number of completed trials for all animals by ~20% (dose: F3,66 = 20.042, p < 0.001; dose x group: F3,66 = 0.380, NS).  Mecamylamine administration Choice behaviour, accuracy, and premature responses. The nAChR antagonist mecamylamine did not affect animals’ choice on the rCET (Figure 5.1d; dose / dose x group: all Fs < 1.562, NS). Mecamylamine caused a modest impairment to all animals’ accuracy on LR trials at the intermediate dose (Figure 5.1e; dose: F3,66 = 2.722, p = 0.051; dose x choice: F3,66 = 3.783, p = 0.014; LR only—dose: F3,66 = 3.896, p = 0.013; —saline vs 1.0mg/kg—dose: F1,22 = 9.160, p = 0.006; —saline vs 0.5mg/kg / —saline vs 2.0mg/kg / HR only / dose x group / choice x dose x group: all Fs < 3.514, NS). The drug had no effect on premature responding (Figure 5.1f; dose / dose x group / choice x dose / choice x dose x group: all Fs < 0.646, NS). 92   Other behavioural measures. For all animals across both trial types, mecamylamine lengthened the latency to choose either the LR or HR lever (dose: F3,66 = 5.406, p = 0.009; dose x group / choice x dose / choice x dose x group: all Fs < 1.637, NS) but did not affect correct or collection latencies (dose / dose x group / choice x dose / choice x dose x group: all Fs < 1.043, NS). The drug did not affect response omissions (dose / dose x group / choice x dose / choice x dose x group: all Fs < 0.939, NS) but increased the number of choice (lever) omissions (dose: F3,66 = 9.172, p < 0.001; dose x group: F3,66 = 1.588, NS) and decreased the number of completed trials for all animals by ~10% (dose: F3,66 = 8.716, p = 0.001; dose x group: F3,66 = 0.682, NS).  Scopolamine administration Choice behaviour, accuracy, and premature responses. The muscarinic acetylcholine receptor (mAChR) antagonist scopolamine decreased all animals’ choice of HR (Figure 5.2a; dose: F3,66 = 4.052, p = 0.011; dose x group: F3,66 = 1.393, NS; group: F1,22 = 27.043, p < 0.001). When examined separately, scopolamine decreased workers’ HR choice (dose: F3,30 = 4.927, p = 0.007; saline vs 0.3mg/kg—dose: F1,10 = 11.971, p = 0.006; saline vs 0.03mg/kg / saline vs 0.1mg/kg: all Fs < 2.871, NS) but had no effect on slackers’ choice (dose: F3,36 = 0.526, NS). The drug had no effect on animals’ accuracy or premature responding (Figure 5.2b-c; dose / dose x group / choice x dose / choice x dose x group / LR only / HR only: all Fs < 2.417, NS).  Other behavioural measures. Scopolamine had an inverted-U-shaped effect on the time taken to choose between LR and HR levers/options, with the lowest dose lengthening choice latency (dose: F3,66 = 4.843, p = 0.004; dose x group / choice x dose / 93  choice x dose x group: all Fs < 1.437, NS; saline vs 0.03mg/kg—dose: F1,22 = 7.051, p = 0.014; 0.03mg/kg vs 0.3mg/kg—dose: F1,22 = 10.727, p = 0.003; 0.03mg/kg vs 0.1mg/kg—dose: F1,22 = 3.826, p = 0.063; saline vs 0.1mg/kg / saline vs 0.3mg/kg: all Fs < 2.961, NS). Scopolamine also significantly lengthened all animals’ correct latency (dose: F3,66 = 7.255, p = 0.004; choice x dose: F3,66 = 3.097, p = 0.066; LR only—dose: F3,66 = 9.153, p = 0.002; HR only / dose x group / choice x dose x group: all Fs < 1.469, NS) but had no effect on collection latency (dose / dose x group / choice x dose / choice x dose x group: all Fs < 1.166, NS). The drug significantly increased both response omissions (dose: F3,66 = 47.154, p < 0.001; dose x group: F3,66 = 2.805, p = 0.046; slackers only / workers only—dose: all Fs > 13.879, p < 0.001; choice x dose / choice x dose x group: all Fs < 0.938, NS) and choice omissions (dose: F3,66 = 26.830, p < 0.001; dose x group: F3,66 = 1.353, NS) and profoundly decreased all animals’ completed trials by over 65% (dose: F3,66 = 121.079, p < 0.001; dose x group: F3,66 = 0.467, NS).  Oxotremorine administration Choice behaviour, accuracy, and premature responses. The mAChR agonist oxotremorine had no effect on animals’ choice or accuracy (Figure 5.2d-e; dose / dose x group / choice x dose / choice x dose x group / LR only / HR only: all Fs < 2.122, NS). Oxotremorine did, however, decrease premature responding for all animals across both trial types (Figure 5.2f; dose: F3,66 = 3.045, p = 0.035; saline vs 0.1mg/kg—dose: F1,22 = 6.214, p = 0.021; saline vs 0.01mg/kg / saline vs 0.03mg/kg / dose x group / choice x dose / choice x dose x group: all Fs < 1.052, NS). 94   Other behavioural measures. Oxotremorine lengthened choice latency for all animals across both trial types (dose: F3,66 = 7.408, p = 0.002; dose x group / choice x dose / choice x dose x group: all Fs < 1.900, NS) but had no effect on correct or collection latencies (dose / dose x group / choice x dose / choice x dose x group: all Fs < 1.925, NS). The drug increased both response omissions and choice omissions (dose: all Fs > 9.026, p < 0.001; dose x group / choice x dose / choice x dose x group: all Fs < 2.002, NS), and also decreased completed trials by ~40% (dose: F3,66 = 36.876, p < 0.001; dose x group: F3,66 = 0.260, NS).  5.4 Discussion  Here we show for the first time the influence of cholinergic functioning on decision making with attentional effort costs. The nAChR agonist nicotine decreased choice of high-effort/high-reward (HR) trials for “slacker” rats, despite a modest improvement in these animals’ performance (i.e. accuracy), whereas the drug did not affect workers’ choice. In contrast to its differential choice effects for workers versus slackers, nicotine increased motor impulsivity (i.e. premature responding) for all animals. Interestingly, the mAChR antagonist scopolamine also decreased choice of HR, particularly for workers, without any concomitant effects on performance or motor impulsivity. Finally, the mAChR agonist oxotremorine had no effect on choice but dose-dependently decreased impulsive responding. Taken together, these data support recent findings that nicotinic and muscarinic cholinergic systems subserve cost/benefit decision making (Mendez et al., 2012; Mendez et al., 2013), and further demonstrate that acetylcholine’s influence on 95  choice can be dissociated from its effects on attentional performance and motor impulsivity.  Central acetylcholine largely originates from the basal forebrain and pons, and projects to a diffuse set of targets in the central nervous system, including the prefrontal cortex, limbic regions, and the midbrain dopaminergic system (Dani and Bertrand, 2007); a small population of cholinergic interneurons is also located in the striatum and projects locally, and thus acetylcholine exerts modulatory control over both dopamine’s midbrain source and its striatal targets (Rice and Cragg, 2004; Zhang and Sulzer, 2004). Broadly speaking, then, central cholinergic systems are excellently placed to both directly and indirectly contribute to the previously established “cortico-limbic-striatal” circuitry that underlies cost/benefit decision making (Floresco et al., 2008c).  Preliminary pharmacological studies of cholinergic contributions to decision making used delay- and risk-discounting tasks, wherein the costs of the HR option were adjusted across blocks within each session (Mendez et al., 2012). On the risk-discounting task, nicotine increased choice of HR when costs ascended across blocks, whereas it decreased choice of HR when costs descended across blocks, indicating that the drug impaired animals’ behavioural flexibility. Scopolamine robustly decreased choice of HR on both tasks. Only null effects on decision making have been reported for mecamylamine and oxotremorine (for a review, see Fobbs and Mizumori, 2014), which parallels the current data and suggests that these drugs may not be ideal for systemic manipulations of cost/benefit decision-making tasks. The variety of nonspecific motor effects for each drug, however, indicated that doses were within a physiologically relevant range. 96   In addition to its putative influence on decision making, acetylcholine’s role in attentional processes has also been well described (for a review, see Klinkenberg et al., 2011). For example, basal forebrain outputs to the sensory cortex increase the salience of objects by enhancing the reliability of sensory coding (Goard and Dan, 2009), while cholinergic contributions to the parietal and frontal lobes mediate shifting attention (Bucci et al., 1998) and sustained attention (Passetti et al., 2000; Dalley et al., 2001), respectively. Human studies of attention and acetylcholine generally correspond with this animal research (Bentley et al., 2003; Hahn et al., 2009). Taken together, acetylcholine appears intrinsically linked to the construct of attention and its various subcomponents, including salience, shift, and sustained effort.  As such, parsing acetylcholine’s contributions to both attention and decision making is essential to interpreting any manipulations of the cholinergic system. A substantial number of previous nicotine studies utilized the rodent Five-Choice Serial Reaction-Time Task (5CSRTT), the precursor to the rCET, which differs from the current task only in its lack of LR/HR options (thus having only a single stimulus duration and reward rate; Robbins, 2002). In these 5CSRTT studies, systemic nicotine’s effect on animals’ accuracy was subtle, typically only benefitting performance under sub-optimal conditions such as when the basal forebrain was lesioned (Muir et al., 1995), when task difficulty was increased (Mirza and Stolerman, 1998), or when using an inbred rat strain (versus the outbred strain of the current study; Mirza and Bright, 2001). In addition to these minimal effects on accuracy, nicotine has also been reported to increase impulsive responding (Mirza and Stolerman, 1998; Hahn et al., 2002; Stolerman et al., 2009). 97  Taken together, these data imply that central cholinergic functioning already resides near an optimal level for attentional performance and inhibitory control.  In the current study, nicotine increased accuracy only for slackers on HR trials, and prima facie this may suggest that slackers suffer some performance impairment versus their worker counterparts. However, as discussed in detail elsewhere (Cocker et al., 2012a; Hosking et al., 2014), workers’ and slackers’ accuracy is equivalent at baseline, all animals demonstrate sensitivity to the task’s contingencies, and thus slackers’ choice of fewer HR trials is not simply dependent upon weaker performance or a failure to acquire the task. Furthermore, if nicotinic agonism was solely influencing attention on the task (and not decision making), then any benefits to HR performance should have been accompanied by increased choice of HR; instead, nicotine decreased HR choice while simultaneously increasing HR accuracy for slackers, suggesting that its effects on choice were separate from those on attention. Similarly, scopolamine decreased workers’ choice of HR but had no significant effect on accuracy. This lack of effect on accuracy stands in contrast to some 5CSRTT literature (Mirza and Stolerman, 2000; Ruotsalainen et al., 2000), and may be the result of additional training for the rCET animals and differences in dosing methodology (Jones and Higgins, 1995; Shannon and Eberle, 2006). Altogether, it appears that acetylcholine manipulations affect multiple subsystems, including those that underlie decision making, attention, and impulsivity.  Nicotine’s apparent lack of effect on workers’ choice is most readily interpreted by considering pharmacological results as a function of individual differences. Interactions between animals’ choice preferences and experimental manipulations have been previously reported for this task and cannot be explained by regression to the mean 98  or indifference to the task’s choices (Experiments 1 and 4). As discussed with amphetamine’s effects (Cocker et al., 2012a, supplementary data), the current data suggest an inverted-U function of basal cholinergic tone versus choice of HR trials, upon which agonism would cause a rightward shift and antagonism a leftward shift; contrary to the monoamine systems, these data predict that slackers sit to the right of the apex on such a curve, hence a stronger choice effect for cholinergic agonism, while workers sit to the left of the curve, hence a stronger choice effect for cholinergic antagonism. A similar hypothesis was recently put forward by Mendez et al. (2012), and directly testing such hypotheses of basal cholinergic and catecholamine functioning versus choice preference will require future in vivo behavioural recordings, such as via microdialysis or microelectrode array (Bruno et al., 2006; Fadel, 2011).  In light of the current and previous data, some tentative, testable models of acetylcholine’s specific contribution to decision making can be made; these models are not mutually exclusive and may in fact complement one another. First, acetylcholine may indirectly influence choice via its interactions with the midbrain dopaminergic system (Rice and Cragg, 2004; Zhang and Sulzer, 2004). Some support for this theory can be observed in the general, but not absolute, congruency of effects for dopamine versus acetylcholine pharmacology on discounting tasks (Fobbs and Mizumori, 2014): dopaminergic and cholinergic agonists tend to have the same effect on choice, and antagonists for each neuromodulator also tend to affect choice similarly. However, cholinergic contributions to decision making are not exclusively driven by dopaminergic interactions, as dopamine antagonists have no measurable effect on choice in the rCET (Experiment 2). Also, amphetamine (which potentiates dopaminergic functioning) has the 99  opposite choice effects to nicotine, instead causing workers to “slack off” and slackers to “work harder” (Experiment 1). Second, acetylcholine may in part underlie animals’ ability to select and/or update their choice behaviour; cholinergic agonism would thus render animals behaviourally inflexible, whereas antagonism would lead to behavioural indifference. This is supported both by previous results (Mendez et al., 2012) and the current data: nicotine arguably exacerbated animals’ existing choice preferences and decreased sampling of animals’ less preferred option, whereas scopolamine drove all animals toward equivalent choice of LR versus HR and more greatly affected workers, whose preference was further from indifference. Third, acetylcholine may influence decision making via attentional processes, such as increasing the salience of the task’s objective and subjective properties. Such an interpretation could equally explain nicotine’s exacerbation of existing preferences on the rCET, when salience is increased, and scopolamine’s drive to indifference, when salience is decreased. Fourth, as cortical ACh efflux is known to track the amount of attentional effort exerted rather than attentional performance per se (Passetti et al., 2000; Dalley et al., 2001), nicotine may have artificially inflated the sense of total effort expended in a rCET session, independent of its actual effects on attentional performance. This theory would suggest that animals more sensitive to the attentional effort exertion (i.e. slackers) would be more strongly affected by the drug, and indeed this is supported by the current data. Conversely, scopolamine could have increased the sense of effort expenditure to a greater degree in workers rather than slackers, thereby leading to the observed decrease in effortful choice predominantly in this harder-working group. Further disentangling these putative contributions of acetylcholine to decision making, for example by elucidating cortical 100  versus striatal cholinergic influence on choice at baseline and in response to drug challenge, will be a focus of future research utilizing the rCET.  In sum, it appears that both nicotinic and muscarinic cholinergic systems contribute to cost/benefit decision making, and in part their contributions can be understood as a function of individual differences. While nicotine has been considered as a cognitive enhancer by both smokers and researchers (Thiel et al., 2005; Heishman et al., 2010; Harrell and Juliano, 2012), these data suggest that its modest benefits to attention may be coupled with impulsiveness and decreased willingness to work hard, especially in individuals who are particularly sensitive to effort costs (i.e. slackers). Nicotine may therefore produce a subjective feeling of increased output or task engagement, while actually producing a decrease in application. Novel therapeutic interventions may therefore be best understood by simultaneously studying multiple cognitive constructs such as decision making, attention, and impulsivity.   101  5.5 Figures   Figure 5.1. Nicotinic drug challenges during the rCET.  (A) The nicotinic acetylcholine receptor (nAChR) agonist nicotine differentially affected choice of HR for workers and slackers (dose: F3,66 = 0.377, NS; dose x group: F3.66 = 3.446, p = 0.022), further decreasing choice of HR for slackers but having no effect on workers (slackers only—dose: F3,36 = 4.300, p = 0.011; —saline vs 1.0mg/kg—dose: F1,12 = 5.376, p = 0.039; —saline vs 0.1mg/kg / —saline vs 0.3mg/kg / workers only: all Fs < 1.285, NS). (B) nicotine increased accuracy of HR for slackers, but had no effect on workers’ HR, or LR for all animals (choice: F1,22 = 142.371, p < 0.001; dose / dose x group / choice x dose / choice x dose x group: all Fs < 0.841, NS; HR only—dose: F3,66 = 1.739, NS; —dose x group: F3.66 = 2.853, p = 0.044; —slackers only—dose: F3,36 = 3.208, p = 0.034; —saline vs 1.0mg/kg: F1,12 = 8.388, p = 0.013; —saline vs 0.1mg/kg / 102  —saline vs 0.3mg/kg: all Fs < 1.007, NS; HR—workers only / LR: all Fs < 1.064, NS). (C) Nicotine increased premature responding for all animals across both trial types (dose: F3,66 = 5.287, p = 0.003; dose x group / choice x dose / choice x dose x group: all Fs < 0.894, NS). (D) The nAChR antagonist mecamylamine did not affect animals’ choice on the rCET (dose / dose x group: all Fs < 1.562, NS). (E) Mecamylamine caused a modest impairment to all animals’ accuracy on LR trials at the intermediate dose (dose: F3,66 = 2.722, p = 0.051; dose x choice: F3,66 = 3.783, p = 0.014; LR only—dose: F3,66 = 3.896, p = 0.013; —saline vs 1.0mg/kg—dose: F1,22 = 9.160, p = 0.006; —saline vs 0.5mg/kg / —saline vs 2.0mg/kg / HR only / dose x group / choice x dose x group: all Fs < 3.514, NS). (F) Mecamylamine had no effect on premature responding (dose / dose x group / choice x dose / choice x dose x group: all Fs < 0.646, NS).   103   Figure 5.2. Muscarinic drug challenges during the rCET.  (A) The muscarinic acetylcholine receptor (mAChR) antagonist scopolamine decreased all animals’ choice of HR (dose: F3,66 = 4.052, p = 0.011; dose x group: F3,66 = 1.393, NS; group: F1,22 = 27.043, p < 0.001). When examined separately, scopolamine decreased workers’ HR choice (dose: F3,30 = 4.927, p = 0.007; saline vs 0.3mg/kg—dose: F1,10 = 11.971, p = 0.006; saline vs 0.03mg/kg / saline vs 0.1mg/kg: all Fs < 2.871, NS) but had no effect on slackers’ choice (dose: F3,36 = 0.526, NS). (B, C) The drug had no effect on animals’ accuracy or premature responding (dose / dose x group / choice x dose / choice x dose x group / LR only / HR only: all Fs < 2.417, NS). (D, E) The mAChR agonist oxotremorine had no effect on animals’ choice or accuracy (dose / dose x group / choice x dose / choice x dose x group / LR only / HR only: all Fs < 2.122, NS). (F) Oxotremorine did, however, decrease premature responding for all animals across both trial types (dose: F3,66 = 3.045, p = 0.035; saline vs 0.1mg/kg—dose: F1,22 = 6.214, p = 104  0.021; saline vs 0.01mg/kg / saline vs 0.03mg/kg / dose x group / choice x dose / choice x dose x group: all Fs < 1.052, NS).   105  Chapter 6: Experiment 4  Dissociable contributions of anterior cingulate cortex and basolateral amygdala on a rodent cost/benefit decision-making task of cognitive effort  6.1 Introduction  Attaining many of the goals we seek requires the decision to invest valuable resources such as time and effort. Diminished motivation for such rewards can negatively impact an individual’s quality of life, reaching clinical significance in a number of conditions including schizophrenia, depression, and ADHD (Egeland et al., 2010; Hammar et al., 2011; Gold et al., 2013b).  The amygdala and anterior cingulate cortex (ACC) are two critical brain regions in decision making. Patients with damage to either region demonstrate impairments on laboratory models of “real-world” decision making where levels of reward and cost are varied across options (Bechara et al., 1999a; Manes et al., 2002; Brand et al., 2007; van Honk et al., 2013). Similarly, functional neuroimaging and intracranial electrophysiology suggest that the amygdala and ACC are involved in evaluating costs and rewards to guide subsequent behaviour (Williams et al., 2004; Smith et al., 2009; Basten et al., 2010; Jenison et al., 2011), and internally generated changes in choice correlate with amygdala and ACC activity (Walton et al., 2004; Sokol-Hessner et al., 2013).  The importance of these regions has also been evaluated in animal models of effort-based decision making, wherein animals can choose to obtain larger rewards by making more responses on a lever or climbing a barrier (Salamone et al., 1994; Floresco 106  et al., 2008a). Excitotoxic lesions and pharmacological inactivation of either the basolateral nucleus of the amygdala (BLA) or ACC shifts rats’ behaviour away from high-effort, high-reward (HR) options (Rudebeck et al., 2006b; Ghods-Sharifi et al., 2009), as does contralateral disconnection of these regions (Floresco and Ghods-Sharifi, 2007a). Thus, the BLA and ACC are necessary to overcome aversive work requirements and select the maximally rewarding option, although their unique contributions remain unclear.  However, decision costs are not unitary in their underlying circuitry (Floresco et al., 2008c). Whereas animal models vary physical work requirements across options, effort costs in industrialized society are overwhelmingly cognitive in nature; literature on human effort reflects this (Naccache et al., 2005; Croxson et al., 2009; Kool et al., 2010). Broadly speaking, cognitive or mental effort costs are those that are non-physical in nature and tax limited neurobiological resources, as observed through the psychological constructs of working memory, attention, response inhibition, etc. (Schmidt et al., 2012). Regions deemed inessential in physical effort (e.g. the prefrontal cortex; Walton et al., 2003a) have been shown to play a prominent role in human cognitive effort (McGuire and Botvinick, 2010; Schmidt et al., 2012). Furthermore, these human studies have emphasized how individual differences in brain function influence individual differences in effort expenditure (Treadway et al., 2012b), an examination all but absent from animal effort literature (but see Randall et al., 2012).  The goal of the current study was therefore to use the rCET to determine the importance of the BLA and ACC in the societally relevant construct of cognitive effort, whether the impact of silencing these regions depends on individual differences in 107  willingness to work, and thus the unique contributions of these regions to effortful decision making.  6.2 Additional methods  Surgery Surgery and microinfusion procedures were modeled after Floresco and Ghods-Sharifi (2007a) and Ghods-Sharifi et al. (2009). When baseline performance was deemed statistically stable (30-35 sessions), animals were implanted with 22-gauge stainless steel guide cannulae (Plastics One; Roanoke, Virginia, USA) bilaterally into the BLA or ACC using standard stereotaxic techniques. Animals in the first cohort were anaesthetized with 100 mg/kg ketamine and 10 mg/kg xylazine and implanted with BLA cannulae (incisor bar set to ~-3.3 [flat skull]: anteroposterior [AP] = -3.1 mm, mediolateral [ML] = ± 5.2 mm from bregma; dorsoventral [DV] = -6.5 mm from dura). Those in the second cohort were anaesthetized with 2% isoflurane in O2 and implanted with ACC cannulae (flat skull: AP = +2.0 mm, ML = ±0.7 mm from bregma; DV = -1.2 mm from dura)(Paxinos and Watson, 1998). All animals were provided with pre- and post-operative analgesia to minimize pain or discomfort, via 5mg/kg Anafen. Guide cannulae were fixed to the skull via three stainless steel screws and dental acrylic, and 29-gauge obdurators with dust caps were inserted and extended flush with the end of the cannulae. Animals were given at least one week of recovery in their home cages before subsequent testing. Twelve animals were excluded due to poor recovery.   108  Microinfusion Following recovery, animals performed ten free-choice sessions, after which all individuals displayed stable behaviour. Animals were then habituated to the microinfusion process with two mock infusions, wherein the 30-gauge injectors with tips extending 1 mm beyond the guide cannulae were inserted for two minutes but no infusion was performed, followed by a testing session. Infusions followed a 3-day cycle starting with a baseline session, followed by a drug or saline injection session, and then by a non-testing day; a single 30-minute testing session was performed for each saline and inactivation condition. The BLA and ACC were inactivated by a mixture of the GABAB agonist baclofen and the GABAA agonist muscimol (Sigma-Aldrich; Oakville, Ontario, Canada), prepared separately at 0.5 µg/µL in saline and mixed together in equal volumes to form a 0.25 µg/µL solution. 0.5 µL injections of saline or baclofen-muscimol (i.e. 0.125 µg of drug) were administered bilaterally at a rate of 0.4 µL/minute, and injectors were left in place for an additional minute to allow diffusion. Injectors were then removed, obdurators were replaced, and animals were returned to their home cages for 10min before being placed in the operant chambers and performing the rCET. On the first infusion day, half of the rats received saline infusions while the other half received baclofen-muscimol; these administrations were reversed on the second infusion day, allowing for a within-subjects comparison.  Histology Following completion of all behavioural testing, animals were killed by carbon dioxide exposure. Brains were removed and fixed in 4% formaldehyde for at least 24 hours, transferred to a 30% sucrose solution, and then frozen and cut via cryostat into 40 µm 109  coronal sections. These sections were stained with Cresyl violet for visualization, and the projected locations of the injector tips protruding from the guide cannulae were mapped onto standard sections from Paxinos and Watson (1998).  6.3 Results  Cannula placements The locations of all acceptable placements, as well as representative samples of BLA and ACC cannulation, are depicted in Figure 6.1. Two animals from the BLA group and two animals from the ACC group were excluded because of inaccurate placements in one hemisphere, leaving a total of 40 animals for analysis (BLA: n = 14; ACC: n = 26).  Basolateral amygdala (BLA) inactivation Choice behaviour, accuracy, and premature responses. As per baseline conditions and previous cohorts (Cocker et al., 2012a), animals chose high-effort/high-reward (HR) trials more than low-effort/low-reward trials (choice: F1,12 = 11.144, p = 0.006) when the BLA was infused with saline. However, there was substantial individual variation across the group and the previously established worker/slacker distinction held; workers in fact chose significantly more HR trials than slackers (group: F1,12 = 34.549, p < 0.001). Inactivation of the BLA had no effect on animals’ choice of LR or HR trials when considered as a homogenous group (inactivation: F1,12 < 0.001, NS). However, BLA inactivation differentially affected workers and slackers, increasing choice of HR trials in slackers but decreasing this behaviour in workers (Figure 6.2a; inactivation x group: F1,12 = 5.445, p = 0.038). Choice effects of BLA inactivation were present from the first 110  quartile of trials, with opposing effects on workers versus slackers predominantly in Q1 and Q4 (inactivation: F1,12 = 0.128, NS; inactivation x group: F1,12 = 4.617, p = 0.053; inactivation x quartile: F3,12 = 3.486, p = 0.026; inactivation x group x quartile: F3,12 = 6.848, p = 0.001; Q1 only -inactivation x group: F1,12 = 7.768, p = 0.016; Q4 only -inactivation x group: F1,12 = 11.939, p = 0.005; Q2 / Q3 -inactivation x group: all Fs < 0.687, NS).  As expected, animals were more accurate on LR versus HR trials, as shown by a main effect of choice on repeated-measures ANOVA (choice: F1,12 = 29.975, p < 0.001). Despite its effects on choice, BLA inactivation did not affect accuracy or premature responding (Figure 6.2b-c; inactivation / choice x inactivation: all Fs < 1.154, NS). Identical to previous cohorts, there were no differences in accuracy or premature responding between workers and slackers, indicating that choice preferences were not primarily due to individuals’ ability to perform the task (group / inactivation x group: all Fs < 1.012, NS).  Other behavioural measures. As per physical effort literature (Ghods-Sharifi et al., 2009), inactivation of the BLA increased the amount of time for animals to choose between LR and HR levers (Figure 6.2d; inactivation: F1,12 = 17.310, p = 0.001; inactivation x choice: F1,12 = 2.210, NS). This effect was the same for workers and slackers (group / inactivation x group: all Fs < 0.322, NS), and animals took equally long to choose between LR and HR options (choice / choice x group: all Fs < 1.331, NS). BLA inactivation also increased the latency to make a correct nosepoke response across all trial types (inactivation: F1,12 = 6.261, p = 0.028; inactivation x choice: F1,12 = 0.282, NS) and correct responses were equally fast for LR and HR trials (choice: F1,12 = 0.017, 111  NS). As per previous results (Cocker et al., 2012a), animals collected rewards faster following HR trials than for LR trials (choice: F1,12 = 73.120, p < 0.001), and subsequent ANOVAs showed that slackers collected on HR trials faster than workers (group: F1,12 = 5.667, p = 0.035; LR only -group: F1,12 = 1.769, NS; HR only -group: F1,12 = 9.369, p = 0.010), suggesting that slackers are not simply indifferent to reward magnitude. BLA inactivation had no effect on collection latency (inactivation / inactivation x choice: all Fs < 0.689, NS). Animals failed to respond by nosepoke more often for HR versus LR trials (choice: F1,12 = 11.029, p = 0.006) but BLA inactivation had no effect on these response omissions (inactivation / inactivation x choice: all Fs < 1.460, NS), and there were no differences between workers and slackers revealed by repeated-measures ANOVA (group / inactivation x group / choice x group: all Fs < 2.528, NS). For all animals, inactivation of the BLA increased the number of lever (choice) omissions (inactivation: F1,12 = 16.740, p = 0.001; inactivation x group: F1,12 = 0.287, NS; group: F1,12 = 0.638, NS) and decreased the number of trials completed (inactivation: F1,12 = 22.003, p = 0.001; inactivation x group: 0.004, NS). Slackers completed more trials than workers when infused with saline but not baclofen-muscimol (group: F1,12 = 5.026, p = 0.045; saline only -group: F1,12 = 11.374, p = 0.006; drug only -group: F1,12 = 1.287, NS).  Anterior cingulate cortex (ACC) inactivation Choice behaviour, accuracy, and premature responses. Unlike the differential effects of BLA inactivation for workers versus slackers, infusion of baclofen-muscimol into the ACC decreased all animals’ preference for the HR option (Figure 6.3a; inactivation: F1,24 = 6.178, p = 0.020; inactivation x group: F1,24 = 1.572, NS). ACC inactivation had a stronger effect on slackers when considered across quartiles, but inactivation effects were 112  present for slackers throughout all quartiles of the session, as demonstrated by lack of inactivation x quartile effect on repeated-measures ANOVA (inactivation: F1,24 = 2.710, NS; inactivation x group: F1,24 = 4.996, p = 0.035; quartile / quartile x group / inactivation x quartile / inactivation x quartile x group: all Fs < 2.710, NS; workers only –inactivation / quartile / inactivation x quartile: all Fs < 0.728, NS; slackers -inactivation: F1,15 = 7.345, p = 0.016; -quartile / inactivation x quartile: all Fs < 1.276, NS).  In contrast, ACC inactivation had virtually no effect on accuracy, with a significant Inactivation by Choice interaction but not significantly increasing or decreasing LR or HR when considered alone (Figure 6.3b; inactivation: F1,24 = 0.023, NS; inactivation x choice: F1,24 = 4.671, p = 0.041; inactivation x choice x group = 3.918, p = 0.059; LR, workers only -inactivation: F1,9 = 4.977, p = 0.053; LR / HR / HR workers / LR slackers / HR slackers -inactivation: all Fs < 2.962, NS). As per 5CSRTT literature (Muir et al., 1996), ACC inactivation increased premature responding for both trial types (Figure 6.3c; inactivation: F1,24 = 9.718, p = 0.005; inactivation x choice / inactivation x choice x group: all Fs < 1.369, NS). There were no group differences between workers and slackers for accuracy and premature responding (group: all Fs < 1.891, NS).  Other behavioural measures. Inactivation of the ACC had no effect on the latency to choose between LR and HR levers (Figure 6.3d; inactivation / inactivation x choice: all Fs < 1.938, NS), but increased the time needed to make a correct nosepoke response for both trial types (inactivation: F1,24 = 35.602, p < 0.001; inactivation x choice: F1,24 = 0.033, NS). ACC inactivation did not affect the latency to collect reward following a correct response (inactivation / inactivation x choice: all Fs < 1.343, NS) but increased response omissions for all animals (inactivation: F1,24 = 37.344, p < 0.001; inactivation x 113  choice: F1,24 = 3.711, p = 0.066; LR only -inactivation: F1,24 = 22.433, p < 0.001; HR only -inactivation: F1,24 = 34.468, p < 0.001). Inactivation of the ACC also increased the number of lever (choice) omissions (inactivation: F1,24 = 5.873, p = 0.023) and decreased the number of completed trials (inactivation: F1,24 = 31.955, p < 0.001) for all animals. There were no significant differences between workers and slackers on all of the above measures (group / inactivation x group / inactivation x choice x group: all Fs < 2.811, NS).  6.4 Discussion  Here we show for the first time that both the BLA and the ACC subserve decision making with cognitive effort costs in rats, as inactivation of either of these regions altered animals’ willingness to expend mental effort. Interestingly, a clear dissociation between inactivations of the BLA and ACC was observed. Without intact functioning of the BLA, workers “slacked off” and slackers “worked harder”, whereas loss of ACC function caused all animals to reduce their choice of high-effort trials. These results are in contrast to physical effort decision-making tasks, where loss of either BLA or ACC decreased choice of high-effort options in rats (Walton et al., 2003a; Floresco and Ghods-Sharifi, 2007b). The dissociation of these two regions in the present study extended beyond choice: BLA inactivations had no effect on motor impulsivity and lengthened the latency to choose between LR and HR options; conversely, ACC inactivations increased motor impulsivity but had no effect on choice latency.  A number of alternate explanations must also be considered for the changes in choice behaviour. First, inactivation may have impaired animals’ ability to predict reward 114  magnitude and/or difficulty associated with the lever options, rather than altering effortful decision making per se. This interpretation is especially relevant for the BLA inactivation, which had opposing effects on workers’ and slackers’ choice. If a given inactivation impaired animals’ ability to predict levers’ costs and benefits, choice should move toward indifference or randomness, i.e. 50% for HR. However, BLA inactivation caused choice behaviour to converge on the baseline average for the cohort, well above chance; ACC inactivation, on the other hand, actually caused slackers’ choice to move away from indifference. Furthermore, if animals could not predict the trial’s reward size, animals would presumably return to the food tray to collect their reward equally fast for LR versus HR trials. In both control and inactivation conditions, however, all animals collected HR reward faster than LR, suggesting that animals could predict the larger, two-pellet reward. Taken together, this evidence would suggest that animals were not indifferent to the outcomes and retained the associated values of options despite their altered choice.  Second, a change in choice behaviour may be the result of degraded performance rather than changes in effortful decision making. However, neither inactivation significantly affected accuracy, implying that changes to choice behaviour were not due to an inability to allocate visuospatial attention to the task. Relatedly, changes in choice due to inactivation were observed from the outset rather than accumulating across the session.  Third, inactivation of BLA and ACC also produced a range of motor effects, including increased latency to make correct nosepoke responses, increased number of lever (choice) omissions, and decreased number of completed trials. It is possible that 115  these motor effects may have decreased motivation to expend effort, and thus choice of the HR option. However, loss of BLA or ACC function decreases willingness to exert physical effort on a T-maze task, but when a second barrier is placed in the LR arm (thus equalizing effort costs for LR and HR options), rats resume their preference for HR (Walton et al., 2003b; Floresco and Ghods-Sharifi, 2007b). Thus, the effects of BLA and ACC inactivations likely represent a shift in their decision-making processes rather than some general motor impairment.  Fourth, pharmacological inactivations encompass a considerably smaller region of brain mass (~1mm spread; see Floresco et al., 2006; Marquis et al., 2007) as compared to excitotoxic lesions (Walton et al., 2002b) used in some previous studies. Such a spread would be sufficient to inactivate substantial portions of the BLA but likely only equivalent to a partial ACC lesion. Thus, it may be argued that the current study’s results and, notably, how they differ from previous studies, are due to unintended consequences of experimental design. However, the current methods closely adhered to a physical effort task with BLA inactivations (Ghods-Sharifi et al., 2009). Previous literature noted that loss of BLA function increased latency to choose (Ghods-Sharifi et al., 2009), whereas loss of ACC function increased motor impulsivity as measured by premature responding (Muir et al., 1996); critically, both effects were observed in the present study. Furthermore, Muir et al. (1996) showed that medial prefrontal cortex (mPFC) lesions decreased rats’ accuracy on the 5CSRTT, an effect not observed in the present study. Thus it is unlikely that the baclofen-muscimol substantially spread from ACC to surrounding regions such as the mPFC. Altogether, it is reasonable to infer that the differences observed for BLA/ACC inactivations on the rCET versus a T-maze or lever-116  pressing task were due to the costs (i.e. cognitive versus physical effort) and not some artefact of experimental design.  It is also important to consider the prevalence of individual differences on the rCET, especially because of their absence from the physical-effort literature. As discussed in much greater length in the initial rCET article (Experiment 1), variability in choice preference (i.e. workers and slackers) appears to reflect differences in sensitivity to the effort costs. The greater variation of choice on the rCET, as compared to physical tasks, may be explained by a greater number of trials completed (and thus a greater total amount of effort exertion per session), a higher level of difficulty (~60-70% accuracy for the rCET, versus virtually guaranteed reward on the physical T-maze, for example), or other differences in task design, rather than simply the type of cost (i.e. cognitive versus physical).  Contemporary understanding of amygdalar function has moved away from simple aversive stimulus-outcome associations and toward a role in acquiring, updating, and monitoring value (Everitt et al., 2003; Morrison and Salzman, 2010). Single-cell recordings in primates demonstrate that separate populations of amygdala neurons track positive and negative values of both conditioned and unconditioned stimuli, suggesting that the amygdala encodes a state value for any given moment (Belova et al., 2007; Belova et al., 2008). In rats, BLA neurons fire more robustly for rewarded versus unrewarded stimuli, and BLA inactivation suppresses animals’ responding for reinforced cues (Ishikawa et al., 2008). Furthermore, BLA firing precedes and drives activity in the nucleus accumbens (NAc), a critical site for motivated behaviour (Ambroggi et al., 2008). Loss of amygdala function in both non-human primates and rats prevents animals 117  from updating their behaviour in response to devaluation of a given option via sensory-specific satiety (Wellman et al., 2005; West et al., 2012); value inflation is also dependent on the BLA (Wassum et al., 2011). Such inflexibility in behaviour is also observed in patients with amygdala damage, when a previously beneficial option’s associated risk of punishment is increased (Bechara et al., 1999a).  As regards effort-based decision making, BLA inactivation decreases preference for high-effort options on both T-maze and operant lever pressing (i.e. physical) paradigms (Floresco and Ghods-Sharifi, 2007b; Ghods-Sharifi et al., 2009). This leads to the prima facie assumption that BLA activity overpowers internal representations of cost in order to bias behaviour toward highly rewarding options. However, the present study suggests a more nuanced contribution of the BLA to effortful decision making. The effects of BLA inactivation on the rCET depended critically on animals’ baseline preferences: animals did not uniformly “slack off” in response to BLA inactivation, but rather moved toward the cohort average. This is not simply a statistical regression to the mean, as choice preferences prove remarkably stable within subjects across baseline and all experimental manipulations (Cocker et al., 2012a). Nor are the effects of BLA inactivation due to indifference between the choices, as choice of the HR option remains far above chance. Unlike previous Pavlovian conditioning experiments (Belova et al., 2007), LR and HR stimuli (levers) carry both appetitive (sucrose reward) and aversive (cognitive effort) predictions, and as such may engage both populations of valence-specific neurons in the BLA, which in turn project to regions such as the NAc. The population-level activity of the BLA may therefore represent the interaction of costs and benefits in a choice, encoding the subjective value of given options, rather than the 118  stimulus properties per se; single-cell recordings from human amygdala strongly support this hypothesis (Jenison et al., 2011). As such, BLA inactivations may have fundamentally disrupted rats’ subjective values of options on the rCET, and in absence of these subjective weights, the subsequent decisions relied more heavily on objective stimulus properties (Dolan, 2012) or simpler processes such as matching law (Herrnstein, 1970).  Such a hypothesis could also explain why the BLA appears necessary for both cognitive and physical effort-based decision making, but with distinct effects of its inactivation depending on the task costs. In the rCET, the considerable and sustained mental effort costs, in addition to no guarantee of reward, may drive high activity in negatively valenced BLA neurons of some animals, shifting these animals’ preference away from high-effort options. By comparison, the relatively brief costs of physical tasks, as well as virtually guaranteed receipt of reward, may allow positively valenced BLA neurons to dominate population activity and guide behaviour of most animals toward highly rewarding options.   On the other hand, ACC inactivations decreased willingness to expend cognitive effort for all animals, the same effect observed on physical effort-based decision-making tasks (Walton et al., 2003a; Rudebeck et al., 2006a; Walton et al., 2009). In non-human primates, ACC neurons preferentially track action-outcome versus stimulus-outcome associations (Luk and Wallis, 2013) and appear to encode the subjective sensitivity to reward value following its delivery (Cai and Padoa-Schioppa, 2012); this is in contrast to BLA neurons which, as mentioned above, likely represent the subjective value of given options prior to reward delivery. In rats, the ACC shows greater metabolic demands 119  when integrating effort costs and reward magnitude versus when effort costs are held constant and only reward magnitude varies (Endepols et al., 2010). Similarly in humans, functional imaging studies suggest that ACC activity at choice and feedback correlates with the option that maximizes reward over the long term (Boorman et al., 2013), and ACC activation is greater when rejecting highly valued options versus rejecting less preferred options (Izuma et al., 2010). Altogether, this suggests that the ACC biases individuals away from prepotent but suboptimal responses, in effect guiding behaviour toward maximal returns. Such a putative function explains why ACC inactivation shifted all animals’ preference toward the LR option on the rCET, and why it would have a stronger effect on slackers versus workers, namely the already strong drive for slackers to choose LR trials. This function also corresponds well with tobacco- and alcohol-dependent individuals, wherein ACC dysfunction heightens salient, prepotent cues for drug and can impair decision making (Le Berre et al., 2012; Janes et al., 2013).  The disturbances of effort-based decision making observed in a number of mental health populations (Treadway et al., 2012a; Gold et al., 2013b) may therefore reflect underlying functional and connective changes to regions such as the amygdala and ACC. The overlapping-yet-distinct effects observed in the present experiments suggest that regions not required for physical effort-based decision making, such as the prefrontal cortex, may in fact be necessary for decision making with cognitive effort costs (Schmidt et al., 2012), and the nucleus accumbens’ prevalence in the effort literature warrants future consideration of its involvement in choice on the rCET. To conclude, these results may disentangle the unique contributions of the BLA and ACC, namely the subjective valuation of options versus the biasing of behaviour toward advantageous choice 120  strategies, respectively, and offer unique insights into targeting these regions for therapeutic intervention.  6.5 Figures   Figure 6.1. Histological analysis of cannulae implantation.  (A) Location of all acceptable ACC infusions, including a representative photomicrograph. (B) Location of all acceptable BLA infusions, including a representative photomicrograph. Coordinates are relative to bregma. Plates modified from Paxinos and Watson (1998).  121   Figure 6.2. Effects of BLA inactivations on the rCET.  (A) Infusion of baclofen-muscimol into the BLA had no effect on animals’ choice of LR or HR trials when considered as a homogenous group (inactivation: F1,12 < 0.001, NS). However, BLA inactivation differentially affected workers and slackers, increasing choice of HR trials in slackers but decreasing this behaviour in workers (inactivation x group: F1,12 = 5.445, p = 0.038). (B)-(C) Inactivation of the BLA did not affect accuracy or premature responding (inactivation / choice x inactivation: all Fs < 1.154, NS). (D) For all animals, BLA inactivation increased the amount of time to choose between LR and HR levers (inactivation: F1,12 = 17.310, p = 0.001; inactivation x choice: F1,12 = 2.210, NS; group / inactivation x group: all Fs < 0.322, NS). Data are shown as the mean percent (A-C) or mean time in seconds (D) for each variable (± SEM). 122   Figure 6.3. Effects of ACC inactivations on the rCET.  (A) Infusion of baclofen-muscimol into the ACC decreased all animals’ preference for the HR option (inactivation: F1,24 = 6.178, p = 0.020; inactivation x group: F1,24 = 1.572, NS). (B) ACC inactivation had virtually no effect on accuracy, with a significant Inactivation by Choice interaction but not significantly increasing or decreasing LR or HR when considered alone (inactivation: F1,24 = 0.023, NS; inactivation x choice: F1,24 = 4.671, p = 0.041; inactivation x choice x group = 3.918, p = 0.059; LR, workers only -inactivation: F1,9 = 4.977, p = 0.053; LR / HR / HR workers / LR slackers / HR slackers -inactivation: all Fs < 2.962, NS). (C) For all animals, inactivation of the ACC increased premature responding for both trial types (inactivation: F1,24 = 9.718, p = 0.005; group / inactivation x choice / inactivation x choice x group: all Fs < 1.891, NS). (D) ACC 123  inactivation had no effect on the latency to choose between LR and HR levers (inactivation / inactivation x choice: all Fs < 1.938, NS). Data are shown as the mean percent (A-C) or mean time in seconds (D) for each variable (± SEM).   124  Chapter 7: Experiment 5  Prefrontal cortical inactivations decrease willingness to expend cognitive effort on a rodent cost/benefit decision-making task  7.1 Introduction  Successful decision making frequently requires weighing a given option’s costs against its associated benefits, and perturbations in this cost/benefit decision making are observed in a number of marginalized groups, including those who suffer from mental illness or live below the poverty line (Gleichgerrcht et al., 2010; Goschke, 2014; Haushofer and Fehr, 2014). The origin of such decision-making studies resides in economics, wherein individuals are identified as “irrational” if they fail to optimize their financial returns on a given task (Tversky and Kahneman, 1981). Indeed, so-called optimal decision making is better than measures of IQ at predicting future “life success”, including emotional coping, SAT scores, educational attainment, and body/mass index (Mischel et al., 1989; Mischel et al., 2011; Schlam et al., 2013).  However, personal success also requires judicious use of our limited cognitive/neurobiological resources, such as attention; the sensation of exerting effort reflects a strain on these limited resources (Kahneman, 1973). The specific computations/mechanisms that effort represents is currently unclear (Kurzban et al., 2013), but nevertheless individuals appear to have finite mental capacities to apply at any one time. Furthermore, a substantial body of literature contends that economic models of rationality are often too narrow in their timescale and index of optimality (for a review, 125  see Brase, 2014) and do not take individuals’ biological limitations into account (Gigerenzer and Selten, 2001). As such, conditions can be met wherein seemingly sub-optimal choices may in fact be driven by a rational strategy (McGuire and Kable, 2013).  A key neurobiological locus of both attention and effort is the prefrontal cortex (PFC). In rodent physiological studies, PFC activity appears to track sustained attention across the session, and reducing the effort required also reduces PFC activity (Passetti et al., 2000; Dalley et al., 2001). Human imaging studies suggest that the PFC is involved in mental effort exertion (Schmidt et al., 2012), and individual variation in both lateral and medial PFC functioning correlates to individual differences in effortful choice (McGuire and Botvinick, 2010; Treadway et al., 2012b). Related to this, prefrontal dysfunction is observed in many mental illnesses as well as stress (Rogers et al., 2004; Arnsten, 2011), and individuals suffering from these conditions also demonstrate both impaired attentional performance (Vedhara et al., 2000; Paelecke-Habermann et al., 2005; Hong et al., 2011) and sub-optimal decision making with effort costs (Shafiei et al., 2012; Treadway et al., 2012a; Barch et al., 2014). One intriguing possibility is therefore that these individuals’ aberrant choice strategies are not solely the result of compromised decision-making processes per se but rather because individuals are more judiciously applying their diminished attentional capacities.  Here we utilize the rat Cognitive Effort Task (rCET) to directly examine the relationship between PFC dysfunction, attention, and effort. To obtain a within-subjects comparison of effortful choice with and without intact PFC functioning, we used temporary inactivations of two sub-regions of the rat medial PFC, the putative functional and connective homologue to human lateral and medial PFC (Uylings et al., 2003; 126  Seamans et al., 2008). As lesions to these regions previously impaired rats’ attentional performance on the 5CSRTT (Muir et al., 1996; Passetti et al., 2002), we therefore hypothesized that animals would decrease their choice of high-effort, high-reward trials as their performance declined.  7.2 Additional methods  Surgery   Surgical methods were based on a previous study (Hosking et al., 2014). When baseline performance was deemed statistically stable (40 sessions), animals were implanted with 22-gauge stainless steel guide cannulae (Plastics One; Roanoke, Virginia, USA) bilaterally into the medial prefrontal cortex (mPFC) using standard stereotaxic techniques. Animals were anaesthetized with 2% isoflurane in O2 and implanted at the following co-ordinates: AP = +2.7mm, ML = ±0.75 mm from bregma, DV = -2.3mm from dura (Paxinos and Watson, 1998). Guide cannulae were fixed to the skull via three stainless steel screws and dental acrylic, and obdurators with dust caps were inserted and extended flush with the end of the cannulae. Animals were given at least one week of recovery in their home cages before subsequent testing. Five animals were excluded due to poor recovery.   Microinfusion   Following recovery, animals performed ten free-choice sessions, after which all individuals displayed stable behaviour. Animals were then habituated to the 127  microinfusion process with two mock infusions, wherein the 30-gauge injectors with tips extending 1mm beyond the guide cannulae were inserted for two minutes but no infusion was performed, followed by a testing session. Infusions adhered to a 3-day cycle starting with a baseline session, followed by a drug or saline injection session, and then by a non-testing day. The prelimbic (PL) and infralimbic (IL) cortices were each inactivated by a mixture of the GABAB agonist baclofen and the GABAA agonist muscimol (Sigma-Aldrich; Oakville, Ontario, Canada), prepared separately at 0.5 µg/µL in saline and mixed together in equal volumes to form a 0.25 µg/µL solution. 0.5 µL per hemisphere injections of saline or baclofen/muscimol (i.e. 0.125 µg of drug per hemisphere) were administered bilaterally at a rate of 0.4 µL/minute, and injectors were left in place for an additional minute to allow diffusion. Injectors were then removed, obdurators were replaced, and animals were returned to their home cages for 10min before being placed in the operant chambers and performing the rCET. Animals underwent four infusion sessions in total: on the first infusion day, half of the rats received saline infusions to the PL (via injectors with +1mm tips) while the other half received baclofen/muscimol to the PL; these administrations were reversed on the second infusion day, allowing for a within-subjects comparison; on the third infusion day, half of the rats received saline infusions to the IL (via injectors with +2mm tips) while the other half received baclofen/muscimol; and again these administrations were reversed on the fourth infusion day. Animals were given a minimum of one week drug-free testing between PL and IL inactivations, to minimize any carryover effects. PL inactivation caused severe behavioural disruption in one animal, while IL inactivation caused behavioural disruption 128  in two animals, such that these animals no longer completed rCET trials, and thus these animals were removed from each respective analysis.   Histology   Following completion of all behavioural testing, animals were anaesthetized with isoflurane and sacrificed by carbon dioxide exposure. Brains were removed and fixed in 4% formaldehyde for at least 24 hours, transferred to a 30% sucrose solution, and then frozen and cut via cryostat into 40 µm coronal sections. These sections were stained with Cresyl violet for visualization, and the projected locations of the injector tips protruding from the guide cannulae were mapped onto standard sections from Paxinos and Watson (1998).  7.3 Results   Cannula placements   The locations of all acceptable placements, as well as a representative sample of mPFC cannulation, are depicted in Figure 7.1. Two animals were excluded because of inaccurate placements in one or both hemispheres, leaving a total of 24 animals for analysis (workers: n = 14; slackers: n = 10).    Prelimbic cortex (PL) inactivation   Choice behaviour, accuracy, and premature responses. Baseline behaviour has been discussed in detail elsewhere and thus will only be briefly addressed. Animals chose 129  high-effort/high-reward (HR) trials more than low-effort/low-reward (LR) trials when the PL was infused with saline (choice: F1,21 = 39.638, p < 0.001). Substantial individual variation in choice behaviour remained, and workers continued to choose a greater proportion of HR trials than slackers (saline only—group: F1,21 = 38.390, p < 0.001). Inactivation of the PL significantly decreased all animals’ choice of HR (Figure 7.2a; inactivation: F1,21 = 5.236, p = 0.033; inactivation x group: F1,21 = 0.078, NS).  As previously demonstrated, animals were more accurate on LR versus HR trials (saline only—choice: F1,21 = 113.923, p < 0.001) and there were no differences in accuracy between workers and slackers, indicating that choice preferences were not solely driven by animals’ ability to perform the task (saline only—choice x group / group: all Fs < 1.003, NS). PL inactivation decreased all animals’ accuracy for both trial types (Figure 7.2b; inactivation: F1,21 = 6.385, p = 0.020; inactivation x group / choice x inactivation / choice x inactivation x group: all Fs < 0.552, NS).  All animals showed equivalent levels of premature responding for LR and HR trials (saline only—choice / choice x group / group: all Fs < 2.026, NS), reiterating that choice preferences were not simply driven by individuals’ level of motor impulsivity. PL inactivation had no main effect on premature responding, although there was a trend for increased premature responding in workers (Figure 7.2c; inactivation / choice x inactivation / choice x inactivation x group: all Fs < 2.125, NS; inactivation x group: F1,21 = 3.979, p = 0.059; workers only—inactivation: F1,12 = 4.356, p = 0.059; inactivation x choice / slackers only—inactivation / inactivation x choice: all Fs < 2.439, NS).  Other behavioural measures. For all animals at saline conditions, nosepoke-response omissions were equivalent for LR and HR (choice / choice x group / group: all 130  Fs < 2.491, NS). Inactivation of the PL dramatically increased these response omissions for all animals on both trial types, indicating a fundamental impairment to maintain attention on the five-hole stimulus array (Figure 7.2d; inactivation: F1,21 = 39.760, p < 0.001; inactivation x group / choice x inactivation / choice x inactivation x group: all Fs < 1.574, NS). However, PL inactivation did not increase the number of lever/choice omissions (inactivation / inactivation x group: all Fs < 1.070, NS), suggesting that the PL inactivation’s effects on response omissions were not simply driven by motor impairments. Inactivation of the PL had no effect on the latency to choose between LR and HR levers (inactivation / inactivation x group / choice x inactivation / choice x inactivation x group: all Fs < 3.214, NS) but increased the latency to make a correct nosepoke response for all animals on both trial types (inactivation: F1,21 = 24.218, p < 0.001; inactivation x group / choice x inactivation / choice x inactivation x group: all Fs < 3.609, NS). Similar to previous cohorts, there was a trend for animals to collect reward faster following HR versus LR trials (saline only—choice: F1,21 = 4.095, p = 0.056; choice x group / group: all Fs < 1.676, NS), indicating that both workers and slackers differentiated the two reward contingencies (i.e. slackers were not indifferent to the options, despite roughly equivalent choice of LR and HR at saline). PL inactivation increased this collection latency for all animals on both trial types (inactivation: F1,21 = 6.107, p = 0.022; inactivation x group / choice x inactivation / choice x inactivation x group: all Fs < 1.243, NS). Finally, inactivation of the PL decreased the number of completed trials for all animals (inactivation: F1,21 = 29.312, p < 0.001; inactivation x group: F1,21 = 1.040, NS).   131  Infralimbic cortex (IL) inactivation   Choice behaviour, accuracy, and premature responses. Inactivation of the IL decreased choice of HR for all animals (Figure 7.3a; inactivation: F1,20 = 6.111, p = 0.023). However, IL inactivation had no significant effect on animals’ accuracy (Figure 7.3b; inactivation / inactivation x group / choice x inactivation / choice x inactivation x group: all Fs < 1.338, NS). IL inactivation also significantly increased premature responding for all animals across both trial types (Figure 7.3c; inactivation: F1,20 = 6.766, p = 0.017; inactivation x group / choice x inactivation / choice x inactivation x group / group: all Fs < 2.570, NS).  Other behavioural measures. Inactivation of the IL increased the proportion of nosepoke-response omissions for all animals across both trial types, indicating difficulties remaining engaged with the trial (Figure 7.3d; inactivation: F1,20 = 19.041, p < 0.001; inactivation x group / choice x inactivation / choice x inactivation x group: all Fs < 1.380, NS), whereas it had no effect on lever/choice omissions (inactivation / inactivation x group: all Fs < 0.579, NS). IL inactivation did not affect lever/choice latency (inactivation / inactivation x group / choice x inactivation / choice x inactivation x group: all Fs < 0.684, NS) but increased the latency to correctly nosepoke for all animals (inactivation: F1,20 = 13.622, p = 0.001; inactivation x group / choice x inactivation / choice x inactivation x group: all Fs < 1.899, NS) and increased all animals’ latency to collect reward, especially following successful LR trials (inactivation: F1,20 = 10.975, p = 0.003; choice x inactivation: F1,20 = 5.314, p = 0.032; inactivation x group / choice x inactivation x group: all Fs < 0.323, NS; LR only—inactivation: F1,20 = 10.743, p = 0.004; HR only—inactivation: F1,20 = 3.818, p = 0.065). Finally, inactivation of the IL 132  decreased the number of completed trials for all animals (inactivation: F1,20 = 28.759, p < 0.001; inactivation x group: F1,20 = 1.381, NS).  7.4 Discussion  Here for the first time we demonstrate PFC contributions to a rodent model of effort-based decision making. Temporary inactivation of either the prelimbic (PL) or infralimbic (IL) subdivisions of the medial PFC decreased all animals’ willingness to expend cognitive effort. Some dissociation was also observed between the two regions: PL inactivations decreased animals’ performance (i.e. accuracy), whereas IL inactivations increased animals’ motor impulsivity (i.e. premature responding), a finding that parallels previous reports (Muir et al., 1996; Passetti et al., 2002; Chudasama et al., 2003). Response omissions sharply increased for inactivations of both regions, and when considered in tandem with accuracy effects, it appears that PFC inactivations greatly impaired animals’ ability to perform the task via decreasing attention. Taken together, these data imply that the prefrontal cortex contributes to an attentional resource pool, and when these resources are diminished, animals will shift their choice (via other brain regions) toward a more judicious strategy.  One potential limitation is the nature of the rat PFC as it relates to its primate homologue. When connectivity, function, and neurotransmitter/neuromodulatory presence are considered together, there is some consensus that the rat IL, PL, and anterior cingulate cortex (ACC) are less differentiated than their human counterparts but that they share features of human dorsolateral PFC and ACC (Uylings et al., 2003; Seamans et al., 2008). Despite this neuroanatomical overlap in rat PL, IL, and ACC, these regions’ 133  unique contributions to behaviour have been demonstrated via selective inactivations and electrophysiological recordings (Seamans et al., 1995; Burgos-Robles et al., 2013). In a previous study using the rCET (Experiment 4), we demonstrated effects of ACC inactivation that both overlap with and diverge from the current results, particularly IL inactivation (which is non-contiguous with the ACC; Paxinos and Watson, 1998). It is therefore reasonable to infer that, while more rudimentary than in primates, the rat PFC is in many ways a valid model for exploring PFC contributions to decision making.  Another important consideration is the rCET’s effort costs as they relate to prefrontal functioning. In contrast to the current data, previous rat models of effort-based decision making found that selective IL-PL lesions had no effect on animals’ willingness to exert effort (Walton et al., 2002b; Walton et al., 2003a). However, these studies utilized a T-maze task wherein animals could scale a barrier in one arm for a larger reward, or enter an open arm for a smaller reward; in other words, the task’s effort demands were physical rather than cognitive in nature, and this difference may underlie the divergence from the current results. A growing body of rCET research supports the notion of interrelated-yet-distinct neurobiological mechanisms for cognitive versus physical effort (Experiments 1-4), and at least one human neuroimaging study suggests the same, noting that lateral PFC activity is increased for mental but not physical effort expenditure (Schmidt et al., 2012). Nevertheless, PFC engagement has been observed in human decision making with both mental and physical effort costs (McGuire and Botvinick, 2010; Treadway et al., 2012b), and to the best of our knowledge, no other established physical effort decision-making task in rats has been used to examine IL-PL contributions. Furthermore, it has long been argued that high-effort conditions necessitate 134  increased attention, regardless of whether they are mentally or physically demanding (Kahneman, 1973). One possibility is therefore that the T-maze paradigm’s HR option is not sufficiently demanding to recruit the attentional resources embodied by the PFC.  While the PFC undoubtedly contributes to many cognitive processes (e.g. behavioral flexibility; Grace et al., 2007), converging evidence has long implicated prefrontal activity in voluntary, or “top-down”, attentional processes (Jansen et al., 1955; Buschman and Miller, 2007; Squire et al., 2013). Research with PFC-lesioned patients suggests that lateral, and to a lesser extent medial, PFC contributes to many aspects of voluntary attention, including novelty processing and anticipatory attention (Solbakk and Lovstad, 2014). Such lesions negatively affect both divided and sustained attention, increasing individuals’ propensity to be distracted by irrelevant stimuli (Godefroy and Rousseaux, 1996); rats demonstrate a similar pattern of deficits following medial PFC lesions (Granon et al., 1998; Broersen and Uylings, 1999). In addition to the direct impairments of attention in the current study (i.e. animals’ accuracy), PFC inactivations increased rats’ response omissions, i.e. failures to nosepoke any hole following stimulus presentation. It is at present impossible to determine whether this reflects animals trying but failing to detect the target, being distracted before attending to the apertures, or suffering some unrelated motor slowing; however, the latter interpretation appears less likely, as these animals do not demonstrate motor impairments on similar behavioural measures, such as lever (choice) omissions or choice latency. Altogether, inactivations of the PFC appear to have decreased animals’ ability to sustain attention and increased their distractibility during the task, an interpretation that is strongly supported by the literature. 135   Decision making comprises a variety of constituent processes, and as such also requires contributions from a number of brain regions (Dolan, 2012). A substantial body of literature implicates cortico-limbic-striatal circuits in various forms of cost/benefit decision making and goal-oriented behaviour (Grace et al., 2007; Floresco et al., 2008c; Hosking et al., 2014). These regions, which include much of the frontal cortex, ACC, amygdala, hippocampus, midbrain, and striatum have been shown to subserve unique and overlapping components of decision making, with both PFC and the striatum implicated in the choice, or action selection, process (Cools et al., 2004; O'Doherty, 2004; Ridderinkhof et al., 2004; Rushworth et al., 2005; Nicola, 2007; Kimchi and Laubach, 2009; O'Doherty, 2011; Seo et al., 2012; Tai et al., 2012). Thus, one interpretation of the current data is that PFC inactivations impaired animals’ ability to select actions based on the options’ contingencies. However, this seems unlikely as a sole explanation for a number of reasons. First, inactivations sites were relatively small as compared to lesions, encompassing approximately a 1mm spread per hemisphere (Floresco et al., 2006; Marquis et al., 2007; Hosking et al., 2014), and thus would have left much of the PFC intact for all conditions. Second, PFC inactivations did not drive animals’ behaviour toward indifference (i.e. 50% choice of HR), nor did they cause behavioural inflexibility (i.e. exacerbate existing choice preferences); rather, all animals decreased their choice of HR, regardless of their baseline choice preferences, with some animals (slackers) in fact moving away from 50% and toward 0%. Together, these data suggest that PFC inactivations caused a greater detriment to attentional processes than action selection, and suggest that other regions within the decision-making circuit, for example the striatum, drove changes in choice behaviour in response to decreased attentional reserves. 136   One frequently reported finding is that individuals living below the poverty line demonstrate greater risk- and delay-discounting than those more financially secure (for a review, see Haushofer and Fehr, 2014). In addition to effects of this chronic stress, poorer individuals have fewer financial resources to spend but the same biological, social, and evolutionary needs to fulfill, and thus they opt instead for smaller, sooner, sure gains. Such behaviour may be identified as “irrational” from an economics perspective, but cognitive biases such as risk aversion appear relatively well conserved across mammalian species (Cocker et al., 2012b; Rogers et al., 2013; Yamada et al., 2013; Tremblay et al., 2014), and thus may indeed positively contribute to an organism’s fitness. A similar case can be made for mental resources. All other task contingencies being equal, individuals will avoid options with higher mental effort demands (Kool et al., 2010). Greater subjective sensitivity to mental effort predicts greater avoidance of those high-effort options (McGuire and Botvinick, 2010; Kool et al., 2013), and common motivational nodes such as the striatum appear to subtract mental effort costs from their associated benefits in order to arrive at a net value signal for action selection (Botvinick et al., 2009). When brain regions responsible for effort expenditure (in this case, the PFC) are compromised, along with the faculties they provide, the striatum may therefore adjust behaviour according to a new net value. This model is supported both by the current data and aberrant effortful decision making observed in individuals with putative PFC dysfunction (Treadway et al., 2012a; Gold et al., 2013a; but see Gold et al., 2014).  One obvious hypothesis that arises from this research is that improvements to an individual’s cognitive resources should concomitantly increase their willingness to expend said mental effort. As such, therapeutic approaches that aim to boost the 137  resources that decision making requires, irrespective of the decision-making process per se, should be effective at ultimately improving choice. One option may be to exploit drugs that benefit attention, such as those that increase acetylcholine function (Wilens and Decker, 2007; Klinkenberg et al., 2011; Bracco et al., 2014). However, in all the experiments we have performed to date using the rCET, choice of the HR option and ability to perform the HR trials are not predictive of each other in healthy animals. Put another way, cognitively lazy animals (i.e. slackers) are just as accurate on HR trials as workers; ability is not synonymous with endeavour. We recently observed a particularly powerful example of this disconnect in that nicotine administration decreased animals’ choice of HR despite increasing their accuracy on the rCET (Experiment 3). As such, increasing attentional abilities by any means may not increase willingness to exert cognitive effort.   However, bearing all these data in mind, perhaps the most important aspect of the current results is that they indicate quite clearly that ability to work and willingness to work are tightly coupled at the level of the PFC; when input is lost from this region, accuracy decreases and/or impulsivity rises and the strategy switches to one that requires less effort. By extension, restoring prefrontal activity in individuals with PFC dysfunction, or boosting the connections between this region and action-selection areas such as the striatum, should enhance ability and willingness to work in tandem. Identifying the mechanisms by which ability and effort are regulated cohesively as well as those that lead to independent modulation of these processes may improve our understanding of how engagement in cognitively effortful processes can be encouraged in both the healthy and diseased brain. 138   The ability to rebalance cost/benefit decision making would be of benefit not only to those diagnosed with mental illness, but may also have an impact on those whose quality of life, and subsequently their decision making, is altered due to sociocultural factors (e.g. poverty, chronic stress). Delineating the circumstances under which neural pathways are activated to reallocate resources towards strategies high or low in cognitive effort may therefore be of significant value to neuroscientists and economists alike.   139  7.5 Figures   Figure 7.1. Histological analysis of cannulae implantation.  Location of all acceptable PFC infusions (black dots: prelimbic cortex; grey dots: infralimbic cortex), including a representative photomicrograph. Coordinates are relative to bregma. Plates modified from Paxinos and Watson (1998).  140   Figure 7.2. Effects of prelimbic cortex (PL) inactivations on the rCET.  (A) Infusion of baclofen-muscimol (BacMus) into the PL significantly decreased all animals’ choice of HR (inactivation: F1,21 = 5.236, p = 0.033; inactivation x group: F1,21 = 0.078, NS). (B) PL inactivation decreased all animals’ accuracy for both trial types (inactivation: F1,21 = 6.385, p = 0.020; inactivation x group / choice x inactivation / choice x inactivation x group: all Fs < 0.552, NS). (C) PL inactivation had no main effect on premature responding, although there was a trend for increased premature responding in workers (inactivation / choice x inactivation / choice x inactivation x group: all Fs < 2.125, NS; inactivation x group: F1,21 = 3.979, p = 0.059; workers only—inactivation: F1,12 = 4.356, p = 0.059; inactivation x choice / slackers only—inactivation / inactivation x choice: all Fs < 2.439, NS). (D) Inactivation of the PL dramatically increased nosepoke 141  response omissions for all animals on both trial types (inactivation: F1,21 = 39.760, p < 0.001; inactivation x group / choice x inactivation / choice x inactivation x group: all Fs < 1.574, NS). Data shown are the mean percent for each variable (± SEM).   Figure 7.3. Effects of infralimbic cortex (IL) inactivations on the rCET.  (A) Baclofen-muscimol (BacMus) inactivation of the IL decreased choice of HR for all animals (inactivation: F1,20 = 6.111, p = 0.023). (B) However, IL inactivation had no significant effect on animals’ accuracy (inactivation / inactivation x group / choice x inactivation / choice x inactivation x group: all Fs < 1.338, NS). (C) IL inactivation also significantly increased premature responding for all animals across both trial types (inactivation: F1,20 = 6.766, p = 0.017; inactivation x group / choice x inactivation / 142  choice x inactivation x group / group: all Fs < 2.570, NS). (D) Inactivation of the IL increased nosepoke response omissions for all animals across both trial types, indicating difficulties remaining engaged with the trial (inactivation: F1,20 = 19.041, p < 0.001; inactivation x group / choice x inactivation / choice x inactivation x group: all Fs < 1.380, NS). Data shown are the mean percent for each variable (± SEM).   143  Chapter 8: General Discussion       8.1 Summary of experimental findings  Here we validate a cost/benefit decision making model with cognitive effort costs, as well as examine both cortico-limbic and neuromodulatory influences on this model. As with delay-, risk-, and physical effort-based choice, decision making with mental effort costs has its own unique and overlapping contributions from previously implicated neural circuitry and neurochemistry. Furthermore, we provide preliminary investigation into the neurobiological basis of individual differences in choice preference, and point to potential targets for future research.  Experiment 1 demonstrated that animals develop remarkably stable choice preferences in their willingness to expend cognitive effort. These preferences were not simply based on animals’ attentional ability to perform the task, baseline differences in motor impulsivity, or indifference to the larger reward. Nor were these choice preferences related to animals’ sensitivity to risk; when effort costs were removed, as in the yoked-control task, animals did not choose according to their yoked worker/slacker designation. Willingness to expend effort for reward was also goal-directed, as acute and chronic satiation (i.e. devaluation of the sugar reward) decreased all animals’ choice of the high-effort option. Thus, the most parsimonious explanation for differences in choice preference appears to be baseline differences in animals’ sensitivity to the cognitive effort costs. 144   Experiment 1 also examined drugs commonly self-administered by individuals. Interestingly, effects of psychostimulants (amphetamine and caffeine) were a function of animals’ baseline preferences: workers slacked off in response to psychostimulants, while slackers worked harder under amphetamine but not caffeine. These data provided the first evidence that individual differences in rCET choice may be due to underlying differences in neuromodulatory function.  Experiment 2 followed up on this hypothesis and also directly compared animals’ willingness to expend cognitive versus physical effort. As amphetamine notably potentiates dopamine function (Robertson et al., 2009), and as previous studies suggested that effort-based decision making is sensitive to dopamine antagonism (Salamone et al., 1994; Floresco et al., 2008a), we systemically administered separate antagonists for the D1- and D2-family of receptors. Neither of these drugs, nor administration of noradrenergic agonists (another neuromodulator upon which amphetamine has an influence), had any effect on willingness to expend cognitive effort; however, both dopamine antagonists decreased animals’ choice of high-effort options on the physical effort task. Furthermore, animals’ baseline choice preferences for the physical task were transiently but significantly correlated to choice preference on the cognitive task. Taken together, these data suggested that mental and physical effort are in some respects part of a cohesive construct of effort, but also in part dissociable at the neuromodulatory and behavioural level.  Experiment 3 examined the role of another neuromodulator, acetylcholine, in decision making with cognitive effort costs. The cholinergic system was chosen for three reasons: previous studies (Muir et al., 1992; Muir et al., 1995) demonstrating its influence 145  on the 5CSRTT, the precursor to the rCET; amphetamine’s potentiation of cholinergic functioning (Arnold et al., 2001); and because cholinergic contributions to decision making remain relatively under-examined (Fobbs and Mizumori, 2014). Again we showed effects of pharmacological agents as a function of baseline differences, with nicotine decreasing slackers’ willingness to expend effort and scopolamine more markedly decreasing workers’ choice of high-effort options. Furthermore, we showed that these drugs’ effects on choice were dissociable from effects on other behavioural measures, such as premature responding (i.e. motor impulsivity). Altogether, acetylcholine’s influence over choice on the rCET is the most unambiguous of all the data collected; extending these studies and elucidating the precise mechanisms underlying acetylcholine’s relation to decision making remains a promising avenue of future research.  Experiment 4 targeted two structures well known for their contribution to physical effort, namely the ACC and BLA, and used temporary inactivations to infer their contributions to choice with mental effort costs. Similar to physical effort studies (Rudebeck et al., 2006a), ACC inactivation decreased all animals’ willingness to expend effort. BLA data diverged somewhat from the physical effort literature (Ghods-Sharifi et al., 2009), in that individual differences again played a role: workers slacked off in response to BLA inactivation, whereas slackers worked harder. These results demonstrated dissociable roles for ACC and BLA in effort-based decision making, a finding hitherto unreported in the literature.  Experiment 5 focused on contributions of the mPFC, a region previously shown to be unrelated to decision making with physical effort costs (Walton et al., 2003b). In 146  contrast to these latter reports, but in line with human studies (McGuire and Botvinick, 2010; Treadway et al., 2012b), both PL and IL inactivations decreased all animals’ willingness to exert mental effort. These changes in choice behaviour were likely due to severely diminishing the cognitive resources required for this form of decision making, namely sustained attention, and thus animals adjusted their behaviour accordingly. As such, these data suggest that other brain regions at least in part govern the choice, or action selection, process.  8.2 Theoretical implications, and predictions for future studies  Table 8.1 summarizes all pharmacology and temporary inactivation results from Experiments 1 through 5. Together, these data suggest that decision making with mental effort costs requires sophisticated neural circuitry and neurochemistry: ACC activity seems to guide animals toward high-reward options, regardless of the specific effort costs; BLA activity may drive subjective valuation, and thus animals’ choice preferences; PFC activity appears critical for the ability to direct and sustain attention; cholinergic function influences the choice process perhaps by re-weighting inputs and outputs from these (or other unspecified) brain regions; and other brain regions and neuromodulatory influences (or combinations thereof) likely play a role in the choice process. These results suggest a number of hypotheses to test in the future. This discussion will focus on those hypotheses that are currently testable within the candidate’s supervisor’s laboratory, although this excludes a number of experiments that could be otherwise addressed, for example via electrophysiological recordings of the BLA. 147   8.2.1 Individual differences in underlying neurobiology, revealed via protein or RNA expression. As a number of manipulations had dissociable choice effects on workers versus slackers (i.e. amphetamine, caffeine, nicotine, BLA inactivation), this suggests measurable differences in underlying expression of proteins and/or RNA at specific brain regions based on choice preference. Using Western blot or quantitative Polymerase Chain Reaction (qPCR), comparison of baseline choice on the rCET versus protein/RNA expression for nicotinic and muscarinic acetylcholine receptors, other cholinergic-system related proteins (e.g. cholinesterase, which enzymatically degrades acetylcholine), adenosine receptors (which are antagonized by caffeine), serotonin receptors (see below), or general influences on neuromodulation (e.g. monoamine transporters such as DAT, NET, and SERT), could reveal the fundamental neurobiological differences underlying our choice preferences. Furthermore, this relationship could be 1) regional specific, showing (for example) that variation in cholinergic-system proteins/RNA at striatum but not PFC correlates to individual differences in choice; or 2) receptor-variant specific, showing that (for example) certain combinations of nicotinic α and β subunits are more or less influential on choice (Mendez et al., 2013). Finally, while dopamine antagonism had no effect on rCET choice, dopamine-related influences may still exist at a regional level, for example in BLA but not PFC.  8.2.2 Dissociable roles for cortical versus striatal cholinergic function. The cholinergic system can be coarsely divided into three separate systems: regions of the tegmentum project diffusely to the midbrain and thalamus; the basal forebrain nuclei project to the cortex and hippocampus; and a subset of striatal interneurons are cholinergic and project locally (Dani and Bertrand, 2007). PFC acetylcholine has been 148  shown to increase with sustained attention on the 5CSRTT (Dalley et al., 2001), whereas the current data and extensive literature suggest striatal involvement in the action-selection process (e.g. Nicola, 2007). Taken together, this leads to the hypothesis that loss of PFC versus striatal acetylcholine may have dissociable effects on accuracy and choice for the rCET, and suggest unique contributions for these two cholinergic outputs. Such a hypothesis could be tested via cholinergic-specific lesions of the basal forebrain versus striatum, for example using the cholinergic immunotoxin 192-IgG-saporin (Klinkenberg et al., 2011).  8.2.3 Striatal involvement on the rCET. While more prosaic than some of the other hypotheses, striatal contributions to behaviour on the rCET should be tested. Animal models of cost/benefit decision making, as well as many human studies, focus on the role of the nucleus accumbens (NAc), often referred to as the ventral striatum (Floresco et al., 2008c; FitzGerald et al., 2014); recent studies also implicate dorsal striatal contributions, previously considered to be motor- rather than reward-related (Kravitz et al., 2012). While the lack of effects for dopaminergic antagonism in Experiment 2 made NAc inactivations a lesser priority for the candidate, the results of Experiments 3 and 5 suggest striatal regulation of choice behaviour. Such investigations could take the temporary inactivation approach, as per Experiments 4 and 5, and/or also examine acetylcholine-specific agonism or antagonism in the striatum (similar to 8.2.2). 8.2.4 Other neuromodulatory influences on choice. Interestingly, no pharmacological manipulation of a single neuromodulator (i.e. dopamine, norepinephrine, acetylcholine) reproduced a pattern of effects related to those observed during amphetamine challenge. Two possibilities remain unexplored. First, serotonin is 149  also potentiated by amphetamine, and its involvement in another animal model of decision making has been demonstrated (Zeeb et al., 2009). Second, at least one study has shown that simultaneously potentiating two neuromodulatory systems, but neither system on its own, is necessary to elicit changes in choice behaviour (Baarendse et al., 2013). As such, either changes to serotonin, or a combination of neuromodulatory systems, may underlie amphetamine’s effects on the rCET. Both hypotheses could easily be tested via systemic pharmacology, similar to Experiments 1 through 3.  8.2.5 Restoration of choice following basal forebrain depletions or TBI via nicotine. Data from Experiment 5 suggest that changes in choice behaviour following PFC inactivation may be driven by a loss of the resources (i.e. attention) necessary for the task. As such, restoring some degree of attentional ability for individuals with PFC dysfunction may concomitantly restore willingness to exert mental effort. To test this, at least two methodologies could be used to induce prefrontal dysfunction: acetylcholine-specific lesions to the basal forebrain (if 8.2.2 proves correct); or a model of traumatic brain injury, already in use by the candidate’s supervisor’s laboratory, that can systematically induce mild, medium, or severe damage to the PFC, and of which preliminary data suggests accompanying respective levels of impairment on the 5CSRTT (Winstanley et al., personal communication). While no data directly supports the hypothesis of 8.2.5, at least two pieces of evidence indirectly support it. First, nicotine can restore performance/attention on the 5CSRTT following lesions to the cholinergic basal forebrain (Muir et al., 1995), but has minimal effect on healthy animals. Second, adenosine antagonists have no effect on decision making when administered alone, but 150  can restore choice behaviour following dopaminergic impairments of the NAc (Farrar et al., 2007; Mott et al., 2009; Worden et al., 2009).  8.3 Critical construct- and task-related considerations  8.3.1. The construct of “cognitive effort”. Psychological constructs such as intelligence, wisdom, attention, or effort are notoriously difficult to rigidly define (Sternberg, 2002). Coarsely, we have herein defined cognitive or mental effort as effort that is not physical in nature, i.e. based on voluntary energetic expenditure in the muscles. More specifically, we have chosen to manipulate attentional demand as one (of many possible) cognitive-effort processes. To reiterate, effort reflects a strain on limited resources due to the voluntary/conscious allocation of those resources to a target. The increased allocation of these resources to a target concomitantly increases 1) performance related to this target and 2) the subjective sense of effort (Norman and Bobrow, 1975). Attention fulfills all of the above criteria for one such cognitive effort cost. Research has long demonstrated that attentional resources are finite, in part consciously expended, and that increased allocation of attention induces the increased experiential feeling of exerting effort, as shown by both visceral responses (e.g. pupil dilation, SCR) and self-report (Kahneman, 1973). In short, individuals find that allocating selective, sustained attention (as modeled by the rCET) is effortful (Borgaro et al., 2003; Bearden et al., 2004; Smit et al., 2005). It should be noted that there are numerous cognitive resources (e.g. working memory) that could replace attention in a model of decision making with cognitive effort costs. It is reasonable to infer that these other costs would demonstrate overlapping-but-151  distinct neural circuitry with the attention costs currently used, and thus be valuable to the field in and of themselves. However, as previously discussed in the general introduction, high effort conditions, regardless of the type of effort, appear inevitably associated with greater attention focused on that target. As such, there appears to be a critical relationship between attention and mental effort expenditure. This link is underexplored and warrants future systematic examination.  Information-processing theories of cognitive effort have emphasized the role of increasing subcomponent processes as underlying increasing mental effort (Longo and Barrett, 2010). In this regard, they resemble theories of cognitive load, which can be understood as the information that is processed at a given moment by working memory (Paas et al., 2004). However, it is important not to conflate cognitive effort with cognitive load; load is but one form of effort, not the satisfying criterion. There are multiple ways in which a cognitive process can be effortful, and the rCET models the increasing mental strain that accompanies increasing attentional demands. It is reasonable to hypothesize that increased attentional demand is associated with increased processing, but this form of effort’s relationship to functional changes in neuronal activity has not yet been explored via methods with a high spatial and temporal resolution.  The experience of cognitive effort has previously been measured in humans using both self-report and increased decision latencies (Kahneman, 1973), suggesting that choice latency is a good correlate for the degree of effort an individual must exert. In the current data, choice latency is similar for all animals across both trial types, which at first appears to undermine the notion of the rCET measuring decision making with cognitive effort costs. It is important to note, however, that the effortful process of the rCET is not 152  in the decision itself (i.e. to choose an easy or hard trial) but rather in the engagement of attention following the choice, at the five-hole array. During the choice process in this task, the individual must weigh the associated costs and benefits of each option and then select the lever that designates the trial type they wish to perform; there is no reason that choosing HR should be any more complicated (i.e. require more information processing) than choosing LR in this respect, and as such the latencies are equivalent. For the nosepoke component of the task, however, the stimulus duration is much shorter for HR trials and requires animals to more quickly identify the illuminated stimulus; as such, it follows that response latencies for HR trials are faster than for LR trials. In other words, by using the rCET, we are not categorizing effort as simply doing more, but doing better.  8.3.2. Effects of amphetamine. Note that the effects of amphetamine were biphasic: amphetamine decreased workers’ choice of HR, and increased slackers’ choice of more effortful tasks. Thus, it could be argued that those who struggle to commit to more effortful challenges benefit from amphetamine administration. It is less clear that amphetamine and caffeine necessarily result in improved cognitive function in healthy subjects. A number of studies suggest an inverted-U shaped curve for neuromodulatory function and cognition: as all individuals would be pushed rightward on the curve in response to stimulants, those who sit to the left of the inverted-U may benefit, as they are pushed toward the centre of the curve, whereas individuals nearer to the centre would be pushed to the right edge of the curve. As such, there often appears an optimal level of neuromodulatory function for maximal cognitive function on a given task, and reaching that optimal level varies depending on the individual (Revelle et al., 1976; Revelle et al., 1980; Arnsten, 2009, 2011; Arnsten and Pliszka, 2011). In this respect, the current data 153  correspond very well: Figure 3.4 suggests that animals sit along a spectrum of neuromodulatory function, and that a rightward shift (via amphetamine) pushes slackers toward the centre of an inverted-U (as represented by a positive gradient of change) and pushes slackers to the right of the inverted-U (as represented by a negative gradient of change).  8.3.3. Individual differences in choice. All other factors being equal, individuals will avoid options that have a higher mental effort cost. The magnitude of this effort cost can be considered a subjective weighting of objective stimulus properties. As such, one may argue that the workers choose HR proportionately more often than slackers because workers find the attentional task easier. Measured performance (i.e. accuracy) is equal for workers and slackers for the task, but this may be the result of slackers putting more “effort” into HR trials; in some sense, then, one could argue that the slackers are actually harder working during a single HR trial.  To deal with the notion of difficulty first, we believe this argument is not in conflict with our interpretation in any way but semantics. When one refers to “difficulty” in this case, it suggests that the less difficult an individual finds the task, the less effort that individual needs to exert to obtain reward. As such, discussions of subjective difficulty are simply another way of framing effort expenditure. We instead prefer to use the term “difficulty” to refer to the objective stimulus properties, and “mental effort” to refer to the subjective sensation of the expenditure of a finite cognitive resource. On the rCET, identifying the 0.2s stimulus is more difficult than identifying the 1.0s stimulus; this is true for both workers and slackers, regardless of how effortful each individual finds the task, and is reflected in the accuracy data. Without an introspective measure (i.e. 154  self-report), using “difficulty” to describe the subjective sensation of difficulty becomes problematic.  As regards the notion of slackers actually being “workers” because they may put more effort into a single HR trial as compared to the workers, we do not find this interpretation to be ideal. First, while they may need to exert more effort on a per-trial basis, slackers actively avoid choosing as many HR trials as workers; the designation “slacker” refers to their actions across the session, their avoidance of effort, rather than their subjective state in a given trial. Second, it is not clear that slackers need to exert more effort on a per-trial basis, as we cannot infer mental states from anything other than behaviour in rats; it is impossible to say whether the task “feels” harder for the slackers. We can only point to the fact that the latencies to collect reward, respond to the lights, and the percent of omissions and correct responses are the same for slackers and workers. Based on these data, it is difficult to say that the slackers feel that the HR trials are more challenging. However, we can say that they appear just as competent, but do not choose the HR option as often as the workers. The reason why will always be subject to speculation. It is equally possible that slacker rats do not find the sensation of exerting cognitive effort as pleasurable, and therefore do not choose the lever as much, but again, we can only speculate about the subject at this time.  Related to this discussion, there is at least one paper that discusses decision making with cognitive effort costs in humans, and in it demonstrates that willingness to exert effort is related but dissociable from a self-report trait measurement of engagement and enjoyment of cognitively demanding activities. This paper also demonstrates a similarly related-but-dissociable relationship between willingness to exert effort and 155  ability to perform the task, suggesting that the willingness itself is a separate psychological construct from enjoyment or ability (Westbrook et al., 2013).  8.3.4. Relationship of choice on rCET to risk/probability. If choice behaviour was based on probability discounting, we would expect to see the same drug effects as in the yoked control task, wherein all effort costs are removed and only the risk (i.e. the reinforcement probabilities) remain, with each yoked-control animal’s HR delivered at a probability equal to their corresponding experimental animal’s accuracy. Hence, if a rat in the experimental group correctly responded to the 0.2s stimulus on 60% of trials, the corresponding rat in the yoked control group would simply receive 2 pellets on 60% of trials when it pressed the HR lever. However, we do not see the same drug effects between the experimental and control tasks; in fact, for amphetamine, we see a constant decrease in HR choice on the control task, versus the opposing effects on workers versus slackers in the experimental task. It is therefore possible that the reduction in preference for the HR option in workers could relate to risk aversion, but it cannot explain the improvement in the slackers. However, the effects of caffeine are very different: in the yoked-control task, there is no decrease in HR choice. Altogether, choice of HR does not seem to be guided simply by risk sensitivity.  8.4 Limitations  The rCET appears to successfully test one form of cost/benefit decision making. Specifically, it offers animals a choice between two highly sampled options with relatively well understood levels of cognitive effort, reward, and uncertainty (i.e. as dictated by animals’ accuracy) associated with each option, and these parameters do not 156  change over the course of a session. While this form of decision making is representative of some of the essential decisions we make in life, it is far from a complete model, even of decision making with mental effort costs.  For example, future studies must more directly examine the relationship between a given option’s cognitive effort costs and the animals’ subsequent behaviour. To that effect, the stimulus duration should be titrated in both directions, increasing the costs (when the stimulus duration is shortened) and decreasing the costs (when stimulus duration is longer), in order to demonstrate a spectrum of concomitant changes to animals’ choice. Other cost/benefit decision-making tasks take advantage of blocks within the session to manipulate the costs; for example, the high-reward option may become increasingly risky or increasingly delayed across the session. This ingenious method has allowed researchers to examine animals’ rates of discounting (Floresco et al., 2008c), i.e. the manner in which animals’ choice changes as a function of changing the costs. Individual differences in these discounting rates remain mostly unexamined in relationship to animals’ underlying neurobiology (although see Cocker et al., 2012b), and as such present an excellent opportunity for future research. It is currently unclear whether animals more sensitive to mental effort costs, i.e. slackers, would have steeper discounting rates or simply the same curve starting from a lower initial choice preference. Data from Experiment 2 would suggest the latter interpretation, but direct examination would be of great utility.  A second critical consideration is that of training time. While virtually all animals in all cohorts continued to sample from both levers (and thus both trial types), the lengthy training time presents potential problems in terms of behavioural flexibility. Indeed, most 157  significant choice effects of pharmacology or inactivation on choice behaviour were relatively modest in comparison to (for example) lesions on the physical effort T-maze task (Rudebeck et al., 2006a). As such, methods that shorten the training time may in turn increase the effects of pharmacology or inactivation. However, caution is urged as regards this point, as simply making the task easier (i.e. lengthening stimulus durations for both LR and HR options) could lead to steady upward drift in choice preferences, and also may not create a sufficient spread of individual differences for analysis. Another option would simply be to reverse the contingencies of the LR and HR levers following the establishment of baseline stability, thereby revealing any behavioural inflexibility (or lack thereof) in workers and/or slackers.  Related to the issue of training time, the rCET only allows for examination of behaviour once free-choice sessions commence. As such, no strong causal relationship can be inferred between individuals’ learning and subsequent baseline choice preferences. Many models of choice focus on the role of Bayesian learning, the updating of subjective values in response to accumulating evidence (Yu et al., 2009); such a role cannot currently be examined with the rCET. One hypothesis, then, is that workers and slackers demonstrate dissociable patterns of learning the task that are later masked by equivalent behaviour at baseline. Weak evidence in support of this hypothesis includes 1) the consistent finding that slackers score visually but not significantly below workers on task accuracy (which may in part explain nicotine’s beneficial effects on slackers’, but not workers’, attention/performance), and 2) slackers consistently score visually but not significantly higher than workers on premature responding. As the task is currently designed, forced-choice sessions ensure that animals continue to sample from both 158  levers/options until reaching training criteria, but an alternative approach may be to train animals to criteria first on the 5CSRTT before introducing the choice component of the rCET. Additionally, while animals have presumably learned task contingencies by the onset of free-choice sessions, a meta-analysis to examine all free-choice (pre-manipulation) behaviour in Experiments 1 through 5 will be performed in the future. This increased sample size and wider baseline duration may reveal subtle differences in (for example) animals’ underlying attentional abilities or motor impulsivity.  Returning to some of the topics from the General Introduction, future studies of decision making, both in humans and animal models, may need to address the traditionally ill-defined nature of navigating our day-to-day lives. Unlike tasks such as the rCET, our options are often not so explicitly defined, not dichotomous in nature, and not so temporally narrow. Instead, we must select from a large behavioural repertoire, often without knowing what actions are allowed, let alone beneficial, for a given situation. Tasks that take a more fluid approach to the decision-making process, wherein choices are not so narrowed in the number of options or by a specific window of time, may complement data from tasks such as the rCET (for example, see Daw et al., 2006). In any case, the best approach to studying decision making, be it with cognitive effort or any other cost, will likely be to take a converging evidence approach, taking advantage of a number of decision-making tasks as well as both human and animal studies.  As reported throughout this dissertation, a spectrum of choice behaviour was observed across each cohort, rather than two characteristically different types of choice behaviour. The worker/slacker distinction was thus a convenient statistical shorthand for examining individual differences in choice, but another option for future research may be 159  to plot baseline choice behaviour against the effect of experimental manipulations. Examining choice as a gradient rather than categorically may necessitate larger sample sizes than the current studies but also directly demonstrate the curve of the function between choice and its associated neurobiology.  Finally, we have developed and validated an animal model of cost/benefit decision making with cognitive effort costs, specifically utilizing sustained attention. However, the term “cognitive” is quite broad and it is easy to imagine that other cognitive costs, such as working memory or task switching, could be substituted with attention in a decision-making task; indeed, at least one human task uses other cognitive costs (Kool et al., 2010). How this could be meaningfully applied to an animal model, and how significantly the neurobiology would diverge such a task versus the rCET, are opportunities for future research.    8.4 Concluding remarks  Here we have shown a direct way to test decision making with mental effort costs in rats, as well as a number of ways in which to perturb this decision-making circuitry and neurochemistry. These results demonstrate that the rCET has the potential to both elucidate the mechanisms underlying choice in healthy individuals, and suggest novel therapeutic interventions for individuals whose decision making has been compromised by any of a number of disease states.  160  Table 8.1. Summary of all pharmacology and inactivation results, Experiments 1-5  Amph: amphetamine; Caff: caffeine; Etic: eticlopride; SCH: SCH23390; Yoh: yohimbine; Atx: atomoxetine; Nic: nicotine; Mec: mecamylamine; Sco: scopolamine; Oxo: oxotremorine; BLAi: basolateral amygdala inactivation; ACCi; anterior cingulate cortex inactivation; PLi: prelimbic cortex inactivation; ILi: infralimbic cortex inactivation.	 Amph	 Alcohol	 Caff	 Etic	 SCH	 Yoh	 Atx	 Nic	 Mec	 Sco	 Oxo	 BLAi	 ACCi	 PLi	 ILi	HR Choice workers	ê	slackers	é NE	 workers	ê NE	 NE	 NE	 NE	 slackers	ê NE	 ê NE	 workers	ê	slackers	é ê ê ê Accuracy NE	 NE	 NE	 NE	 NE	 all	ê	 NE	 slackers	HR	é LR	ê NE	 NE	 NE	 NE	 ê NE	Premature responding é NE	 é NE	 NE	 NE	 NE	 é NE	 NE	 ê NE	 é NE	 é Choice latency NE	 ê LR	ê NE	 é ê	 all	é NE	 é inverted-U	 é é NE	 NE	 NE	Correct latency NE	 ê NE	 é NE	 ê	 NE	 NE	 NE	 é NE	 é é	 é	 é	Collection latency NE	 NE	 NE	 NE	 NE	 ê	 NE	 NE	 NE	 NE	 NE	 NE	 NE	 é	 é	Response omissions HR	é,	LR	ê LR	ê	 é é é U	 NE	 ê NE	 é é	 NE	 é	 é é Choice omissions é NE	 NE	 é NE	 é é é é	 é é	 é	 é	 NE	 NE	Completed trials ê inverted-U ê ê ê inverted-U ê ê ê ê ê ê ê ê ê 	161  Bibliography Ambroggi F, Ishikawa A, Fields HL, Nicola SM (2008) Basolateral amygdala neurons facilitate reward-seeking behavior by exciting nucleus accumbens neurons. Neuron 59:648-661. Anderson KJ, Revelle W (1994) Impulsivity and time of day: is rate of change in arousal a function of impulsivity? J Pers Soc Psychol 67:334-344. Arnold HM, Fadel J, Sarter M, Bruno JP (2001) Amphetamine-stimulated cortical acetylcholine release: role of the basal forebrain. Brain Res 894:74-87. Arnsten AF (2009) Stress signalling pathways that impair prefrontal cortex structure and function. Nat Rev Neurosci 10:410-422. Arnsten AF (2011) Prefrontal cortical network connections: key site of vulnerability in stress and schizophrenia. International journal of developmental neuroscience : the official journal of the International Society for Developmental Neuroscience 29:215-223. Arnsten AF, Pliszka SR (2011) Catecholamine influences on prefrontal cortical function: relevance to treatment of attention deficit/hyperactivity disorder and related disorders. Pharmacol Biochem Behav 99:211-216. Baarendse PJ, Winstanley CA, Vanderschuren LJ (2013) Simultaneous blockade of dopamine and noradrenaline reuptake promotes disadvantageous decision making in a rat gambling task. Psychopharmacology (Berl) 225:719-731. Barch DM, Treadway MT, Schoen N (2014) Effort, anhedonia, and function in schizophrenia: Reduced effort allocation predicts amotivation and functional impairment. J Abnorm Psychol 123:387-397. Bardgett ME, Depenbrock M, Downs N, Points M, Green L (2009) Dopamine modulates effort-based decision making in rats. Behav Neurosci 123:242-251. Barnes A, Bullmore ET, Suckling J (2009) Endogenous human brain dynamics recover slowly following cognitive effort. PLoS ONE 4:e6626. Bartra O, McGuire JT, Kable JW (2013) The valuation system: a coordinate-based meta-analysis of BOLD fMRI experiments examining neural correlates of subjective value. Neuroimage 76:412-427. Basten U, Biele G, Heekeren HR, Fiebach CJ (2010) How the brain integrates costs and benefits during decision making. Proc Natl Acad Sci U S A 107:21767-21772. Bearden TS, Cassisi JE, White JN (2004) Electrophysiological correlates of vigilance during a continuous performance test in healthy adults. Appl Psychophysiol Biofeedback 29:175-188. Bechara A, Damasio H, Damasio AR, Lee GP (1999a) Different contributions of the human amygdala and ventromedial prefrontal cortex to decision-making. J Neurosci 19:5473-5481. Bechara A, Damasio H, Damasio AR, Lee GP (1999b) Different contributions of the human amygdala and ventromedial prefrontal cortex to decision-making. Journal of Neuroscience 19:5473-5481. Bell DE, Raiffa H, Tversky A (1988) Decision making : descriptive, normative, and prescriptive interactions. Cambridge ; New York: Cambridge University Press. Belova MA, Paton JJ, Salzman CD (2008) Moment-to-moment tracking of state value in the amygdala. J Neurosci 28:10023-10030. 162  Belova MA, Paton JJ, Morrison SE, Salzman CD (2007) Expectation modulates neural responses to pleasant and aversive stimuli in primate amygdala. Neuron 55:970-984. Bentley P, Vuilleumier P, Thiel CM, Driver J, Dolan RJ (2003) Cholinergic enhancement modulates neural correlates of selective attention and emotional processing. Neuroimage 20:58-70. Bizarro L, Patel S, Stolerman IP (2003) Comprehensive deficits in performance of an attentional task produced by co-administering alcohol and nicotine to rats. Drug Alcohol Depend 72:287-295. Bizarro L, Patel S, Murtagh C, Stolerman IP (2004) Differential effects of psychomotor stimulants on attentional performance in rats: nicotine, amphetamine, caffeine and methylphenidate. Behav Pharmacol 15:195-206. Boorman ED, Rushworth MF, Behrens TE (2013) Ventromedial Prefrontal and Anterior Cingulate Cortex Adopt Choice and Default Reference Frames during Sequential Multi-Alternative Choice. J Neurosci 33:2242-2253. Borgaro S, Pogge DL, DeLuca VA, Bilginer L, Stokes J, Harvey PD (2003) Convergence of different versions of the continuous performance test: clinical and scientific implications. J Clin Exp Neuropsychol 25:283-292. Botvinick MM, Huffstetler S, McGuire JT (2009) Effort discounting in human nucleus accumbens. Cogn Affect Behav Neurosci 9:16-27. Bouton ME, Woods AM, Todd TP (2014) Separation of time-based and trial-based accounts of the partial reinforcement extinction effect. Behav Processes 101:23-31. Bracco L, Bessi V, Padiglioni S, Marini S, Pepeu G (2014) Do cholinesterase inhibitors act primarily on attention deficit? A naturalistic study in Alzheimer's disease patients. Journal of Alzheimer's disease : JAD 40:737-742. Brancucci A, Tommasi L (2011) "Binaural rivalry": dichotic listening as a tool for the investigation of the neural correlate of consciousness. Brain Cogn 76:218-224. Brand M, Grabenhorst F, Starcke K, Vandekerckhove MM, Markowitsch HJ (2007) Role of the amygdala in decisions under ambiguity and decisions under risk: evidence from patients with Urbach-Wiethe disease. Neuropsychologia 45:1305-1317. Brase GL (2014) The nature of thinking, shallow and deep. Front Psychol 5:435. Broersen LM, Uylings HB (1999) Visual attention task performance in Wistar and Lister hooded rats: response inhibition deficits after medial prefrontal cortex lesions. Neuroscience 94:47-57. Brooks P, Zank H (2005) Loss averse behavior. Journal of Risk and Uncertainty 31:301-325. Bruno JP, Gash C, Martin B, Zmarowski A, Pomerleau F, Burmeister J, Huettl P, Gerhardt GA (2006) Second-by-second measurement of acetylcholine release in prefrontal cortex. Eur J Neurosci 24:2749-2757. Brunye TT, Mahoney CR, Lieberman HR, Taylor HA (2010) Caffeine modulates attention network function. Brain Cogn 72:181-188. Bucci DJ, Holland PC, Gallagher M (1998) Removal of cholinergic input to rat posterior parietal cortex disrupts incremental processing of conditioned stimuli. J Neurosci 18:8038-8046. 163  Burgos-Robles A, Bravo-Rivera H, Quirk GJ (2013) Prelimbic and infralimbic neurons signal distinct aspects of appetitive instrumental behavior. PLoS One 8:e57575. Buschman TJ, Miller EK (2007) Top-down versus bottom-up control of attention in the prefrontal and posterior parietal cortices. Science 315:1860-1862. Byrnes JP, Miller DC, Schafer WD (1999) Gender differences in risk taking: A meta-analysis. Psychological bulletin 125:367. Caceda R, Nemeroff CB, Harvey PD (2014) Toward an Understanding of Decision Making in Severe Mental Illness. J Neuropsychiatry Clin Neurosci. Cai X, Padoa-Schioppa C (2012) Neuronal encoding of subjective value in dorsal and ventral anterior cingulate cortex. J Neurosci 32:3791-3808. Cardinal RN, Aitken MRF (2006) ANOVA for the behavioural sciences researcher. Mahwah, N.J.: L. Erlbaum. Cardinal RN, Parkinson JA, Hall J, Everitt BJ (2002) Emotion and motivation: the role of the amygdala, ventral striatum, and prefrontal cortex. Neurosci Biobehav Rev 26:321-352. Carli M, Robbins TW, Evenden JL, Everitt BJ (1983) Effects of lesions to ascending noradrenergic neurones on performance of a 5-choice serial reaction task in rats; implications for theories of dorsal noradrenergic bundle function based on selective attention and arousal. Behav Brain Res 9:361-380. Cavanna AE, Trimble MR (2006) The precuneus: a review of its functional anatomy and behavioural correlates. Brain 129:564-583. Christakou A, Gershman SJ, Niv Y, Simmons A, Brammer M, Rubia K (2013) Neural and psychological maturation of decision-making in adolescence and young adulthood. J Cogn Neurosci 25:1807-1823. Chudasama Y, Dalley JW, Nathwani F, Bouger P, Robbins TW (2004) Cholinergic modulation of visual attention and working memory: dissociable effects of basal forebrain 192-IgG-saporin lesions and intraprefrontal infusions of scopolamine. Learn Mem 11:78-86. Chudasama Y, Passetti F, Rhodes SE, Lopian D, Desai A, Robbins TW (2003) Dissociable aspects of performance on the 5-choice serial reaction time task following lesions of the dorsal anterior cingulate, infralimbic and orbitofrontal cortex in the rat: differential effects on selectivity, impulsivity and compulsivity. Behav Brain Res 146:105-119. Claassen DO, van den Wildenberg WP, Ridderinkhof KR, Jessup CK, Harrison MB, Wooten GF, Wylie SA (2011) The risky business of dopamine agonists in Parkinson disease and impulse control disorders. Behav Neurosci 125:492-500. Cocker PJ, Hosking JG, Benoit J, Winstanley CA (2012a) Sensitivity to Cognitive Effort Mediates Psychostimulant Effects on a Novel Rodent Cost/Benefit Decision-Making Task. Neuropsychopharmacology. Cocker PJ, Dinelle K, Kornelson R, Sossi V, Winstanley CA (2012b) Irrational choice under uncertainty correlates with lower striatal D(2/3) receptor binding in rats. J Neurosci 32:15450-15457. Cohen RM, Weingartner H, Smallberg SA, Pickar D, Murphy DL (1982) Effort and cognition in depression. Arch Gen Psychiatry 39:593-597. Cole BJ, Robbins TW (1987) Amphetamine impairs the discriminative performance of rats with dorsal noradrenergic bundle lesions on a 5-choice serial reaction time 164  task: new evidence for central dopaminergic-noradrenergic interactions. Psychopharmacology (Berl) 91:458-466. Cole BJ, Robbins TW (1989) Effects of 6-hydroxydopamine lesions of the nucleus accumbens septi on performance of a 5-choice serial reaction time task in rats: implications for theories of selective attention and arousal. Behav Brain Res 33:165-179. Collins LE, Sager TN, Sams AG, Pennarola A, Port RG, Shahriari M, Salamone JD (2011) The novel adenosine A(2A) antagonist Lu AA47070 reverses the motor and motivational effects produced by dopamine D2 receptor blockade. Pharmacol Biochem Behav. Cools R, Clark L, Robbins TW (2004) Differential responses in human striatum and prefrontal cortex to changes in object and rule relevance. J Neurosci 24:1129-1135. Cousins MS, Wei W, Salamone JD (1994) Pharmacological characterization of performance on a concurrent lever pressing/feeding choice procedure: effects of dopamine antagonist, cholinomimetic, sedative and stimulant drugs. Psychopharmacology (Berl) 116:529-537. Cousins MS, Atherton A, Turner L, Salamone JD (1996) Nucleus accumbens dopamine depletions alter relative response allocation in a T-maze cost/benefit task. Behav Brain Res 74:189-197. Croxson PL, Walton ME, O'Reilly JX, Behrens TE, Rushworth MF (2009) Effort-based cost-benefit valuation and the human brain. J Neurosci 29:4531-4541. Dalley JW, Mar AC, Economidou D, Robbins TW (2008) Neurobehavioral mechanisms of impulsivity: fronto-striatal systems and functional neurochemistry. Pharmacol Biochem Behav 90:250-260. Dalley JW, McGaughy J, O'Connell MT, Cardinal RN, Levita L, Robbins TW (2001) Distinct changes in cortical acetylcholine and noradrenaline efflux during contingent and noncontingent performance of a visual attentional task. J Neurosci 21:4908-4914. Dalley JW, Theobald DE, Bouger P, Chudasama Y, Cardinal RN, Robbins TW (2004) Cortical cholinergic function and deficits in visual attentional performance in rats following 192 IgG-saporin-induced lesions of the medial prefrontal cortex. Cereb Cortex 14:922-932. Damasio AR (2005) Descartes' error : emotion, reason, and the human brain. London: Penguin. Damiano CR, Aloi J, Treadway M, Bodfish JW, Dichter GS (2012) Adults with autism spectrum disorders exhibit decreased sensitivity to reward parameters when making effort-based decisions. J Neurodev Disord 4:13. Dani JA, Bertrand D (2007) Nicotinic acetylcholine receptors and nicotinic cholinergic mechanisms of the central nervous system. Annu Rev Pharmacol Toxicol 47:699-729. Daw ND, O'Doherty JP, Dayan P, Seymour B, Dolan RJ (2006) Cortical substrates for exploratory decisions in humans. Nature 441:876-879. Day JC, Tham CS, Fibiger HC (1994) Dopamine depletion attenuates amphetamine-induced increases of cortical acetylcholine release. Eur J Pharmacol 263:285-292. 165  de Visser L, van der Knaap LJ, van de Loo AJ, van der Weerd CM, Ohl F, van den Bos R (2010) Trait anxiety affects decision-making differently in healthy men and women: towards gender-specific endophenotypes of anxiety. Neuropsychologia 48:1598-1606. de Wit H, Crean J, Richards JB (2000) Effects of d-amphetamine and ethanol on a measure of behavioral inhibition in humans. Behav Neurosci 114:830-837. de Wit H, Enggasser JL, Richards JB (2002) Acute administration of d-amphetamine decreases impulsivity in healthy volunteers. Neuropsychopharmacology 27:813-825. Denk F, Walton ME, Jennings KA, Sharp T, Rushworth MF, Bannerman DM (2005) Differential involvement of serotonin and dopamine systems in cost-benefit decisions about delay or effort. Psychopharmacology (Berl) 179:587-596. Diamond A (2011) Biological and social influences on cognitive control processes dependent on prefrontal cortex. Prog Brain Res 189:319-339. Dixon MR, Marley J, Jacobs EA (2003) Delay discounting by pathological gamblers. J Appl Behav Anal 36:449-458. Dolan RJ (2012) Neuroscience of preference and choice : cognitive and neural mechanisms, 1st Edition. London ; Waltham, MA: Academic Press/Elsevier. Drummer OH, Gerostamoulos J, Batziris H, Chu M, Caplehorn JR, Robertson MD, Swann P (2003) The incidence of drugs in drivers killed in Australian road traffic crashes. Forensic Sci Int 134:154-162. Egeland J, Johansen SN, Ueland T (2010) Do low-effort learning strategies mediate impaired memory in ADHD? J Learn Disabil 43:430-440. Endepols H, Sommer S, Backes H, Wiedermann D, Graf R, Hauber W (2010) Effort-based decision making in the rat: an [18F]fluorodeoxyglucose micro positron emission tomography study. J Neurosci 30:9708-9714. Eppinger B, Walter M, Heekeren HR, Li SC (2013) Of goals and habits: age-related and individual differences in goal-directed decision-making. Front Neurosci 7:253. Everitt BJ, Cardinal RN, Parkinson JA, Robbins TW (2003) Appetitive behavior: impact of amygdala-dependent mechanisms of emotional learning. Ann N Y Acad Sci 985:233-250. Fadel JR (2011) Regulation of cortical acetylcholine release: insights from in vivo microdialysis studies. Behav Brain Res 221:527-536. Farrar AM, Pereira M, Velasco F, Hockemeyer J, Muller CE, Salamone JD (2007) Adenosine A(2A) receptor antagonism reverses the effects of dopamine receptor antagonism on instrumental output and effort-related choice in the rat: implications for studies of psychomotor slowing. Psychopharmacology (Berl) 191:579-586. Faumont S, Lindsay TH, Lockery SR (2012) Neuronal microcircuits for decision making in C. elegans. Curr Opin Neurobiol 22:580-591. Fellows LK, Farah MJ (2005) Different underlying impairments in decision-making following ventromedial and dorsolateral frontal lobe damage in humans. Cereb Cortex 15:58-63. Fibiger W, Singer G, Miller AJ (1984) Relationships between catecholamines in urine and physical and mental effort. Int J Psychophysiol 1:325-333. 166  Field M, Wiers RW, Christiansen P, Fillmore MT, Verster JC (2010) Acute alcohol effects on inhibitory control and implicit cognition: implications for loss of control over drinking. Alcohol Clin Exp Res 34:1346-1352. Fink JS, Weaver DR, Rivkees SA, Peterfreund RA, Pollack AE, Adler EM, Reppert SM (1992) Molecular cloning of the rat A2 adenosine receptor: selective co-expression with D2 dopamine receptors in rat striatum. Brain Res Mol Brain Res 14:186-195. FitzGerald TH, Schwartenbeck P, Dolan RJ (2014) Reward-related activity in ventral striatum is action contingent and modulated by behavioral relevance. J Neurosci 34:1271-1279. Floresco SB, Ghods-Sharifi S (2007a) Amygdala-prefrontal cortical circuitry regulates effort-based decision making. Cerebral Cortex 17:251-260. Floresco SB, Ghods-Sharifi S (2007b) Amygdala-prefrontal cortical circuitry regulates effort-based decision making. Cereb Cortex 17:251-260. Floresco SB, Tse MT, Ghods-Sharifi S (2008a) Dopaminergic and glutamatergic regulation of effort- and delay-based decision making. Neuropsychopharmacology 33:1966-1979. Floresco SB, Tse MTL, Ghods-Sharifi S (2008b) Dopaminergic and glutamatergic regulation of effort- and delay-based decision making. Neuropsychopharmacology 33:1966-1979. Floresco SB, Ghods-Sharifi S, Vexelman C, Magyar O (2006) Dissociable roles for the nucleus accumbens core and shell in regulating set shifting. J Neurosci 26:2449-2457. Floresco SB, St Onge JR, Ghods-Sharifi S, Winstanley CA (2008c) Cortico-limbic-striatal circuits subserving different forms of cost-benefit decision making. Cogn Affect Behav Neurosci 8:375-389. Fobbs WC, Mizumori SJ (2014) Cost-benefit decision circuitry: proposed modulatory role for acetylcholine. Progress in molecular biology and translational science 122:233-261. Freeze BS, Kravitz AV, Hammack N, Berke JD, Kreitzer AC (2013) Control of basal ganglia output by direct and indirect pathway projection neurons. J Neurosci 33:18531-18539. Ghods-Sharifi S, Floresco SB (2010) Differential effects on effort discounting induced by inactivations of the nucleus accumbens core or shell. Behav Neurosci 124:179-191. Ghods-Sharifi S, St Onge JR, Floresco SB (2009) Fundamental contribution by the basolateral amygdala to different forms of decision making. J Neurosci 29:5251-5259. Gigerenzer G, Selten R (2001) Bounded rationality : the adaptive toolbox. Cambridge, Mass.: MIT Press. Givens B (1997) Effect of ethanol on sustained attention in rats. Psychopharmacology (Berl) 129:135-140. Gleichgerrcht E, Ibanez A, Roca M, Torralva T, Manes F (2010) Decision-making cognition in neurodegenerative diseases. Nat Rev Neurol 6:611-623. Goard M, Dan Y (2009) Basal forebrain activation enhances cortical coding of natural scenes. Nat Neurosci 12:1444-1449. 167  Godefroy O, Rousseaux M (1996) Divided and focused attention in patients with lesion of the prefrontal cortex. Brain Cogn 30:155-174. Gold JM, Strauss GP, Waltz JA, Robinson BM, Brown JK, Frank MJ (2013a) Negative symptoms of schizophrenia are associated with abnormal effort-cost computations. Biol Psychiatry 74:130-136. Gold JM, Strauss GP, Waltz JA, Robinson BM, Brown JK, Frank MJ (2013b) Negative Symptoms of Schizophrenia Are Associated with Abnormal Effort-Cost Computations. Biol Psychiatry. Gold JM, Kool W, Botvinick MM, Hubzin L, August S, Waltz JA (2014) Cognitive effort avoidance and detection in people with schizophrenia. Cogn Affect Behav Neurosci. Goschke T (2014) Dysfunctions of decision-making and cognitive control as transdiagnostic mechanisms of mental disorders: advances, gaps, and needs in current research. International journal of methods in psychiatric research 23 Suppl 1:41-57. Grace AA, Floresco SB, Goto Y, Lodge DJ (2007) Regulation of firing of dopaminergic neurons and control of goal-directed behaviors. Trends Neurosci 30:220-227. Granon S, Hardouin J, Courtier A, Poucet B (1998) Evidence for the involvement of the rat prefrontal cortex in sustained attention. The Quarterly journal of experimental psychology B, Comparative and physiological psychology 51:219-233. Guthrie M, Leblois A, Garenne A, Boraud T (2013) Interaction between cognitive and motor cortico-basal ganglia loops during decision making: a computational study. J Neurophysiol 109:3025-3040. Hahn B, Shoaib M, Stolerman IP (2002) Nicotine-induced enhancement of attention in the five-choice serial reaction time task: the influence of task demands. Psychopharmacology (Berl) 162:129-137. Hahn B, Ross TJ, Wolkenberg FA, Shakleya DM, Huestis MA, Stein EA (2009) Performance effects of nicotine during selective attention, divided attention, and simple stimulus detection: an fMRI study. Cereb Cortex 19:1990-2000. Hammar A, Lund A, Hugdahl K (2003) Selective impairment in effortful information processing in major depression. J Int Neuropsychol Soc 9:954-959. Hammar A, Strand M, Ardal G, Schmid M, Lund A, Elliott R (2011) Testing the cognitive effort hypothesis of cognitive impairment in major depression. Nord J Psychiatry 65:74-80. Harrell PT, Juliano LM (2012) A direct test of the influence of nicotine response expectancies on the subjective and cognitive effects of smoking. Exp Clin Psychopharmacol 20:278-286. Hauber W, Sommer S (2009) Prefrontostriatal circuitry regulates effort-related decision making. Cereb Cortex 19:2240-2247. Haushofer J, Fehr E (2014) On the psychology of poverty. Science 344:862-867. Heishman SJ, Kleykamp BA, Singleton EG (2010) Meta-analysis of the acute effects of nicotine and smoking on human performance. Psychopharmacology (Berl) 210:453-469. Herrnstein RJ (1970) On the law of effect. J Exp Anal Behav 13:243-266. Hess EH, Polt JM (1964) Pupil Size in Relation to Mental Activity during Simple Problem-Solving. Science 143:1190-1192. 168  Higgins GA, Grzelak ME, Pond AJ, Cohen-Williams ME, Hodgson RA, Varty GB (2007) The effect of caffeine to increase reaction time in the rat during a test of attention is mediated through antagonism of adenosine A2A receptors. Behav Brain Res 185:32-42. Himmelheber AM, Sarter M, Bruno JP (2000) Increases in cortical acetylcholine release during sustained attention performance in rats. Brain Res Cogn Brain Res 9:313-325. Holdstock L, King AC, de Wit H (2000) Subjective and objective responses to ethanol in moderate/heavy and light social drinkers. Alcohol Clin Exp Res 24:789-794. Hong LE, Schroeder M, Ross TJ, Buchholz B, Salmeron BJ, Wonodi I, Thaker GK, Stein EA (2011) Nicotine enhances but does not normalize visual sustained attention and the associated brain network in schizophrenia. Schizophr Bull 37:416-425. Hosking JG, Cocker PJ, Winstanley CA (2014) Dissociable contributions of anterior cingulate cortex and basolateral amygdala on a rodent cost/benefit decision-making task of cognitive effort. Neuropsychopharmacology 39:1558-1567. Huang ZL, Qu WM, Eguchi N, Chen JF, Schwarzschild MA, Fredholm BB, Urade Y, Hayaishi O (2005) Adenosine A2A, but not A1, receptors mediate the arousal effect of caffeine. Nat Neurosci 8:858-859. Hubel DH, Wiesel TN (1998) Early exploration of the visual cortex. Neuron 20:401-412. Hyman JM, Ma L, Balaguer-Ballester E, Durstewitz D, Seamans JK (2012) Contextual encoding by ensembles of medial prefrontal cortex neurons. Proc Natl Acad Sci U S A 109:5086-5091. Ishikawa A, Ambroggi F, Nicola SM, Fields HL (2008) Contributions of the amygdala and medial prefrontal cortex to incentive cue responding. Neuroscience 155:573-584. Izuma K, Matsumoto M, Murayama K, Samejima K, Sadato N, Matsumoto K (2010) Neural correlates of cognitive dissonance and choice-induced preference change. Proc Natl Acad Sci U S A 107:22014-22019. Janes AC, Jensen JE, Farmer SL, Frederick BD, Pizzagalli DA, Lukas SE (2013) GABA Levels in The Dorsal Anterior Cingulate Cortex Associated with Difficulty Ignoring Smoking-Related Cues in Tobacco-Dependent Volunteers. Neuropsychopharmacology. Jansen J, Jr., Andersen P, Kaada BR (1955) Subcortical mechanisms in the searching or attention response elicited by prefrontal cortical stimulation in unanesthetized cats. The Yale journal of biology and medicine 28:331-341. Jenison RL, Rangel A, Oya H, Kawasaki H, Howard MA (2011) Value encoding in single neurons in the human amygdala during decision making. J Neurosci 31:331-338. Jimura K, Chushak MS, Braver TS (2013) Impulsivity and self-control during intertemporal decision making linked to the neural dynamics of reward value representation. J Neurosci 33:344-357. Jones-Cage C, Stratford TR, Wirtshafter D (2011) Differential effects of the adenosine A(2A) agonist CGS-21680 and haloperidol on food-reinforced fixed ratio responding in the rat. Psychopharmacology (Berl). Jones DN, Higgins GA (1995) Effect of scopolamine on visual attention in rats. Psychopharmacology (Berl) 120:142-149. 169  Kable JW, Glimcher PW (2007) The neural correlates of subjective value during intertemporal choice. Nat Neurosci 10:1625-1633. Kahneman D (1973) Attention and effort. Englewood Cliffs, N.J.,: Prentice-Hall. Kahneman D, Tversky A (1979) Prospect theory: An analysis of decision under risk. Econometrica: Journal of the Econometric Society:263-291. Kilpatrick MR, Rooney MB, Michael DJ, Wightman RM (2000) Extracellular dopamine dynamics in rat caudate-putamen during experimenter-delivered and intracranial self-stimulation. Neuroscience 96:697-706. Kimchi EY, Laubach M (2009) Dynamic encoding of action selection by the medial striatum. J Neurosci 29:3148-3159. Kirby KN, Marakovic NN (1996) Delay-discounting probabilistic rewards: Rates decrease as amounts increase. Psychon Bull Rev 3:100-104. Kitzbichler MG, Henson RN, Smith ML, Nathan PJ, Bullmore ET (2011) Cognitive effort drives workspace configuration of human brain functional networks. J Neurosci 31:8259-8270. Klinkenberg I, Sambeth A, Blokland A (2011) Acetylcholine and attention. Behav Brain Res 221:430-442. Kool W, McGuire JT, Rosen ZB, Botvinick MM (2010) Decision making and the avoidance of cognitive demand. J Exp Psychol Gen 139:665-682. Kool W, McGuire JT, Wang GJ, Botvinick MM (2013) Neural and behavioral evidence for an intrinsic cost of self-control. PLoS One 8:e72626. Kravitz AV, Tye LD, Kreitzer AC (2012) Distinct roles for direct and indirect pathway striatal neurons in reinforcement. Nat Neurosci 15:816-818. Kuhl DE, Minoshima S, Fessler JA, Frey KA, Foster NL, Ficaro EP, Wieland DM, Koeppe RA (1996) In vivo mapping of cholinergic terminals in normal aging, Alzheimer's disease, and Parkinson's disease. Ann Neurol 40:399-410. Kurniawan IT, Guitart-Masip M, Dolan RJ (2011) Dopamine and effort-based decision making. Front Neurosci 5:81. Kurzban R, Duckworth A, Kable JW, Myers J (2013) An opportunity cost model of subjective effort and task performance. The Behavioral and brain sciences 36:661-679. Lak A, Stauffer WR, Schultz W (2014) Dopamine prediction error responses integrate subjective value from different reward dimensions. Proc Natl Acad Sci U S A 111:2343-2348. Lawrie SM, MacHale SM, Power MJ, Goodwin GM (1997) Is the chronic fatigue syndrome best understood as a primary disturbance of the sense of effort? Psychol Med 27:995-999. Lazarus M, Shen HY, Cherasse Y, Qu WM, Huang ZL, Bass CE, Winsky-Sommerer R, Semba K, Fredholm BB, Boison D, Hayaishi O, Urade Y, Chen JF (2011) Arousal effect of caffeine depends on adenosine A2A receptors in the shell of the nucleus accumbens. J Neurosci 31:10067-10075. Le Berre AP, Rauchs G, La Joie R, Mezenge F, Boudehent C, Vabret F, Segobin S, Viader F, Allain P, Eustache F, Pitel AL, Beaunieux H (2012) Impaired decision-making and brain shrinkage in alcoholism. Eur Psychiatry. Longo L, Barrett S (2010) A computational analysis of cognitive effort. In: Intelligent Information and Database Systems, pp 65-74: Springer. 170  Luk CH, Wallis JD (2013) Choice coding in frontal cortex during stimulus-guided or action-guided decision-making. J Neurosci 33:1864-1871. Mackowick KM, Barr MS, Wing VC, Rabin RA, Ouellet-Plamondon C, George TP (2014) Neurocognitive endophenotypes in schizophrenia: modulation by nicotinic receptor systems. Prog Neuropsychopharmacol Biol Psychiatry 52:79-85. Mandel RJ, Leanza G, Nilsson OG, Rosengren E (1994) Amphetamine induces excess release of striatal acetylcholine in vivo that is independent of nigrostriatal dopamine. Brain Res 653:57-65. Manes F, Sahakian B, Clark L, Rogers R, Antoun N, Aitken M, Robbins T (2002) Decision-making processes following damage to the prefrontal cortex. Brain 125:624-639. Mannie ZN, Williams C, Browning M, Cowen PJ (2014) Decision making in young people at familial risk of depression. Psychol Med:1-6. Marquis JP, Killcross S, Haddon JE (2007) Inactivation of the prelimbic, but not infralimbic, prefrontal cortex impairs the contextual control of response conflict in rats. Eur J Neurosci 25:559-566. Mattay VS, Goldberg TE, Fera F, Hariri AR, Tessitore A, Egan MF, Kolachana B, Callicott JH, Weinberger DR (2003) Catechol O-methyltransferase val158-met genotype and individual variation in the brain response to amphetamine. Proc Natl Acad Sci U S A 100:6186-6191. McGaughy J, Dalley JW, Morrison CH, Everitt BJ, Robbins TW (2002) Selective behavioral and neurochemical effects of cholinergic lesions produced by intrabasalis infusions of 192 IgG-saporin on attentional performance in a five-choice serial reaction time task. J Neurosci 22:1905-1913. McGuire JT, Botvinick MM (2010) Prefrontal cortex, cognitive control, and the registration of decision costs. Proc Natl Acad Sci U S A 107:7922-7926. McGuire JT, Kable JW (2013) Rational temporal predictions can underlie apparent failures to delay gratification. Psychol Rev 120:395-410. Mendez IA, Gilbert RJ, Bizon JL, Setlow B (2012) Effects of acute administration of nicotinic and muscarinic cholinergic agonists and antagonists on performance in different cost-benefit decision making tasks in rats. Psychopharmacology (Berl) 224:489-499. Mendez IA, Damborsky JC, Winzer-Serhan UH, Bizon JL, Setlow B (2013) Alpha4beta2 and alpha7 nicotinic acetylcholine receptor binding predicts choice preference in two cost benefit decision-making tasks. Neuroscience 230:121-131. Miller EK, Cohen JD (2001) An integrative theory of prefrontal cortex function. Annu Rev Neurosci 24:167-202. Mirza NR, Stolerman IP (1998) Nicotine enhances sustained attention in the rat under specific task conditions. Psychopharmacology (Berl) 138:266-274. Mirza NR, Stolerman IP (2000) The role of nicotinic and muscarinic acetylcholine receptors in attention. Psychopharmacology (Berl) 148:243-250. Mirza NR, Bright JL (2001) Nicotine-induced enhancements in the five-choice serial reaction time task in rats are strain-dependent. Psychopharmacology (Berl) 154:8-12. Mischel W, Shoda Y, Rodriguez MI (1989) Delay of gratification in children. Science 244:933-938. 171  Mischel W, Ayduk O, Berman MG, Casey BJ, Gotlib IH, Jonides J, Kross E, Teslovich T, Wilson NL, Zayas V, Shoda Y (2011) 'Willpower' over the life span: decomposing self-regulation. Social cognitive and affective neuroscience 6:252-256. Mitchell MR, Vokes CM, Blankenship AL, Simon NW, Setlow B (2011) Effects of acute administration of nicotine, amphetamine, diazepam, morphine, and ethanol on risky decision-making in rats. Psychopharmacology (Berl). Morrison SE, Salzman CD (2010) Re-valuing the amygdala. Curr Opin Neurobiol 20:221-230. Morsella E, Feinberg GH, Cigarchi S, Newton JW, Williams LE (2011) Sources of avoidance motivation: Valence effects from physical effort and mental rotation. Motivation and emotion 35:296-305. Mott AM, Nunes EJ, Collins LE, Port RG, Sink KS, Hockemeyer J, Muller CE, Salamone JD (2009) The adenosine A2A antagonist MSX-3 reverses the effects of the dopamine antagonist haloperidol on effort-related decision making in a T-maze cost/benefit procedure. Psychopharmacology (Berl) 204:103-112. Muir JL, Everitt BJ, Robbins TW (1995) Reversal of visual attentional dysfunction following lesions of the cholinergic basal forebrain by physostigmine and nicotine but not by the 5-HT3 receptor antagonist, ondansetron. Psychopharmacology (Berl) 118:82-92. Muir JL, Everitt BJ, Robbins TW (1996) The cerebral cortex of the rat and visual attentional function: dissociable effects of mediofrontal, cingulate, anterior dorsolateral, and parietal cortex lesions on a five-choice serial reaction time task. Cereb Cortex 6:470-481. Muir JL, Dunnett SB, Robbins TW, Everitt BJ (1992) Attentional functions of the forebrain cholinergic systems: effects of intraventricular hemicholinium, physostigmine, basal forebrain lesions and intracortical grafts on a multiple-choice serial reaction time task. Exp Brain Res 89:611-622. Mukherjee S, Yadav R, Yung I, Zajdel DP, Oken BS (2011) Sensitivity to mental effort and test-retest reliability of heart rate variability measures in healthy seniors. Clin Neurophysiol 122:2059-2066. Murray JE, Belin D, Everitt BJ (2012) Double dissociation of the dorsomedial and dorsolateral striatal control over the acquisition and performance of cocaine seeking. Neuropsychopharmacology 37:2456-2466. Naccache L, Dehaene S, Cohen L, Habert MO, Guichart-Gomez E, Galanaud D, Willer JC (2005) Effortless control: executive attention and conscious feeling of mental effort are dissociable. Neuropsychologia 43:1318-1328. Nakamura K, Kurasawa M (2001) Aniracetam restores motivation reduced by satiation in a choice reaction task in aged rats. Pharmacol Biochem Behav 68:65-69. Navarra R, Graf R, Huang Y, Logue S, Comery T, Hughes Z, Day M (2008) Effects of atomoxetine and methylphenidate on attention and impulsivity in the 5-choice serial reaction time test. Prog Neuropsychopharmacol Biol Psychiatry 32:34-41. Newell A, Simon HA (1972) Human problem solving. Englewood Cliffs, N.J.,: Prentice-Hall. Nicola SM (2007) The nucleus accumbens as part of a basal ganglia action selection circuit. Psychopharmacology (Berl) 191:521-550. 172  Norman DA, Bobrow DG (1975) On data-limited and resource-limited processes. Cognitive psychology 7:44-64. Nunes EJ, Randall PA, Santerre JL, Given AB, Sager TN, Correa M, Salamone JD (2010) Differential effects of selective adenosine antagonists on the effort-related impairments induced by dopamine D1 and D2 antagonism. Neuroscience 170:268-280. O'Doherty JP (2004) Reward representations and reward-related learning in the human brain: insights from neuroimaging. Curr Opin Neurobiol 14:769-776. O'Doherty JP (2011) Contributions of the ventromedial prefrontal cortex to goal-directed action selection. Ann N Y Acad Sci 1239:118-129. Ongini E, Fredholm BB (1996) Pharmacology of adenosine A2A receptors. Trends Pharmacol Sci 17:364-372. Paas F, Renkl A, Sweller J (2004) Cognitive load theory: Instructional implications of the interaction between information structures and cognitive architecture. Instructional science 32:1-8. Paelecke-Habermann Y, Pohl J, Leplow B (2005) Attention and executive functions in remitted major depression patients. J Affect Disord 89:125-135. Passetti F, Chudasama Y, Robbins TW (2002) The frontal cortex of the rat and visual attentional performance: dissociable functions of distinct medial prefrontal subregions. Cereb Cortex 12:1254-1268. Passetti F, Dalley JW, O'Connell MT, Everitt BJ, Robbins TW (2000) Increased acetylcholine release in the rat medial prefrontal cortex during performance of a visual attentional task. Eur J Neurosci 12:3051-3058. Paterson NE, Ricciardi J, Wetzler C, Hanania T (2011) Sub-optimal performance in the 5-choice serial reaction time task in rats was sensitive to methylphenidate, atomoxetine and d-amphetamine, but unaffected by the COMT inhibitor tolcapone. Neurosci Res 69:41-50. Pattij T, Vanderschuren LJ (2008) The neuropharmacology of impulsive behaviour. Trends Pharmacol Sci 29:192-199. Paxinos G, Watson C (1998) The rat brain in stereotaxic coordinates, 4th Edition. San Diego: Academic Press. Peeling P, Dawson B (2007) Influence of caffeine ingestion on perceived mood states, concentration, and arousal levels during a 75-min university lecture. Adv Physiol Educ 31:332-335. Poorthuis RB, Mansvelder HD (2013) Nicotinic acetylcholine receptors controlling attention: behavior, circuits and sensitivity to disruption by nicotine. Biochem Pharmacol 86:1089-1098. Randall PA, Pardo M, Nunes EJ, Lopez Cruz L, Vemuri VK, Makriyannis A, Baqi Y, Muller CE, Correa M, Salamone JD (2012) Dopaminergic modulation of effort-related choice behavior as assessed by a progressive ratio chow feeding choice task: pharmacological studies and the role of individual differences. PLoS ONE 7:e47934. Revelle W, Amaral P, Turriff S (1976) Introversion/extroversion, time stress, and caffeine: effect on verbal performance. Science 192:149-150. 173  Revelle W, Humphreys MS, Simon L, Gilliland K (1980) The interactive effect of personality, time of day, and caffeine: a test of the arousal model. J Exp Psychol Gen 109:1-31. Rice ME, Cragg SJ (2004) Nicotine amplifies reward-related dopamine signals in striatum. Nat Neurosci 7:583-584. Ridderinkhof KR, van den Wildenberg WP, Segalowitz SJ, Carter CS (2004) Neurocognitive mechanisms of cognitive control: the role of prefrontal cortex in action selection, response inhibition, performance monitoring, and reward-based learning. Brain Cogn 56:129-140. Robbins TW (2002) The 5-choice serial reaction time task: behavioural pharmacology and functional neurochemistry. Psychopharmacology (Berl) 163:362-380. Robbins TW, Roberts AC (2007) Differential regulation of fronto-executive function by the monoamines and acetylcholine. Cereb Cortex 17 Suppl 1:i151-160. Robertson SD, Matthies HJ, Galli A (2009) A closer look at amphetamine-induced reverse transport and trafficking of the dopamine and norepinephrine transporters. Molecular neurobiology 39:73-80. Robinson ES, Eagle DM, Mar AC, Bari A, Banerjee G, Jiang X, Dalley JW, Robbins TW (2008) Similar effects of the selective noradrenaline reuptake inhibitor atomoxetine on three distinct forms of impulsivity in the rat. Neuropsychopharmacology 33:1028-1037. Rogers MA, Kasai K, Koji M, Fukuda R, Iwanami A, Nakagome K, Fukuda M, Kato N (2004) Executive and prefrontal dysfunction in unipolar depression: a review of neuropsychological and imaging evidence. Neurosci Res 50:1-11. Rogers RD, Baunez C, Everitt BJ, Robbins TW (2001) Lesions of the medial and lateral striatum in the rat produce differential deficits in attentional performance. Behav Neurosci 115:799-811. Rogers RD, Wong A, McKinnon C, Winstanley CA (2013) Systemic administration of 8-OH-DPAT and eticlopride, but not SCH23390, alters loss-chasing behavior in the rat. Neuropsychopharmacology 38:1094-1104. Rudebeck PH, Walton ME, Smyth AN, Bannerman DM, Rushworth MF (2006a) Separate neural pathways process different decision costs. Nat Neurosci 9:1161-1168. Rudebeck PH, Walton ME, Smyth AN, Bannerman DM, Rushworth MFS (2006b) Separate neural pathways process different decision costs. Nature Neuroscience 9:1161-1168. Ruotsalainen S, Miettinen R, MacDonald E, Koivisto E, Sirvio J (2000) Blockade of muscarinic, rather than nicotinic, receptors impairs attention, but does not interact with serotonin depletion. Psychopharmacology (Berl) 148:111-123. Rushworth MF, Buckley MJ, Gough PM, Alexander IH, Kyriazis D, McDonald KR, Passingham RE (2005) Attentional selection and action selection in the ventral and orbital prefrontal cortex. J Neurosci 25:11628-11636. Salamone JD (2009) Dopamine, effort, and decision making: theoretical comment on Bardgett et al. (2009). Behav Neurosci 123:463-467. Salamone JD, Cousins MS, Bucher S (1994) Anhedonia or anergia? Effects of haloperidol and nucleus accumbens dopamine depletion on instrumental response selection in a T-maze cost/benefit procedure. Behav Brain Res 65:221-229. 174  Salamone JD, Correa M, Farrar A, Mingote SM (2007) Effort-related functions of nucleus accumbens dopamine and associated forebrain circuits. Psychopharmacology (Berl) 191:461-482. Salamone JD, Correa M, Farrar AM, Nunes EJ, Pardo M (2009) Dopamine, behavioral economics, and effort. Front Behav Neurosci 3:13. Samanez-Larkin GR, Wagner AD, Knutson B (2011) Expected value information improves financial risk taking across the adult life span. Social cognitive and affective neuroscience 6:207-217. Schlam TR, Wilson NL, Shoda Y, Mischel W, Ayduk O (2013) Preschoolers' delay of gratification predicts their body mass 30 years later. J Pediatr 162:90-93. Schmidt L, Lebreton M, Clery-Melin ML, Daunizeau J, Pessiglione M (2012) Neural mechanisms underlying motivation of mental versus physical effort. PLoS Biol 10:e1001266. Schultz W, Dayan P, Montague PR (1997) A neural substrate of prediction and reward. Science 275:1593-1599. Schweimer J, Hauber W (2006) Dopamine D1 receptors in the anterior cingulate cortex regulate effort-based decision making. Learn Mem 13:777-782. Seamans JK, Floresco SB, Phillips AG (1995) Functional differences between the prelimbic and anterior cingulate regions of the rat prefrontal cortex. Behav Neurosci 109:1063-1073. Seamans JK, Lapish CC, Durstewitz D (2008) Comparing the prefrontal cortex of rats and primates: insights from electrophysiology. Neurotox Res 14:249-262. Seo M, Lee E, Averbeck BB (2012) Action selection and action value in frontal-striatal circuits. Neuron 74:947-960. Shafiei N, Gray M, Viau V, Floresco SB (2012) Acute stress induces selective alterations in cost/benefit decision-making. Neuropsychopharmacology 37:2194-2209. Shah AK, Oppenheimer DM (2008) Heuristics made easy: an effort-reduction framework. Psychol Bull 134:207-222. Shalev A, Bleich A, Ursano RJ (1990) Posttraumatic stress disorder: somatic comorbidity and effort tolerance. Psychosomatics 31:197-203. Shannon HE, Eberle EL (2006) Effects of biasing the location of stimulus presentation, and the muscarinic cholinergic receptor antagonist scopolamine, on performance of a 5-choice serial reaction time attention task in rats. Behav Pharmacol 17:71-85. Silber BY, Croft RJ, Papafotiou K, Stough C (2006) The acute effects of d-amphetamine and methamphetamine on attention and psychomotor performance. Psychopharmacology (Berl) 187:154-169. Smit AS, Eling PA, Coenen AM (2004) Mental effort causes vigilance decrease due to resource depletion. Acta Psychol (Amst) 115:35-42. Smit AS, Eling PA, Hopman MT, Coenen AM (2005) Mental and physical effort affect vigilance differently. Int J Psychophysiol 57:211-217. Smith BW, Mitchell DG, Hardin MG, Jazbec S, Fridberg D, Blair RJ, Ernst M (2009) Neural substrates of reward magnitude, probability, and risk during a wheel of fortune decision-making task. Neuroimage 44:600-609. 175  Smith DG, Xiao L, Bechara A (2012) Decision making in children and adolescents: impaired Iowa Gambling Task performance in early adolescence. Developmental psychology 48:1180-1187. Sokol-Hessner P, Camerer CF, Phelps EA (2013) Emotion regulation reduces loss aversion and decreases amygdala responses to losses. Social cognitive and affective neuroscience 8:341-350. Solbakk AK, Lovstad M (2014) Effects of focal prefrontal cortex lesions on electrophysiological indices of executive attention and action control. Scand J Psychol 55:233-243. Squire RF, Noudoost B, Schafer RJ, Moore T (2013) Prefrontal contributions to visual selective attention. Annu Rev Neurosci 36:451-466. St Onge JR, Floresco SB (2009) Dopaminergic modulation of risk-based decision making. Neuropsychopharmacology 34:681-697. St Onge JR, Floresco SB (2010) Prefrontal cortical contribution to risk-based decision making. Cereb Cortex 20:1816-1828. St Onge JR, Chiu YC, Floresco SB (2010) Differential effects of dopaminergic manipulations on risky choice. Psychopharmacology (Berl) 211:209-221. Sternberg RJ (2002) Why smart people can be so stupid. New Haven: Yale University Press. Stolerman IP, Naylor CG, Mesdaghinia A, Morris HV (2009) The duration of nicotine-induced attentional enhancement in the five-choice serial reaction time task: lack of long-lasting cognitive improvement. Behav Pharmacol 20:742-754. Stulemeijer M, Andriessen TM, Brauer JM, Vos PE, Van Der Werf S (2007) Cognitive performance after mild traumatic brain injury: the impact of poor effort on test results and its relation to distress, personality and litigation. Brain Inj 21:309-318. Stulemeijer M, van der Werf S, Bleijenberg G, Biert J, Brauer J, Vos PE (2006) Recovery from mild traumatic brain injury: a focus on fatigue. J Neurol 253:1041-1047. Sun H, Cocker PJ, Zeeb FD, Winstanley CA (2012) Chronic atomoxetine treatment during adolescence decreases impulsive choice, but not impulsive action, in adult rats and alters markers of synaptic plasticity in the orbitofrontal cortex. Psychopharmacology (Berl) 219:285-301. Sun H, Green TA, Theobald DE, Birnbaum SG, Graham DL, Zeeb FD, Nestler EJ, Winstanley CA (2010) Yohimbine increases impulsivity through activation of cAMP response element binding in the orbitofrontal cortex. Biol Psychiatry 67:649-656. Tai LH, Lee AM, Benavidez N, Bonci A, Wilbrecht L (2012) Transient stimulation of distinct subpopulations of striatal neurons mimics changes in action value. Nat Neurosci 15:1281-1289. Thiel CM, Zilles K, Fink GR (2005) Nicotine modulates reorienting of visuospatial attention and neural activity in human parietal cortex. Neuropsychopharmacology 30:810-820. Treadway MT, Bossaller NA, Shelton RC, Zald DH (2012a) Effort-based decision-making in major depressive disorder: a translational model of motivational anhedonia. J Abnorm Psychol 121:553-558. 176  Treadway MT, Buckholtz JW, Schwartzman AN, Lambert WE, Zald DH (2009a) Worth the 'EEfRT'? The Effort Expenditure for Rewards Task as an Objective Measure of Motivation and Anhedonia. PLoS ONE 4. Treadway MT, Buckholtz JW, Schwartzman AN, Lambert WE, Zald DH (2009b) Worth the 'EEfRT'? The effort expenditure for rewards task as an objective measure of motivation and anhedonia. PLoS ONE 4:e6598. Treadway MT, Buckholtz JW, Cowan RL, Woodward ND, Li R, Ansari MS, Baldwin RM, Schwartzman AN, Kessler RM, Zald DH (2012b) Dopaminergic mechanisms of individual differences in human effort-based decision-making. J Neurosci 32:6170-6176. Tremblay M, Cocker PJ, Hosking JG, Zeeb FD, Rogers RD, Winstanley CA (2014) Dissociable effects of basolateral amygdala lesions on decision making biases in rats when loss or gain is emphasized. Cogn Affect Behav Neurosci. Tursky B, Shapiro D, Crider A, Kahneman D (1969) Pupillary, heart rate, and skin resistance changes during a mental task. J Exp Psychol 79:164-167. Tversky A, Kahneman D (1974) Judgment under Uncertainty: Heuristics and Biases. Science 185:1124-1131. Tversky A, Kahneman D (1981) The framing of decisions and the psychology of choice. Science 211:453-458. Uylings HB, Groenewegen HJ, Kolb B (2003) Do rats have a prefrontal cortex? Behav Brain Res 146:3-17. Van Dort CJ, Baghdoyan HA, Lydic R (2009) Adenosine A(1) and A(2A) receptors in mouse prefrontal cortex modulate acetylcholine release and behavioral arousal. J Neurosci 29:871-881. van Honk J, Eisenegger C, Terburg D, Stein DJ, Morgan B (2013) Generous economic investments after basolateral amygdala damage. Proc Natl Acad Sci U S A 110:2506-2510. Vartanian O, Mandel DR (2011) Neuroscience of decision making. New York, NY: Psychology Press. Vedhara K, Hyde J, Gilchrist ID, Tytherleigh M, Plummer S (2000) Acute stress, memory, attention and cortisol. Psychoneuroendocrino 25:535-549. Vervaeke J, Ferraro L (2013) Relevance Realization and the Neurodynamics and Neuroconnectivity of General Intelligence. In: SmartData, pp 57-68: Springer. Vervaeke J, Lillicrap TP, Richards BA (2009) Relevance realization and the emerging framework in cognitive science. Journal of Logic and Computation:exp067. Wallman KE, Sacco P (2007) Sense of effort during a fatiguing exercise protocol in chronic fatigue syndrome. Res Sports Med 15:47-59. Walton ME, Bannerman DM, Rushworth MF (2002a) The role of rat medial frontal cortex in effort-based decision making. J Neurosci 22:10996-11003. Walton ME, Bannerman DM, Rushworth MFS (2002b) The role of rat medial frontal cortex in effort-based decision making. Journal of Neuroscience 22:10996-11003. Walton ME, Devlin JT, Rushworth MF (2004) Interactions between decision making and performance monitoring within prefrontal cortex. Nat Neurosci 7:1259-1265. Walton ME, Bannerman DM, Alterescu K, Rushworth MFS (2003a) Functional specialization within medial frontal cortex of the anterior cingulate for evaluating effort-related decisions. Journal of Neuroscience 23:6475-6479. 177  Walton ME, Bannerman DM, Alterescu K, Rushworth MF (2003b) Functional specialization within medial frontal cortex of the anterior cingulate for evaluating effort-related decisions. J Neurosci 23:6475-6479. Walton ME, Croxson PL, Rushworth MF, Bannerman DM (2005) The mesocortical dopamine projection to anterior cingulate cortex plays no role in guiding effort-related decisions. Behav Neurosci 119:323-328. Walton ME, Groves J, Jennings KA, Croxson PL, Sharp T, Rushworth MF, Bannerman DM (2009) Comparing the role of the anterior cingulate cortex and 6-hydroxydopamine nucleus accumbens lesions on operant effort-based decision making. Eur J Neurosci 29:1678-1691. Wardle MC, Treadway MT, Mayo LM, Zald DH, de Wit H (2011) Amping up effort: effects of d-amphetamine on human effort-based decision-making. J Neurosci 31:16597-16602. Wassum KM, Cely IC, Balleine BW, Maidment NT (2011) Micro-opioid receptor activation in the basolateral amygdala mediates the learning of increases but not decreases in the incentive value of a food reward. J Neurosci 31:1591-1599. Wellman LL, Gale K, Malkova L (2005) GABAA-mediated inhibition of basolateral amygdala blocks reward devaluation in macaques. J Neurosci 25:4577-4586. West EA, Forcelli PA, Murnen AT, McCue DL, Gale K, Malkova L (2012) Transient inactivation of basolateral amygdala during selective satiation disrupts reinforcer devaluation in rats. Behav Neurosci 126:563-574. Westbrook A, Kester D, Braver TS (2013) What is the subjective cost of cognitive effort? Load, trait, and aging effects revealed by economic preference. PLoS ONE 8:e68210. White TL, Lejuez CW, de Wit H (2007) Personality and gender differences in effects of d-amphetamine on risk taking. Exp Clin Psychopharmacol 15:599-609. Wickens JR, Reynolds JN, Hyland BI (2003) Neural mechanisms of reward-related motor learning. Curr Opin Neurobiol 13:685-690. Wilens TE, Decker MW (2007) Neuronal nicotinic receptor agonists for the treatment of attention-deficit/hyperactivity disorder: focus on cognition. Biochem Pharmacol 74:1212-1223. Williams NM et al. (2012) Genome-wide analysis of copy number variants in attention deficit hyperactivity disorder: the role of rare variants and duplications at 15q13.3. Am J Psychiatry 169:195-204. Williams ZM, Bush G, Rauch SL, Cosgrove GR, Eskandar EN (2004) Human anterior cingulate neurons and the integration of monetary reward with motor responses. Nat Neurosci 7:1370-1375. Winstanley CA, Theobald DE, Dalley JW, Robbins TW (2005) Interactions between serotonin and dopamine in the control of impulsive choice in rats: therapeutic implications for impulse control disorders. Neuropsychopharmacology 30:669-682. Worden LT, Shahriari M, Farrar AM, Sink KS, Hockemeyer J, Muller CE, Salamone JD (2009) The adenosine A2A antagonist MSX-3 reverses the effort-related effects of dopamine blockade: differential interaction with D1 and D2 family antagonists. Psychopharmacology (Berl) 203:489-499. 178  Wright RA, Martin RE, Bland JL (2003) Energy resource depletion, task difficulty, and cardiovascular response to a mental arithmetic challenge. Psychophysiology 40:98-105. Wright RA, Stewart CC, Barnett BR (2008) Mental fatigue influence on effort-related cardiovascular response: extension across the regulatory (inhibitory)/non-regulatory performance dimension. Int J Psychophysiol 69:127-133. Yamada H, Tymula A, Louie K, Glimcher PW (2013) Thirst-dependent risk preferences in monkeys identify a primitive form of wealth. Proc Natl Acad Sci U S A 110:15788-15793. Yu AJ, Dayan P (2005) Uncertainty, neuromodulation, and attention. Neuron 46:681-692. Yu AJ, Dayan P, Cohen JD (2009) Dynamics of attentional selection under conflict: toward a rational Bayesian account. J Exp Psychol Hum Percept Perform 35:700-717. Zeeb FD, Winstanley CA (2011) Lesions of the basolateral amygdala and orbitofrontal cortex differentially affect acquisition and performance of a rodent gambling task. J Neurosci 31:2197-2204. Zeeb FD, Winstanley CA (2013) Functional disconnection of the orbitofrontal cortex and basolateral amygdala impairs acquisition of a rat gambling task and disrupts animals' ability to alter decision-making behavior after reinforcer devaluation. J Neurosci 33:6434-6443. Zeeb FD, Robbins TW, Winstanley CA (2009) Serotonergic and dopaminergic modulation of gambling behavior as assessed using a novel rat gambling task. Neuropsychopharmacology 34:2329-2343. Zhang H, Sulzer D (2004) Frequency-dependent modulation of dopamine release by nicotine. Nat Neurosci 7:581-582.    179  Appendices  Appendix 1: Baseline behavioural measures for the rat Cognitive Effort Task (rCET) Data shown are the mean ± SEM. Measure Baseline session Workers Slackers  High reward Low reward High reward Low reward Lever choice latency (s) 1 2.56 ± 0.19 2.04 ± 0.33 2.94 ± 0.43 2.68 ± 0.40 2 2.59 ± 0.17 2.19 ± 0.28 2.89 ± 0.44 2.94 ± 0.44 3 2.60 ± 0.15 2.08 ± 0.30 2.79 ± 0.48 2.77 ± 0.36 4 2.70 ± 0.13 2.24 ± 0.26 2.92 ± 0.41 2.90 ± 0.34  5 2.59 ± 0.15 2.19 ± 0.27 2.98 ± 0.58 2.75 ± 0.42 Collection latency (s) 1 1.37 ± 0.64  1.40 ± 0.07 1.15 ± 0.06 1.25 ± 0.10 2 1.34 ± 0.05 1.39 ± 0.07 1.15 ± 0.06 1.34 ± 0.15 3 1.40 ± 0.08 1.47 ± 0.06 1.16 ± 0.07 1.26 ± 0.10 4 1.35 ± 0.06 1.56 ± 0.07 1.17 ± 0.06 1.30 ± 0.08 5 1.49 ± 0.15 1.49 ± 0.06 1.13 ± 0.07 1.33 ± 0.11 Correct latency (s) 1 1.37 ± 0.06 1.40 ± 0.07 1.15 ± 0.06 1.25 ± 0.10  2 1.34 ± 0.05 1.39 ± 0.07 1.15 ± 0.06 1.34 ± 0.15 3 1.40 ± 0.08 1.47 ± 0.06 1.16 ± 0.07 1.26 ± 0.10 4 1.35 ± 0.06 1.56 ± 0.07 1.17 ± 0.06 1.30 ± 0.08 5 1.49 ± 0.15 1.49 ± 0.06 1.49 ± 0.15 1.33 ± 0.11 Response omissions (%) 1 3.00 ± 0.95  3.49 ± 1.49 3.12 ± 0.81 2.17 ± 0.99 2 3.13 ± 1.44 5.48 ± 3.02 2.50 ± 1.02 4.84 ± 2.42 3 3.43 ± 1.45 2.42 ± 1.03 1.60 ± 0.88 2.70 ± 0.96 4 3.11 ± 1.05 4.25 ± 1.35 2.76 ± 1.17 2.36 ± 0.93 5 2.54 ± 1.20 5.13 ± 2.95 2.28 ± 0.56 2.57 ± 1.49   Workers Slackers Choice Omission 1 1.36 ± 0.47 1.75 ± 1.08  2 0.64 ± 0.31 1.25 ± 1.25 3 0.27 ± 0.14 0.63 ± 0.26 4 1.73 ± 0.79 4.00 ± 2.34  5 0.36 ± 0.28 3.88 ± 2.79 Trials completed 1 162.55 ± 4.49 162.00 ± 10.77 2 161.73 ± 4.96 160.38 ± 10.93 3 164.82 ± 4.32 167.50 ± 8.17 4 156.73 ± 4.03 156.88 ± 10.60  5 163.00 ± 4.79 163.75 ± 13.20   180  Appendix 2: Behavioural measures during amphetamine challenge for the rCET  Data shown are the mean ± SEM.  Measure Amphetamine Workers Slackers  Dose High reward Low reward High reward Low reward Lever choice latency (s) Saline 2.47 ± 0.19 2.05 ± 0.27 2.77 ± 0.19 2.31 ± 0.32 0.3mg/kg 2.09 ± 0.21 2.20 ± 0.30 2.09 ± 0.21 2.49 ± 0.30 0.6mg/kg 1.92 ± 0.26 2.67 ± 0.56 1.92 ± 0.26 2.68 ± 0.40 1.0 mg/kg 2.26 ± 0.22 2.57 ± 0.34 2.26 ± 0.22 1.96 ± 0.33 Collection latency (s) Saline 1.75 ± 0.36 1.49 ± 0.12 1.13 ± 0.07 1.30 ± 0.11 0.3mg/kg 1.78 ± 0.26 1.65 ± 0.28 1.23 ± 0.17 1.97 ± 0.61 0.6mg/kg 2.00 ± 0.58 1.89 ± 0.37 1.21 ± 0.09 1.48 ± 0.22 1.0 mg/kg 1.42 ± 0.21 1.91 ± 0.37 2.01 ± 0.58 1.65 ± 0.44 Correct Latency (s) Saline 1.75 ± 0.36 1.49 ± 0.12 1.13 ± 0.07 1.30 ± 0.11 0.3mg/kg 1.78 ± 0.26 1.65 ± 0.28 1.23 ± 0..7 1.97 ± 0.61 0.6mg/kg 2.00 ±0.58 1.89 ± 0.37 1.21 ± 0.09 1.48 ± 0.22 1.0 mg/kg 1.43 ± 0.21 1.91 ± 0.37 2.01 ± 0.58 1.65 ± 0.43 Response omissions (%) Saline 6.33 ± 2.31 17.51 ± 8.57 4.52 ± 1.74 6.29 ± 3.71 0.3mg/kg 8.84 ± 3.80 4.59 ± 2.51 13.94 ± 7.86 3.91 ± 2.56 0.6mg/kg 7.63 ± 2.74 4.0 ± 3.29 12.86 ± 9.83 1.91 ± 1.38 1.0 mg/kg 1.60 ± 0.54 2.56 ± 1.14 2.18 ± 0.70 2.78 ± 0.95   Workers Slackers Choice omissions Saline 0.73 ± 0.51 2.25 ± 1.61 0.3mg/kg 0.73 ± 0.30 3.38 ± 1.27 0.6mg/kg 4.18 ± 1.48 9.75 ± 4.45 1.0 mg/kg 12.09 ± 3.80 10.25 ± 4.17 Trials completed Saline 162.55 ± 5.44 157.88 ± 13.54 0.3mg/kg 161.18 ± 5.59 150.88 ± 13.74 0.6mg/kg 140.73 ± 11.58 134.00 ± 16.38 1.0 mg/kg 142.46 ± 8.79 139.38 ± 19.41   181  Appendix 3: Behavioural measures during ethanol challenge for the rCET  Data shown are the mean ± SEM.  Measure Ethanol Dose Workers Slackers  High reward Low reward High reward Low reward Lever choice latency (s) Saline 2.62 ± 0.18 2.20 ± 0.23 2.76 ± 0.18 2.64 ± 0.40 0.3g/kg 2.36 ± 0.13 2.19 ± 0.22 2.37 ± 0.46 2.44 ± 0.30 0.6g/kg 2.54 ± 0.19 2.00 ± 0.28 2.51 ± 0.43 2.65 ± 0.42 1.0g/kg 2.87 ± 0.30 2.54 ± 0.39 2.69 ± 0.49 2.81 ± 0.47 Collection latency (s) Saline 2.03 ± 0.70 1.48 ± 0.09 1.12 ± 0.07 1.37 ± 0.13 0.3g/kg 1.32 ± 0.06 1.46 ± 0.09 1.20 ± 0.10 1.31 ± 0.18 0.6g/kg 1.41 ± 0.11 1.33 ± 0.06 1.12 ± 0.07 1.20 ± 0.09 1.0g/kg 1.46 ± 0.13 1.68 ± 0.20 1.11 ± 0.07 1.21 ± 0.10 Correct latency (s) Saline 2.03 ± 0.70  1.48 ± 0.09 1.12 ± 0.07 1.37 ± 0.13 0.3g/kg 1.32 ± 0.06 1.46 ± 0.09 1.20 ± 0.10 1.31 ± 0.18 0.6g/kg 1.41 ± 0.11 1.33 ± 0.06 1.12 ± 0.07 1.20 ± 0.09 1.0g/kg 1.46 ± 0.13 1.68 ± 0.20 1.11 ± 0.07 1.21 ± 0.10 Response omissions (%) Saline 4.81 ± 1.15 4.88 ± 1.70 9.26 ± 4.20 8.17 ± 2.97 0.3g/kg 3.53 ± 1.99 1.69 ± 0.92 6.06 ± 2.80 3.96 ± 2.35 0.6g/kg 1.77 ± 0.53 2.69 ± 1.61 3.42 ± 1.05 2.00 ± 0.79 1.0g/kg 6.86 ± 3.28 10.67 ± 4.11 6.13 ± 2.97 8.57 ± 4.58   Workers Slackers Choice Omission Saline 1.73 ± 0.38 3.00 ± 1.22 0.3g/kg 0.82 ± 0.26 2.88 ± 1.20 0.6g/kg 0.73 ± 0.24 1.63 ± 0.78 1.0g/kg 5.45 ± 2.81 4.50 ± 2.40 Trials completed Saline 157.09 ± 6.62 147.38 ± 17.29 0.3g/kg 167.14 ± 4.10 161.50 ± 14.47 0.6g/kg 166.91 ± 3.84  171.00 ± 8.74 1.0g/kg 145.36 ± 8.93 164.50 ± 11.85   182  Appendix 4: Behavioural measures during caffeine challenge for the rCET  Data shown are the mean ± SEM.  Measure Caffeine Dose Workers Slackers  High reward Low reward High reward Low reward Lever choice latency (s) Saline 2.58 ± 0.23 2.81 ± 0.34 2.68 ± 0.25 2.98 ± 0.36  5 mg/kg 2.47 ± 0.21 2.01 ± 0.24 2.53 ± 0.25 2.43 ± 0.25 10mg/kg 2.52 ± 0.25 2.46 ± 0.42 2.33 ± 0.32 2.72 ± 0.18 20mg/kg 2.83 ± 0.30 2.19 ± 0.25 2.38 ± 0.35 2.51 ± 0.32 Collection latency (s) Saline 1.38 ± 0.07 1.41 ± 0.07 1.12 ± 0.06 1.61 ± 0.35 5 mg/kg 1.62 ± 0.23 1.40 ± 0.09 1.12 ± 0.06 1.20 ± 0.09 10mg/kg 1.51 ± 0.16 1.31 ± 0.04 1.10 ± 0.06 1.18 ± 0.09 20mg/kg 1.45 ± 014 1.64 ± 0.21 1.08 ± 0.06 1.16 ± 0.08 Correct latency (s) Saline 1.38 ± 0.07 1.41 ± 0.07 1.12 ± 0.06 1.61 ± 0.35 5 mg/kg 1.62 ± 0.23 1.40 ± 0.09 1.12 ± 0.06 1.20 ± 0.09 10mg/kg 1.51 ± 0.16 1.31 ± 0.04 1.10 ± 0.06 1.18 ± 0.09 20mg/kg 1.45 ± 0.14 1.64 ± 0.21 1.08 ± 0.06 1.16 ± 0.08 Response omissions (%) Saline 2.39 ± 0.80 3.98 ± 2.13 4.16 ± 1.18 3.64 ± 2.08  5 mg/kg 2.12 ± 0.39 6.15 ± 3.05 3.19 ± 1.12 3.55 ± 1.85 10mg/kg 4.86 ± 1.03 7.10 ± 2.68 2.90 ± 0.69 2.25 ± 1.71 20mg/kg 5.80 ± 1.62 6.23 ± 3.05 7.21 ± 2.35 6.54 ±1.94   Workers Slackers Choice Omission Saline 2.45 ± 1.14 2.75 ± 1.37  5 mg/kg 1.45 ± 0.34 2.88 ± 1.25 10mg/kg 3.09 ± 0.94 1.75 ± 0.77 20mg/kg 4.45 ± 1.14 3.13 ± 0.90 Trials completed Saline 161.00 ± 4.76 151.13 ± 16.46 5 mg/kg 161.91 ± 5.50 161.63 ± 11.48 10mg/kg 149.00 ± 6.27 163.13 ± 11.01 20mg/kg 137.64 ± 7.65 146.63 ± 12.30   183  Appendix 5: Behavioural measures during satiation manipulations for the rCET  Data shown are the mean ± SEM. Measure Condition Workers Slackers  High reward Low reward High reward Low reward Lever choice latency (s) Food restricted 2.75 ± 0.20 2.42 ± 0.28 3.04 ± 0.48 2.62 ± 0.51 Acute satiation 3.04 ± 0.27 3.39 ± 0.59 3.36 ± 0.24 3.26 ± 0.47 Chronic satiation 3.37 ± 0.15 3.67 ± 0.29 3.74 ± 0.33 3.34 ± 0.38 Collection latency (s) Food restricted 1.47 ± 0.09 1.73 ± 0.17 1.36 ± 0.13 1.28 ± 0.07 Acute satiation 1.59 ± 0.07 1.83 ± 0.17 1.98 ± 0.31 1.81 ± 0.38 Chronic satiation 1.75 ± 0.06 2.63 ± 0.51 2.07 ± 0.26 1.65 ± 0.11 Correct latency (s) Food restricted 1.47 ± 0.09 1.73 ± 0.17 1.28 ± 0.07 1.36 ± 0.17 Acute satiation 1.60 ± 0.07 1.83 ± 0.17 1.81 ± 0.38 1.98 ± 0.31 Chronic satiation 1.75 ± 0.06 2.63 ± 0.51 1.65 ± 0.11 2.07 ± 0.26 Response omissions (%) Food restricted 3.14 ± 1.10 9.20 ± 4.47 2.47 ± 1.10 8.80 ± 6.07 Acute satiation 18.34 ± 4.86 11.03 ± 5.46  22.01 ± 7.74 17.16 ± 6.57 Chronic satiation 38.01 ± 4.55 42.04 ± 7.74 51.14 ± 7.46 41.71 ± 6.97   Workers Slackers Choice omissions Food restricted 3.27 ± 1.29  3.38 ± 2.41 Acute satiation 6.64 ± 1.84 10.25 ± 3.26 Chronic satiation 5.37 ± 1.09 6.08 ± 2.11 Trials completed Food restricted 147.91 ± 9.24 164.63 ± 11.63 Acute satiation 89.55 ± 14.53 84.38 ± 19.18 Chronic satiation 44.80 ± 8.30 31.10 ± 6.01       184  Appendix 6: Baseline behavioural measures for the yoked-control task  Data shown are the mean ± SEM. Measure Baseline session Yoked-workers Yoked-slackers  High reward Low reward High reward Low reward Lever choice latency (s) 1 1.97 ± 0.16 2.51 ± 0.41 2.37 ± 0.27 3.03 ± 0.37 2 2.05 ± 0.21 2.33 ± 0.36 2.21 ± 0.30 2.69 ± 0.25 3 2.04 ± 0.18  2.47 ± 0.47 2.51 ± 0.31 3.16 ± 0.36 4 2.18 ± 0.22 2.45 ± 0.28 2.26 ± 0.22 2.76 ± 0.33  5 2.15 ± 0.21 2.29 ± 0.30 2.44 ± 0.25 2.83 ± 0.24 Collection latency (s) 1 1.15 ± 0.06 1.30 ± 0.06 1.21 ± 0.09 1.34 ± 0.08 2 1.19 ± 0.06 1.35 ± 0.07 1.18 ± 0.09 1.27 ± 0.09 3 1.22 ± 0.08 1.36 ± 0.07 1.18 ± 0.08 1.27 ± 0.13 4 1.23 ± 0.08 1.33 ± 0.05 1.22 ± 0.10 1.35 ± 0.09 5 1.19 ± 0.07 1.30 ± 0.06 1.17 ± 0.09 1.28 ± 0.05 Correct latency (s) 1 1.15 ± 0.06 1.30 ± 0.06 1.21 ± 0.09 1.31 ± 0.08 2 1.19 ± 0.06 1.35 ± 0.07 1.18 ± 0.09 1.27 ± 0.09 3 1.22 ± 0.08 1.36 ± 0.07 1.11 ± 0.08 1.27 ± 0.13 4 1.22 ± 0.08 1.33 ± 0.05 1.22 ± 0.10 1.35 ± 0.09 5 1.19 ± 0.07 1.30 ± 0.06 1.17 ± 0.09 1.28 ± 0.05   Yoked-workers Yoked-slackers Choice Omission 1 2.55 ± 1.45 1.13 ± 0.61 2 0.82 ± 0.38 0.88 ± 0.44 3 1.18 ± 0.81 1.63 ± 0.96 4 1.55 ± 0.65 0.63 ± 0.42  5 0.73 ± 0.33 1.25 ± 0.53 Trials completed 1 173.91 ± 9.07 166.13 ± 9.25 2 181.09 ± 6.57 168.13 ± 7.86 3 185.27 ± 5.24 163.88 ± 6.85  4 180.36 ± 6.34 168.75 ± 6.94  5 180.09 ± 5.93 168.50 ± 7.27   185  Appendix 7: Behavioural measures during amphetamine challenge for the yoked-control task  Data shown are the mean ± SEM. Measure Amphetamine Dose Yoked-workers Yoked-slackers  High reward Low reward High reward Low reward Lever choice latency (s) Saline 1.90 ± 0.20 2.35 ± 0.37 1.99 ± 0.16 3.15 ± 0.67 0.3 mg/kg 1.79 ± 0.14 1.84 ± 0.25 1.88 ± 0.22 3.09 ± 0.58 0.6 mg/kg 1.80 ± 0.16 2.13 ± 0.32 1.85 ± 0.31 2.86 ± 0.48 1.0 mg/kg 1.69 ± 0.15 2.18 ± 0.27 1.93 ± 0.19 2.44 ± 0.22 Collection latency (s) Saline 1.56 ± 0.06 1.30 ± 0.06 1.14 ± 0.08  1.17 ± 0.06 0.3 mg/kg 1.08 ± 0.05 1.17 ± 0.05 1.06 ± 0.08 1.26 ± 0.20 0.6 mg/kg 1.01 ± 0.5 1.10 ± 0.06 1.03 ± 0.08 1.06 ± 0.07 1.0 mg/kg 1.05 ± 0.04 1.11 ± 0.06 1.07 ± 0.09 1.11 ± 0.09 Correct latency (s) Saline 1.16 ± 0.06 1.30 ± 0.06 1.14 ± 0.08 1.17 ± 0.06 0.3 mg/kg 1.08 ± 0.05 1.17 ± 0.05 1.06 ± 0.08 1.26 ± 0.20 0.6 mg/kg 1.01 ± 0.05 1.10 ± 0.06 1.03 ± 0.08 1.06 ± 0.07 1.0 mg/kg 1.05 ± 0.04 1.11 ± 0.06 1.07 ± 0.09 1.11 ± 0.09   Yoked-workers Yoked-slackers Choice Omission Saline 0.82 ± 0.30 2.63 ± 1.43 0.3 mg/kg 1.18 ± 0.69 5.88 ± 3.37 0.6 mg/kg 3.36 ± 2.19 2.75 ± 1.57 1.0 mg/kg 4.36 ± 1.61 1.25 ± 0.73 Trials completed Saline 178.82 ± 9.51  170.38 ± 11.64 0.3 mg/kg 179.73 ± 8.68 178.00 ± 5.22 0.6 mg/kg 169.09 ± 10.01 165.38 ± 7.65 1.0 mg/kg 159.18 ± 14.01 165.00 ± 6.61   186  Appendix 8: Behavioural measures during ethanol challenge for the yoked-control task  Data shown are the mean ± SEM. Measure Ethanol Dose Yoked-workers Yoked-slackers  High reward Low reward High reward Low reward Lever choice latency (s) Saline 1.73 ± 0.15 2.52 ± 0.25 2.01 ± 0.33 2.81 ± 0.42 0.3g/kg 1.72 ± 0.14 2.21 ± 0.32 2.10 ± 0.34 2.03 ± 0.23 0.6g/kg 2.09 ± 0.27 2.36 ± 0.29 2.01 ± 0.33 2.49 ± 0.39 1.0g/kg 2.18 ± 0.21 2.71 ± 0.31 2.07 ± 0.33 3.14 ± 0.45 Collection latency (s) Saline 1.13 ± 0.06 1.20 ± 0.05 1.16 ± 0.08 1.25 ± 0.10 0.3g/kg 1.13 ± 0.06 1.24 ± 0.06 1.16 ± 0.08 1.22 ± 0.08 0.6g/kg 1.20 ± 0.08 1.31 ± 0.07 1.15 ± 0.08 1.25 ± 0.09 1.0g/kg 1.20 ± 0.07 1.30 ± 0.07 1.17 ± 0.09 1.40 ± 0.14 Correct latency (s) Saline 0.59 ± 0.06 0.59 ± 0.04 0.66 ± 0.08 0.71 ± 0.07 0.3g/kg 0.65 ± 0.05 0.62 ± 0.07 0.69 ± 0.09 0.70 ± 0.10 0.6g/kg 0.62 ± 0.04 0.60 ± 0.04 0.70 ± 0.09 0.86 ± 0.16 1.0g/kg 0.75 ± 0.08 0.66 ± 0.03 0.64 ± 0.06 0.67 ± 0.08   Yoked-workers Yoked-slackers Choice Omission Saline 0.09 ± 0.09 0.38 ± 0.26 0.3g/kg 1.27 ± 1.27 0.50 ± 0.27 0.6g/kg 2.09 ± 1.80  0.63 ± 0.42 1.0g/kg 2.91 ± 1.67  1.00 ± 0.76 Trials completed Saline 189.73 ± 3.60 186.75 ± 7.33 0.3g/kg 187.00 ± 6.47 184.38 ± 7.69 0.6g/kg 184.73 ± 6.73 182.13 ± 7.48 1.0g/kg 169.00 ± 9.15 182.13 ± 8.58   187  Appendix 9: Behavioural measures during caffeine challenge for the yoked-control task  Data shown are the mean ± SEM. Measure Caffeine Dose Yoked-workers Yoked-slackers  High reward Low reward High reward Low reward Lever choice latency (s) Saline 1.95 ± 0.16 2.16 ± 0.22 1.94 ± 0.29 2.05 ± 0.14 5 mg/kg 1.58 ± 0.16 1.91 ± 0.15 1.50 ± 0.27 2.14 ± 0.37 10mg/kg 1.73 ± 0.14 1.92 ± 0.14 1.53 ± 0.33 2.06 ± 0.30 20mg/kg 1.85 ± 0.17 2.07 ± 0.26 1.76 ± 0.26 2.37 ± 0.40 Collection latency (s) Saline 1.19 ± 0.07 1.28 ± 0.05 1.19 ± 0.08 1.27 ± 0.07 5 mg/kg 1.09 ± 0.05 1.17 ± 0.05 1.08 ± 0.08 1.23 ± 0.13 10mg/kg 1.10 ± 0.05 1.17 ± 0.05 1.09 ± 0.07 1.30 ± 0.09 20mg/kg 1.11 ± 0.05 1.25 ± 0.14 1.14 ± 0.08 1.22 ± 0.09 Correct latency (s) Saline 0.79 ± 0.13  0.94 ± 0.21 0.70 ± 0.05 0.78 ± 0.07 5 mg/kg 0.71 ± 0.07 0.66 ± 0.05 0.65 ± 0.09 0.86 ± 0.20 10mg/kg 0.72 ± 0.08 0.73 ± 0.07 1.99 ± 1.34  0.76 ± 0.11 20mg/kg 0.81 ± 0.09 0.79 ± 0.09 0.69 ± 0.09 1.09 ± 0.38   Yoked-workers Yoked-slackers Choice Omission Saline 0.45 ± 0.21 1.71 ± 1.25 5 mg/kg 0.45 ± 0.37 4.14 ± 3.65 10mg/kg 0.82 ± 0.30 2.86 ± 2.86 20mg/kg 0.91 ± 0.41 3.43 ± 2.48 Trials completed Saline 183.91 ± 5.68 181.43 ± 8.67 5 mg/kg 186.55 ± 4.03 193.71 ± 10.81 10mg/kg 183.36 ± 3.90 171.14 ± 20.38 20mg/kg 178.18 ± 6.13 182.71 ± 10.92   188  Appendix 10: Behavioural measures during satiation manipulations for the yoked-control task  Data shown are the mean ± SEM. Measure Condition Yoked-workers Yoked-slackers  High reward Low reward High reward Low reward Lever choice latency (s) Food restricted 1.98 ± 0.15 2.28 ± 0.24 1.74 ± 0.30 2.10 ± 0.35 Acute satiation 2.91 ± 0.18 3.65 ± 0.34 2.91 ± 0.18 4.03 ± 0.67 Chronic satiation 3.35 ± 0.11 4.01 ± 0.15 3.35 ± 0.11 5.11 ± 0.55 Collection latency (s) Food restricted 1.19 ± 0.56 1.37 ± 0.10 1.20 ± 0.9 1.34 ± 0.05 Acute satiation 1.43 ± 0.04 1.70 ± 0.12 1.51 ± 0.19 1.73 ± 0.17 Chronic satiation 1.77 ± 0.14 2.80 ± 0.51 1.91 ± 0.24 2.82 ± 0.39 Correct latency (s) Food restricted 1.19 ± 0.06 1.37 ± 0.10 1.20 ± 0.09 1.34 ± 0.08 Acute satiation 1.43 ± 0.04 1.70 ± 0.12 1.51 ± 0.19 1.73 ± 0.17 Chronic satiation 1.77 ± 0.14 2.80 ± 0.52 1.91 ± 0.24 2.82 ± 0.39   Yoked-workers Yoked-slackers Choice omissions Food restricted 0.73 ± 0.19 0.00 ± 0.00 Acute satiation 7.63 ± 1.89 5.86 ± 1.34 Chronic satiation 8.71 ± 1.16 6.63 ± 0.93 Trials completed Food restricted 184.09 ± 4.69 197.57 ± 6.98 Acute satiation 120.73 ± 10.79 118.71 ± 13.62 Chronic satiation 65.23 ± 9.63 43.85 ± 8.73        189  Appendix 11: Distribution of HR choice for all animals across all cohorts   The total number of subjects at baseline was 191 (workers: n = 102; slackers: n = 89) with an average HR choice of 69%. Again, note that the group designations were based on a mean split of a spectrum of choice behaviour and do not represent two distinct groups of animals. Such an approach to examining individual differences has a significant scientific precedence (e.g. Revelle et al., 1980) and allows for the examination of data from all animals, rather than discarding significant numbers of subjects and only examining those with extreme behavioural profiles. In the future, however, examining behaviour of only the top and bottom quartiles may provide even more pronounced distinctions in behavioural measures and underlying neurobiological profiles. 

Cite

Citation Scheme:

        

Citations by CSL (citeproc-js)

Usage Statistics

Share

Embed

Customize your widget with the following options, then copy and paste the code below into the HTML of your page to embed this item in your website.
                        
                            <div id="ubcOpenCollectionsWidgetDisplay">
                            <script id="ubcOpenCollectionsWidget"
                            src="{[{embed.src}]}"
                            data-item="{[{embed.item}]}"
                            data-collection="{[{embed.collection}]}"
                            data-metadata="{[{embed.showMetadata}]}"
                            data-width="{[{embed.width}]}"
                            async >
                            </script>
                            </div>
                        
                    
IIIF logo Our image viewer uses the IIIF 2.0 standard. To load this item in other compatible viewers, use this url:
http://iiif.library.ubc.ca/presentation/dsp.24.1-0166997/manifest

Comment

Related Items