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The neurocognitive consequences of the wandering mind : a mechanistic account of sensory-motor decoupling Kam, Julia 2014

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  THE NEUROCOGNITIVE CONSEQUENCES OF THE WANDERING MIND: A MECHANISTIC ACCOUNT OF SENSORY-MOTOR DECOUPLING     by  JULIA KAM  Bachelor of Arts, University of British Columbia, 2007 Master of Arts, University of British Columbia, 2010     A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE  REQUIREMENTS FOR THE DEGREE OF  DOCTOR OF PHILOSOPHY    in    THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES  (Psychology)    THE UNIVERSITY OF BRITISH COLUMBIA  (Vancouver)   August, 2014  © Julia Kam, 2014
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Abstract One unique characteristic of humans is our ability to mind wander – a state in which we engage in thoughts that are not directly tied to sensations from our surrounding environment. The Executive Function Model of mind wandering proposed the decoupling of our executive resources from the external environment is what facilitates the maintenance of these internal trains of thoughts. Accordingly, my dissertation aims to characterize how our neurocognitive processing of external stimuli waxes and wanes as our minds wander away from the task-at-hand. I present three sets of studies in this dissertation, each examining one specific aspect of neurocognitive engagement with the external environment, namely affective processing, behavioral performance monitoring, and attentional processing. Consistent with the Executive Function Model, my research indicates all three types of neurocognitive processing were attenuated during mind wandering episodes. This suggests mind wandering appears to disengage executive resources from our environment and direct them to inner streams of thoughts via this wide-ranging neurocognitive attenuation. One exception to this global pattern of attenuation of external processing is the detection of external stimuli that deviates from our expectations. Taken together, these observations suggest our ability to transiently decouple our thoughts from the external environment is integral to normal human neurocognitive functioning. A deeper understanding of this phenomenon may therefore inform strategies for regulating this mental experience so as to maximize their utility, and minimize their detrimental effects on our daily functioning and well-being. 
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Preface I prepared the content of this dissertation, with minor edits from Todd Handy and Colleen Brenner. The research presented in Chapters 2 to 4 was primarily conducted by myself, and I was responsible for the description of theory and summary of current research described in Chapters 1 and 5. All research studies have been published (see details below).  A version of Chapter 2 has been published as Kam, J.W.Y., Xu, J., & Handy, T.C. (2014). I donʼt feel your pain (as much): The desensitizing effect of mind wandering on the perception of othersʼ discomfort. Cognitive, Affective and Behavioral Neuroscience, 14, 286-296. I was responsible for study conception and design, data analysis and interpretation, and manuscript composition. J. Xu was primarily responsible for data collection. T.C. Handy was responsible for study conception and design, interpretation, and critical review of the manuscript. This study was approved by the University of British Columbiaʼs Research Ethics Board, H09-03295: Attention and Cognition.   A version of Chapter 3 has been published as Kam, J.W.Y., Dao, E., Blinn, P., Krigolson, O.E., Boyd, L.A. & Handy, T.C. (2012). Mind wandering and motor control: off-task thinking disrupts the online adjustment of behavior. Frontiers in Human Neuroscience, 6, 329. doi: 10.3389/fnhum.2012.00329. I was responsible for study conception and design, data analysis and interpretation, and manuscript composition. E. Dao & P. Blinn were primarily responsible for data collection. O.E. Krigolson, L.A. Boyd, 
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and T.C. Handy were responsible for study conception and design, interpretation, and critical review of the manuscript. Both experiments were approved by the University of British Columbiaʼs Research Ethics Board: H07-00056, and C03-0419: Spatial Attention in Visuomotor Processing.  A version of Chapter 4 has been published as Kam, J.W.Y., Dao, E., Stanciulescu, M., Tildesley, H., & Handy, T.C. (2013). Mind wandering and the adaptive control of attentional resources. Journal of Cognitive Neuroscience, 25, 952-960. I was responsible for study conception and design, data analysis and interpretation, and manuscript composition. E. Dao, M. Stanciulesu, & H. Tildesley were primarily responsible for data collection. T.C. Handy was responsible for study conception and design, interpretation, and critical review of the manuscript. Both experiments were approved by the University of British Columbiaʼs Research Ethics Board: H05-70344 (C05-0344): Control of Visual Attention by the Demands of Locomotion, and C03-0419: Spatial Attention in Visuomotor Processing.   A more extensive version of Chapter 5 has been published as Kam, J.W.Y., & Handy, T.C. (2013). The functional consequences of mind wandering: A mechanistic account of sensory and cognitive decoupling. Frontiers in Perception Science, 4, 725. doi: 10.3389/fpsyg.2013.00725. I contributed to theoretical development and manuscript composition. T.C. Handy was responsible for critical review of the manuscript.  
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Table of Contents  Abstract ...........................................................................................................................ii Preface ...........................................................................................................................iii Table of Contents ...........................................................................................................v List of Tables ................................................................................................................vii List of Figures..............................................................................................................viii Acknowledgements.......................................................................................................ix CHAPTER 1: Introduction ..............................................................................................1 Phenomenon of Mind Wandering.......................................................................................... 2 Individual Differences in Mind Wandering............................................................................. 3 The Neurocognitive Consequences of Mind Wandering.................................................... 4 Executive Function Model of Mind Wandering.................................................................... 6 Mind Wandering and Related Models of Attention.............................................................. 8 General Methodology........................................................................................................... 11 Measures of Task-Related Attention................................................................................... 11 Measures of Neurocognitive Consequences of Task-Related Attention............................. 14 Overview of Dissertation ..................................................................................................... 15 CHAPTER 2: The Desensitizing Effect of Mind Wandering on Affectively Salient Stimuli............................................................................................................................18 Introduction ........................................................................................................................... 18 Experiment 1 ......................................................................................................................... 23 Methods .............................................................................................................................. 23 Results ................................................................................................................................ 28 Discussion........................................................................................................................... 33 Experiment 2 ......................................................................................................................... 34 Methods .............................................................................................................................. 34 Results ................................................................................................................................ 36 Discussion........................................................................................................................... 39 General Discussion .............................................................................................................. 40 CHAPTER 3: Mind Wandering and Motor Control: Off-Task Thinking Disrupts the Online Adjustment of Behavior...................................................................................45 Introduction ........................................................................................................................... 45 Experiment 1 ......................................................................................................................... 47 Methods .............................................................................................................................. 47 Results ................................................................................................................................ 52 Discussion........................................................................................................................... 52 Experiment 2 ......................................................................................................................... 53 Introduction ......................................................................................................................... 53 Methods .............................................................................................................................. 54 Results ................................................................................................................................ 57 
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Discussion........................................................................................................................... 64 General Discussion .............................................................................................................. 65 CHAPTER 4: Mind Wandering and the Adaptive Control of Attentional Resources........................................................................................................................................70 Introduction ........................................................................................................................... 70 Experiment 1 ......................................................................................................................... 72 Methods .............................................................................................................................. 72 Results ................................................................................................................................ 76 Discussion........................................................................................................................... 78 Experiment 2 ......................................................................................................................... 80 Introduction ......................................................................................................................... 80 Methods .............................................................................................................................. 81 Results ................................................................................................................................ 84 Discussion........................................................................................................................... 90 General Discussion .............................................................................................................. 91 CHAPTER 5: General Discussion ...............................................................................97 Points of Consideration ....................................................................................................... 99 1. Validity of Subjective Reports of Attention ...................................................................... 99 2. Allocation of Executive Resources during Mind Wandering.......................................... 100 3. Relationship between Attentional Lapse and Mind Wandering..................................... 102 4. Relationship between the Default Mode Network and Mind Wandering ....................... 103 Potential Clinical Implications........................................................................................... 104 Future Directions ................................................................................................................ 109 1. Is Decoupling an Incidental or Necessary Process?..................................................... 109 2. What is the Time Course of Mind Wandering and its Associated Attenuation?............ 110 3. Can we Predict an Individualʼs Attentional State Online? ............................................. 110 4. Is there a Neural Network that Regulates these Attentional Fluctuations? ................... 112 5. What are Functionalities of Mind Wandering? .............................................................. 113 6. Is it Possible to Control these Fluctuations of Attention? If so, how? ........................... 115 Conclusion .......................................................................................................................... 116 References..................................................................................................................117  
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List of Tables Table 2.1. Amplitudes of ERP components in response to painful and neutral images in Experiment 1……………………………………………………………………………………32 Table 2.2. Behavioral ratings of painful and neutral images in Experiment 2…………...37  Table 4.1. Mean amplitudes for MMN (90-150ms) and N1 components (95-115ms)….87
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List of Figures  Figure 2.1. Visual stimuli in Experiments 1 and 2…………………………………………. . 24 Figure 2.2. ERP waveforms in response to painful and neutral images………………… 30 Figure 3.1. Task paradigm of Experiment 1………………………………………………... 48 Figure 3.2. The absolute change in time estimate (in percentage), with standard errors…………………………………………………………………………………………….59 Figure 3.3. P3 in response to correct and error feedback during on-task and mind wandering attentional states…………………………………………………………………..61 Figure 3.4. fERN in difference waveforms (error – correct) as a function of on-task versus mind wandering states………………………………………………………………..62 Figure 3.5. Conditional waveforms of on-task and mind wandering attentional states in response to correct and error feedback……………………………………………………..63 Figure 4.1. Task paradigm of Experiment 1………………………………………………...74 Figure 4.2. Reaction times for both volitional and reflexive spatial orienting tasks……. 77 Figure 4.3. ERP waveforms in response to (a) standard tones and (b) deviant tones, as well as the (c) difference waveforms in Experiment 2…………………………………….. 86   
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Acknowledgements  I would like to express my deepest gratitude to my family, friends, colleagues and collaborators for their support and encouragement during my pursuit of the doctoral degree. A special thanks to my supervisors, Todd Handy and Colleen Brenner, for their guidance throughout my academic training. Thank you to all the research assistants who have dedicated their time and effort to collecting data presented in this dissertation. Finally, I would also like to thank the Natural Sciences and Engineering Research Council of Canada (NSERC) for supporting me with their trainee award.   All research presented in Chapters 2 to 4 were supported by the Natural Sciences and Engineering Research Council (RGPIN 2009-298190) to TCH. 
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CHAPTER 1: Introduction Many of us have had the experience of being disengaged from a conversation, wherein our eyes are fixated on our partner but our minds have wandered off to another time and space. Perhaps we are reminiscing the last music gig we attended, or reflecting on the quality of the glass of red wine we just consumed, or as scientists, pondering upon our recent discovery of an unexpected finding. What happens to our processing of the conversation when we engage in our own internal trains of thoughts instead?   My dissertation aims to examine the functional consequences of mind wandering on the neurocognitive processing of the external environment. Chapter 1 introduces several critical issues concerning mind wandering and its neurocognitive study. Specifically, it begins with a focused review of the literature, introducing mind wandering as a cognitive phenomenon, as well as the current topical issues of mind wandering research. I will then describe the Executive Function model of mind wandering, which provides a framework for understanding resource allocation as our attention fluctuates between on-task and mind wandering states. Following this, I present a brief summary of the traditional models of attention, and describe how they relate to mind wandering. Finally, I will provide a brief description of my methodological approach to the scientific study of mind wandering and an overview of my research studies that will be discussed in this dissertation.  
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Phenomenon of Mind Wandering Mind wandering is defined as the phenomenon in which our minds drift away from demands of the immediate external environment to focus on the internal milieu. From planning for the weekend to fantasizing about our next vacation, we get lost in our own thoughts, especially when performing well-practiced tasks such as driving or washing dishes. In these instances, our minds become decoupled from stimuli in the external environment, which is a regular and periodic experience that occupies a notable portion of our mental life (e.g., Killingsworth & Gilbert, 2010; Klinger & Cox, 1987; Smallwood & Schooler, 2006).  Indeed, our proclivity to mind wander is sufficiently hard-wired that despite the best of our will power, these “decoupled” thoughts occur whether we want them to or not (Braboszcz et al., 2010).  Converging lines of evidence suggest we often engage in these decoupled thoughts, yet very little is known about how they emerge. Conceivably, these decoupled thoughts can occur in a spontaneous or deliberate manner (Christoff, 2012), and through different means. For example, one possibility is that a salient memory may reach a threshold of activation, which initiates a switch from attending to the external task to internal thoughts, suggesting a bottom-up influence on mind wandering occurrence. Another possibility is that we may intentionally allow a low-salience thought to intrude an ongoing task during a lapse in goal maintenance, which would suggest involvement of top-down cognitive control. While decoupled thoughts appear to be modulated by both top-down and bottom-up processes (Giambra, 1995), the exact 
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underlying mechanism of the emergence of decoupled thoughts have yet to be determined.   Individual Differences in Mind Wandering Not surprisingly, there are notable individual differences in the tendency to engage in task-unrelated thoughts. Several factors have been shown to influence rates of mind wandering. For one, age is a modulating factor, with young adults reporting higher levels of mind wandering compared to seniors (Giambra, 1989; Maillet & Rajah, 2013; Nagamatsu et al., 2013). Second, individuals with current personal concerns also report more mind wandering relative to those with few if any personal concerns (e.g. Klinger, 1975; Klinger & Cox, 1987). A third individual factor impacting mind wandering is oneʼs emotional state. In particular, dysphoric individuals reported higher levels of mind wandering compared to those who were not dysphoric (Murphy, Macpherson, Jeyabalasingham, Manly, & Dunn, 2013; Smallwood, OʼConnor, Sudberry & Obonsawin, 2007).    Finally, an individualʼs working memory capacity also plays a role in mind wandering frequency, a relationship that appears to be modulated by contextual factors including the difficulty of the ongoing task (Kane et al., 2007; McVay & Kane, 2009). In their study, individuals with lower working memory capacity tended to mind wander more when performing challenging or effortful tasks, whereas individuals with comparatively higher working memory capacity managed to stay on-task regardless of 
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the challenge or effort required (Kane et al., 2007). In summary, these aforementioned factors all contribute to how frequently individuals engage in mind wandering.  The Neurocognitive Consequences of Mind Wandering  In addition to factors that modulate mind wandering frequency, three major issues have dominated the empirical study of the experience of mind wandering itself. While much effort has been dedicated to the first two issues outlined below, my dissertation primarily focuses on the third issue, one that concerns the neurocognitive engagement with the external environment during mind wandering.  One issue of mind wandering concerns the neural network implicated in the occurrence of decoupled thoughts. Specifically, mind wandering has been linked to activity in the default mode network (DMN) localized in medial brain regions, including the ventral anterior cingulate cortex, precuneus and the temporoparietal junction (e.g., Christoff et al., 2009; Kirschner et al., 2012; Mason et al., 2007). For instance, activity in the DMN has been associated with conditions conducive to mind wandering, as indexed by decreasing demands of the ongoing task (Mason et al., 2007). Likewise, activation of regions within DMN was observed during direct subjective reports of mind wandering but not reports of on-task (e.g. Christoff et al., 2009; Kirschner et al., 2012).  Another issue concerns understanding the qualitative content of decoupled thoughts (Killingsworth & Gilbert, 2010; McVay, Kane, & Kwapil, 2009; Smallwood et al., 
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2011).  For example, in terms of what our minds focus on when they wander, one study found we were more likely to think about the future than the past or present, an effect that has been linked to strategic planning (Smallwood et al., 2011). These task-unrelated thoughts were also more likely to concern personal issues instead of unfocused daydreams (McVay, Kane, & Kwapil, 2009), underscoring the utility of these thoughts. Further, when mind wandering in their daily lives, individuals also reported being less happy (Killingsworth & Gilbert, 2010).  Notably, however, the consequences of mind wandering extend beyond the qualitative content of decoupled thinking itself (for a review, see Schooler et al. 2011).  In particular, my dissertation concerns understanding the impact decoupled thoughts have on how we process and respond to stimuli in the external environment (e.g. Barron et al., 2012; OʼConnell et al., 2009; Smallwood et al., 2008; Stawarcyzk et al., 2011). This third issue is pressing for two central reasons.  From a basic cognitive neuroscience perspective, mind wandering is now being recognized as a novel form of attentional selection, where our attention to the outside world is disrupted, and we no longer highlight or "select" certain external stimuli for preferential sensory or cognitive processing (e.g., Kam et al., 2011; Smallwood et al., 2008).  Likewise, from a clinical perspective, abnormal patterns of mind wandering and their effects on attention to the external environment have been implicated in conditions such as attention deficit/hyperactivity disorder (e.g., Sonuga-Barke & Castellanos, 2007).  In brief, this 
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highlights the importance of elucidating the neurocognitive basis of the decoupling process itself – an issue that is the main focus of my dissertation.  Executive Function Model of Mind Wandering If mind wandering does in fact alter our neurocognitive engagement of the external environment, what might be the underlying mechanism? The Executive Function Model of mind wandering (Smallwood & Schooler, 2006) provides a theoretical framework for how mind wandering may modulate neurocognitive processing. According to this model, executive resources are drawn away from the external task during mind wandering periods. This model proposed that mind wandering decouples our executive resources from the task-at-hand and redirects them to facilitate inner trains of thought. Executive resources are higher-level cognitive functions that regulate other thought and behavioral processes (e.g. Alvarez & Emory, 2006; Miyake et al., 2000). In particular, they are considered “general purpose control mechanisms that modulate the operation of various cognitive subprocesses and thereby regulate the dynamics of human cognition” (Miyake et al., 2000, p. 50).   Given that our executive processes are limited by a finite capacity attentional workspace (Dehaene & Naccache, 2001), we can not deeply engage in multiple streams of inputs simultaneously, rendering the decoupling process a necessary part of mind wandering. As an extension of the executive function model, the “decoupling hypothesis” predicts that the maintenance of inner trains of thoughts should attenuate 
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the sensory processing of external events (Smallwood, 2013), owing to the fact that these thoughts, and the subjective experiences they invoke in particular, rely on the same domain-specific processes engaged by events in the external world (e.g., Schooler et al., 2011; Schooler & Smallwood, 2006). This is consistent with the observation that internally-generated thoughts recruit the same domain-specific processes as stimulus in the external world (e.g. Schooler et al., 2011; Smallwood & Schooler, 2006; Smallwood, 2013). Similarly, visual imagery (e.g., Kosslyn, Thompson, & Alpert, 1997) and auditory imagery (e.g., Zatorre & Halpern, 2005) have been shown to activate many domain-specific regions of cortex that are also activated during interactions with the outside world. To the extent that task-unrelated thoughts parallel mental imagery, then both lines of evidence indicate that externally and internally guided thoughts can engage similar processes. In this view, internally guided thoughts would have the same impact on sensory-motor regions of cortex as mental imagery itself, leading to a general attenuation in processing of external events during mind wandering episodes. Therefore, if executive resources are critical to maintaining internal thoughts, then these resources become unavailable for processing external stimuli during periods of mind wandering.   Direct evidence in support for this model of mind wandering comes from both behavioral and neuroimaging findings. For example, executive processing as indexed by performance on a random number generation task was disrupted during task-independent thoughts (Teasdale et al., 1995), suggesting that mind wandering can 
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recruit executive resources that would normally be devoted to the external task. Moreover, mind wandering has been shown to recruit not only the brainʼs default mode network (DMN; Christoff et al., 2009; Kirschner, Kam, Handy, & Ward, 2012; Mason et al., 2007), but also the executive network as well (e.g., Christoff et al., 2009; Christoff, 2012). These findings support the idea that the facilitation of task-unrelated thoughts does indeed require executive functions. However, the neural basis underlying the monitoring and allocation of executive resources is largely unknown. For example, whether this allocation of executive resources to external vs. internal environments requires conscious effort or occurs automatically is not explicated in the executive function model of mind wandering. Further, the system that regulates the competition between external and internal inputs to enter consciousness and neural basis of this evaluation has yet to be determined. We return to this issue in the Conclusion section highlighting recent theoretical work proposing potential networks responsible for the allocation of executive resources.  Mind Wandering and Related Models of Attention Another theoretical issue to consider concerns the relationship between mind wandering and traditional models of attention. A recent theory proposed that at least two distinct controllers operate in parallel to ensure resilience of complex systems – one involved in initiating control on a moment-to-moment basis and the other responsible for providing stability over time (Dosenbach, Fair, Cohen, Schlaggar, & Petersen, 2008). These controllers are distinct not only in terms of their functional purpose, but also their 
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time scale of influence, and cortical networks. This theory is supported by empirical evidence in the context of top-down attentional control. Specifically, sensory activity in cortex has been shown to be modulated by at least two control systems – one associated with rapid shifts of selective visual attention (e.g., Corbetta, Miezin, & Dobmeyer, 1991; Hopfinger, Bouncocore & Mangun, 2000), and a second associated with slower temporal fluctuations in task-related attention (e.g., Kam et al., 2011). Notably, the stability of a dynamic cortical system is optimized when there are multiple controlling inputs operating at multiple scales of time (e.g., Honey et al., 2007).  According to what is known about these control systems, task-related attention – which encapsulates both on-task and mind wandering states – differ from selective attention in several ways (Dosenbach et al., 2008).  First, selective attention is involved in adjusting control of external stimuli on a trial-by-trial basis, whereas task-related attention concerns the maintenance of goals over time. Second, selective attention operates in a relatively rapid manner, on the order of milliseconds (e.g., Mangun & Hillyard, 1991); in contrast, task-related attention fluctuates in a comparatively slower manner, on the order of tens of seconds. Third, selective attention is mediated by a well-established network comprising of frontal and parietal regions of cortex (e.g., Corbetta, Miezin, & Dobmeyer, 1991; Corbetta et al. 1993; Corbetta & Shulman, 2002; Hopfinger, Buonocore, & Mangun, 2000). On the other hand, task-related attention seems to recruit a different set of neural networks, including precuneus, posterior cingulate cortex, 
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medial prefrontal cortex and bilateral temporoparietal junction (e.g. Christoff et al., 2009).  Another difference is that while mind wandering is finally gaining interest in the world of cognitive neuroscience research, much scientific effort has been historically devoted to selective attention. Accordingly, both theoretical development and empirical evidence for selective attention is much more comprehensive. In particular, these comparatively rapid fluctuations in selective attention can be construed in at least three ways – spatial selection, integrated competition, and attentional resources. For one, the attention spotlight model compares the attended location in a visual field to a spotlight, within which response time to the attended/selected stimuli is faster (Posner, 1980), and sensory-evoked responses to the attended/selected stimuli is enhanced in visual cortex (e.g., Mangun & Hillyard, 1991). Another perspective relates to the integrated competition hypothesis, which suggests that different objects compete to be represented in consciousness (Desimone & Duncan, 1995). Once selected, the perceptual processing of the selected object in our visual fields is facilitated by the cooperation of multiple brain systems working together to analyze the different properties of that object  (Duncan, Humphreys & Ward, 1997). Further, a third view of selective attention concerns the division of a single pool of attentional resources for different purposes as proposed in the capacity model of attention (Kahneman, 1973). Given that resources are finite, we can only attend to one stream of input at a time, and therefore few if any resources remain for the unselected input.  
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 Importantly, these accounts of selective attention are not mutually exclusive, and empirical evidence exists for all three models. These different approaches also interact to facilitate selective attention. For example, biased competition for graspable objects can draw spatial attention to that object in a bottom-up manner (Handy et al., 2003). In contrast, when top-down control orients spatial attention to the location of a graspable object, it modulates the biased competition responses to that object in visuomotor cortex (Handy et al., 2005). Of relevance, regardless of the underlying mechanism, ample research unequivocally indicates the impact that selective attention has on various types of cognitive processes. However, far less research has examined the effects of mind wandering on these processes. Therefore in my dissertation, I focused on the modulatory effects of task-related attention on the neurocognitive processing of stimulus in the external environment.   General Methodology Below, I provide a summary of how constructs used in this dissertation were measured.  Measures of Task-Related Attention Given the prevalence and potential impact of mind wandering on our everyday lives, researchers have recently gained interest in understanding the neural and functional correlates of mind wandering. In the context of an experiment, mind 
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wandering is characterized as attention away from the demands of the external environment, which generally refer to the processing required by the task-at-hand. As a direct consequence, several measures have been developed in an attempt to measure this ubiquitous experience and quantify its frequency of occurrence. For one, the most straightforward manner to investigate mind wandering is to directly ask participants to report their attentional state. This method of experience sampling has been used extensively in the literature in both experimental and observational studies. In an experimental setting, participants are asked at unpredictable intervals to report their attentional state in the moment while performing a task. The behavioral or neural responses occurring within a specified time window preceding the report are averaged and compared between on-task vs. mind wandering reports (e.g. Christoff et al., 2009; Kam et al., 2011; 2012; OʼConnell et al., 2008; Smallwood et al., 2008). In observational studies, participants are typically asked several times throughout the day to report their attentional state at that moment (e.g. Kane et al., 2007; Killingsworth & Gilbert, 2010; McVay, Kane & Kwapil, 2009). These attentional reports are then correlated with other measures, like emotional states (Killingsworth & Gilbert, 2010) and the context of the current task (Kane et al., 2007).   Second, several questionnaires have been used to explore the frequency and impact of mind wandering in day-to-day lives. For instance, the Mindful Attention Awareness Scale (MAAS; Brown & Ryan, 2003) and Attention-Related Cognitive Errors Scale (ARCES; Cheyne, Carriere, & Smilek, 2006) are both self-report questionnaires 
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that index the propensity to attentional lapses and range of errors committed as a result of failures of attention, respectively. Another example is the Dundee Stress State Questionnaire (DSSQ; Matthews et al., 1999), which has been commonly used as a retrospective measure of mind wandering experiences during an external task (e.g. Smallwood, OʼConnor, & Heim, 2005).  Responses on these questionnaires have been associated with other measures. For example, propensity towards attentional lapses or mind wandering was associated with depressed mood (e.g. Carriere, Cheyne, & Smilek, 2008; Smallwood, OʼConnor, Sudberry, & Obonsawin, 2007), and impaired performance in a sustained-attention task (e.g. Cheyne, Carriere, & Smilek, 2006).   Lastly, performance measures like reaction times (RTs) and error rates have been used as an indirect indicator of mind wandering (e.g. Weissman, Roberts, Visscher, & Woldorff, 2006; Weissman, Warner, & Woldorff, 2009).  This is based on previous findings that both RTs and error rates tend to increase during periods of mind wandering in sustained attention tasks (Robertson et al., 1997; Smallwood et al., 2004).   For the purpose of my research, I adopted the experience sampling approach to examine mind wandering and its frequency of occurrence. While questionnaires and performance measures have been used in numerous experiments, experience sampling is the most direct measure. In particular, this methodology has been used to demonstrate reliable and replicable differences in neurocognitive functioning between on-task and mind wandering states (e.g., Christoff et al., 2009; Franklin et al., 2011; 
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Kam et al., 2011; Kirschner et al., 2012; Mason et al., 2007; McKiernan et al., 2006; Smallwood et al., 2008; Starwarcyzk et al., 2011). Participants are generally asked at unpredictable intervals to report their attentional state in the moment while performing an external task. In using participantsʼ report of attentional state to categorize their behavioral and neural responses occurring within a specified time window preceding the report, this dichotomous self-report classification of attentional state has shown a systematic modulation of both sensory (e.g., Kam et al., 2011) and cognitive processing (e.g., Smallwood et al., 2008) during on-task vs. mind wandering states.  Measures of Neurocognitive Consequences of Task-Related Attention  As my research aims to identify the functional consequences of mind wandering on neurocognitive processing of external events, my methodological approach includes both measures of neural processing and behavioral performance that may differentiate between attentional states.   Event-Related Potentials  We used event-related potentials (ERPs), which are signal-averaged epochs of electroencephalogram time-locked to the onset of condition-specific stimulus or motor events, to index sensory and cognitive level stimulus processing. ERP offers a high degree of resolution in the amplitude (or voltage) domain, giving them an ability to reveal differences in the intensity of neurocognitive processing between conditions, or specifically in my studies, attentional states. Further, ERP provides exquisite temporal 
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resolution with respect to the nature of on-going neurocognitive functions on the order of milliseconds, allowing us to infer the time course of processing (Handy, 2005).  Behavioral Measures  Behavioral indices were also used in my studies as they provide an objective measure of performance, which can complement the subjective reports of attention. The main behavioral measures used in my studies are reaction times and ratings of visual stimuli.  Overview of Dissertation My dissertation centers on the hypothesis that mind wandering disrupts a broad range of neurocognitive processing of stimuli in the external environment. Throughout this dissertation, I will provide evidence that mind wandering systematically alters our processing of external stimuli. The importance of my research lies in its theoretical and clinical impact. From a cognitive neuroscience perspective, mind wandering modulates a wide range of sensory and cognitive processes, an understanding of which may help shed light on the control mechanism involved in switching between externally and internally oriented attention.  In terms of clinical applications, a solid understanding of this phenomenon may provide a novel perspective in elucidating the processes underlying cognitive symptoms of clinical disorders – an issue to which we return to in the General Discussion.  
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The three following chapters of my dissertation examined three distinct questions concerning the neurocognitive consequences of mind wandering. In the second chapter, I examined whether our affective response changes as a function of our task-related attention using ERPs. In two experiments, I observed an attenuated neural response (as indexed by the P3 ERP component) and reduced saliency ratings to affectively salient stimuli, during mind wandering relative to on-task episodes.  The third chapter examined whether mind wandering disrupts our motor task performance monitoring. I first demonstrated that visuomotor tracking errors were greater during mind wandering states by having participants track a moving target across the screen and occasionally report their attentional state. In a second experiment, I found mind wandering also impaired performance monitoring, as indexed by a reduced feedback error-related negativity ERP component. Taken together, the novelty of these results lies in the impact of mind wandering on disengaging us from monitoring and adjusting our behavioral outputs.  The fourth chapter examined whether mind wandering differentially impacted two distinct types of attentional processing: attentional orienting and deviance detection. In the context of the first experiment, while there are many ways to conceptualize types of attentional orienting, I refer to the controlled shifting of attention as volitional orienting, and the automatic shifting of attention as reflexive orienting. I found that mind wandering was associated with impairments in both volitional and reflexive attentional orienting, as 
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indexed by slower reaction times. In contrast, the magnitude of sensory response to deviant tones in an auditory oddball task did not significantly differ between the two attentional states. These findings indicate mind wandering does not disrupt attentional systems in a unitary manner. While mind wandering impaired attentional orienting, the sensory processing of rare, deviant events in the environment appears to be preserved during mind wandering.  In summary, my dissertation presented the extent of the impacts of mind wandering, whose effects ripple widely extending to a broad array of neurocognitive functions. The experiments outlined above established the range of affective, motor, and attentional processes that are attenuated during mind wandering states, all of which play a critical role in the maintenance of ongoing internal thoughts. My dissertation concludes with an integrative discussion of the relationship between mind wandering and allocation of executive resources, including some points of considerations, clinical implications and issues for future investigations in this increasingly influential line of research.     
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CHAPTER 2: The Desensitizing Effect of Mind Wandering on Affectively Salient Stimuli  Introduction Mind wandering is the ubiquitous phenomenon when our thoughts drift away from the external environment to focus on the internal milieu (e.g., Schooler et al., 2011; Smallwood & Schooler, 2006). Given that we mind wander frequently (Killingsworth & Gilbert, 2011) and it appears to be integral to normal human brain function (Smallwood & Schooler, 2006), there has been growing interest in understanding the functional consequences of the wandering mind, and specifically, elucidating how our neurocognitive processes change as our thoughts drift away from the current task at hand.   In this regard, mind wandering can be seen as having two primary effects at the neural level. First, modern neuroimaging methods have revealed that there are a core set of networks in the brain that oscillate in their activity over time, depending on the nature of the task being performed and other key variables (e.g., Dosenbach et al., 2008; Corbetta & Shulman, 2002; Gusnard & Raichle, 2001).  As we now know, mind wandering is one of these core determinants, in that it up-regulates activity in the Default Mode Network (DMN; Gusnard & Raichle, 2001), an effect that has been shown using both fMRI (e.g., Christoff et al., 2009; Mason et al., 2007; Starwarczyk, Majerus, Maquet, & DʼArgembeau, 2011) and EEG (e.g., Kirschner, Kam, Handy, & Ward, 2012).  
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Moreover, there is an anti-correlation in activity between the DMN and task-related networks (e.g. Fox et al., 2005), a finding that supports the hypothesis that the DMN may facilitate mind wandering and the internal trains of thought it generates by disengaging our attention from the external environment (e.g., Schooler et al., 2011).  Second, and more directly related to our study, mind wandering also actively attenuates the neural processing of external sensory inputs, an effect that goes hand-in-hand with up-regulation of activity in the DMN.  For example, sensory-evoked responses to task-irrelevant stimuli decrease in both visual and auditory cortex (e.g., Braboszcz & Delorme, 2011; Kam et al., 2011), suggesting that like top-down attentional control, mind wandering has a direct impact on cortical sensory gain control. Further upstream, our cognitive analysis of target events also diminishes (e.g., Barron, Greer & Smallwood, 2011; O'Connell et al., 2009), an effect that is independent of any concomitant sensory attenuation (Smallwood et al., 2008). These disruptions in stimulus processing, which co-occur with DMN up-regulation, effectively shut out the external world and allow our thoughts to drift off to other times, places and events––the cognitive hallmarks of the subjective mind wandering state (e.g., Smallwood & Schooler, 2006; Smallwood et al., 2011).  Given these neural effects, a key unanswered question concerns whether the qualitative nature of the in-coming stimulation itself affects its extent of attenuation during mind wandering. In particular, the stimuli used to examine these attenuating 
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sensory and cognitive effects of mind wandering have been relatively impoverished so far with respect to their contextual representation or information content; for instance, numbers and letters were used in the sustained attention to response task (e.g., Kam et al., 2011; Smallwood et al., 2008), and colored, geometrically-patterned visual stimuli were used in oddball (e.g., Barron et al., 2011) and continuous temporal expectancy tasks (e.g., OʼConnell et al., 2009). Yet the extent to which ––and perhaps if–– the qualitative content of task-relevant stimuli influences how it is attenuated during mind wandering remains unknown. Our goal here was to examine whether the attenuating effects of mind wandering extend to more naturalistic stimulus inputs, and specifically, those having some measure of affective saliency.   To address this question, we chose a stimulus set that was both comparatively high in ecological validity and had a moderate degree of affective content (Fan & Han, 2008; Gu & Han, 2007). This set contains pictures of hands in the first person perspective in painful or neutral situations (see Figure 1). In asking participants to rate these pictures while recording the event-related potentials (or ERPs) they engendered, Fan and Han (2008) not only identified two successive stages of neural processing that are engaged by the painful and neutral pictures, they also found differential affect-related associations with each of these stages. Specifically, the earlier, more automatic stage of processing was found to index the emotional sharing of the painful experience, which correlated with subjective ratings of unpleasantness. Alternatively, the later, more controlled stage of processing was found to index the cognitive or contextual evaluation 
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of the experience, which appears to be directly modulated by both task demands (Fan & Han, 2008), as well as shifts in the visual perspectives subjects took during the task, or whether they adopted a first-person or third-person perspective (Li & Han, 2010). This dissociation in the two stages of processing triggered by painful images was ideal for our study, in that we could investigate whether mind wandering would affect either or both levels of processing of affectively salient stimuli. Of particular interest here was whether these kinds of affect-related responses are immune to the impacts of mind wandering, or whether being disengaged from the external environment leads to transient reductions in our proclivity for sensitivity to perceived pain.  Given the available evidence from the affective literature, the possible effect of mind wandering on the processing of painful images remains uncertain. On one hand, affective stimuli appear to automatically elicit or trigger heightened attention (Armony & Dolan, 2002; Frank et al. 2012). For example, emotional stimuli increased ERP response relative to neutral stimuli (Delplanque et al., 2004; Foti et al., 2009), and enhanced the fMRI BOLD signal in neural regions involved in emotional processing (Kensinger & Schacter, 2006; Morris et al., 1998). This suggests that stimuli having emotional content may have privileged access to neural processing relative to affectively-neutral stimuli. On the other hand, recent studies have also found top-down modulation of our neural response to perceived pain of others (Fan & Han, 2008; Gu & Han, 2007). That is, top-down attentional control appears to impact the strength of the late but not early ERP response to the perceived pain of others (Fan & Han, 2008; Li & 
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Han, 2010). Of relevance, the question of interest concerned whether mind wandering would modulate this later evaluative response in a manner akin to top-down attentional control. Given the similarities between effects of mind wandering and top-down control (c.f. Smallwood, Brown, Baird, & Schooler, 2012), we hypothesized that mind wandering would attenuate the response to painful images in a manner similar to the modulatory effects of top-down attention––that is, the effect would be present in the late but not early ERP response to painful images.  In the first experiment, we thus recorded ERPs while participants viewed a serial stream of visual images of hands pictured from a first-person perspective that were in either a painful or comparable neutral situation (e.g., shut in a drawer vs. next to a drawer).  During the task, participants were also prompted at random intervals to report on their attentional state as either “on-task” or “mind wandering”.  To determine the impact of mind wandering on the sensitivity to others in pain, we compared the ERP response to painful and neutral images in the interval immediately preceding on-task vs. mind wandering reports. At issue was whether the late vs. early responses to perceived pain as defined by Fan and Han (2008) would selectively attenuate during mind wandering. If so, it would indicate that our responses to affectively-salient stimuli are in fact labile to control by slow fluctuations in task-related attentional states.    
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Experiment 1  Methods Participants A total of 19 individuals (12 females; M= 22.63 years, S.D.=4.89) participated in the study. All were right handed, had normal or corrected-to-normal vision, gave written informed consent, and received $20 for their participation.  All procedures and protocols of this study were approved by the UBC Behavioral Review Ethics Board.   Stimuli and Paradigm The visual stimuli and primary task replicated Fan and Hanʼs (2008).  As shown in Figure 2.1A, the stimuli consisted of 40 cartoon-ized images of one or two hands viewed from a first-person perspective in everyday situations. Half of these images showed the hand(s) in painful situations and the other half of these images showed the hand(s) in similar, but pain-neutral situations. Each image was 10.6 cm by 11.2 cm, and was viewed on an 18 in. color monitor at a distance of 80 cm.  Each trial began with an image presented for 300 ms, followed by a randomly jittered inter-trial interval between 1500-1900 ms. During that time, participants made a forced-choice decision regarding whether the image showed hands in a painful or pain-neutral situation; responses were made with the two thumbs, with the response mapping counterbalanced between participants.   
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Figure 2.1. Visual stimuli in Experiments 1 and 2. Examples of (a) cartoon-ized images of hands in painful and pain-neutral situations used in Experiment 1, and (b) similar but naturalistic images used in Experiment 2.   Task-Related Attention Our approach to determining whether or not participants were in a mind wandering state at any given moment was based on experience sampling. Considered to be a direct measure of mind wandering, experience sampling relies on our ability to reliably report whether or not our attention is focused on the task at hand (e.g., 
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McKiernan, DʼAngelo, Kaufman, & Binder, 2006; Smallwood, Baracaia, Lowe, & Obonsawin, 2003; Smallwood, Mcspadden, & Schooler, 2008; see Gruberger et al., 2011, for a review). In this method, participants were instructed to verbally report their attentional state when prompted as either being on-task or mind wandering. To facilitate this, participants were provided with descriptions of these attentional states prior to testing: on-task states were defined as when oneʼs attention is firmly directed towards the task, and mind wandering states were defined as when oneʼs attention has drifted away from the task.   We used the experience sampling method because in using the report to categorize a participantʼs attentional state in the 10-15 seconds immediately prior to the report, the methodology has been used to demonstrate replicable differences in neurocognitive functioning between on-task and mind wandering states (e.g., Christoff et al., 2009; Franklin et al., 2011; Kam et al., 2011; Kirschner et al., 2012; Mason et al., 2007; McKiernan et al., 2006; Smallwood et al., 2008; Starwarcyzk et al., 2011). Specifically, this dichotomous self-report classification of attentional state has shown a systematic down-regulation of both sensory (e.g., Kam et al., 2011) and cognitive processing (e.g., Smallwood et al., 2008) during mind wandering vs. on-task states, and further, an up-regulation of activity in the brainʼs DMN (e.g., Christoff et al., 2009; Kirschner et al., 2012; Starwarczyk et al., 2011).    
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Accordingly, our question in the current study was whether comparable down-regulation of processing of affectively-salient stimulus also occurs when this self-report, experience sampling approach is used to classify attentional state.  Attentional reports were thus recorded at the conclusion of each trial block by the investigator, and these reports were then used to sort ERP data based on on-task and mind wandering states. In order to maximize the variability of attentional states and minimize predictability of when an attentional report would be required, the duration of each trial block was randomly varied between 30 and 90 s, or 15 to 45 trials (c.f. Kam et al., 2011; Kam et al., 2012; Smallwood et al., 2008).   Electrophysiological Recording and Analysis   During task performance, electroencephalograms (EEGs) were recorded from 64 active electrodes mounted on a cap in accordance to the International 10-20 system using a Biosemi Active-Two amplifier system. Two additional electrodes located over medial-parietal cortex (Common Mode Sense and Driven Right Leg) were used as ground electrodes.  All EEG activities were recorded using a high-pass filter of 0.05Hz, digitized on-line at a sampling rate of 256 samples-per-second. To ensure proper eye fixation and allow for the removal of events associated with eye movement artifacts, vertical and horizontal electrooculograms (EOGs) were also recorded – the vertical EOGs from an electrode inferior to the right eye, and the horizontal EOGs from two electrodes on the right and left outer canthus.  Offline, computerized artifact rejection was used to remove trials during which detectable eye movements and blinks occurred 
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from subsequent signal averaging. These eye artifacts were detected by identifying the maximum voltage values on all recorded EOG channels from -200 to 800 ms post-stimulus for each epoch if that value exceeded 200µV, a value estimated to capture all blinks, saccades and other eye movements. An average of 29% of the total number of trials across participants were rejected due to these signal artifacts. The percentage of trials rejected did not significantly differ between painful and neutral images (p = .508), nor did they significantly differ between on-task and mind wandering states (p = .122). Subsequently, all ERPs were referenced to the average of the left and right mastoid signals, and subjected to a low-pass Gaussian filter at 25.6 Hz to eliminate any residual high-frequency artifacts in the waveforms.  The resulting ERPs were used to generate grand-averaged waveforms. All ERP data analyses reported below were based on mean amplitude measures using repeated-measures ANOVAs, with specific time-windows of analyses centered on the components of interest as identified in the grand-averaged waveforms. These measures were all taken relative to a -200 to 0 ms pre-stimulus baseline. To compare ERP responses between on-task and mind wandering states, we only included the 6 images in our ERP averages that were presented in the 12 s preceding each attentional report (on-task vs. mind wandering) –– a time window we have used previously with ERP data (e.g., Kam et al., 2011; Kam et al., 2012; Kam et al., in press; Kirschner et al., 2012; Smallwood et al., 2008) that is designed to maximize the number of events that can be included in the ERP averages while still maintaining a reasonable fidelity to the actual attentional report (i.e., as the time window increases, the signal-to-noise ratio of 
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the ERP averages improves, but the validity of the attentional report for individual events decreases).  We averaged separately the painful and neutral images occurring within this time period. Results Attentional Reports Participants completed an average of 48 trial blocks, of which 57% ended with an on-task report and 43% ended with a mind wandering report (SD=16%). While the verbal report of attentional states may have increased the risk of demand characteristics thereby potentially affecting the validity of the reports, the proportion of on-task vs. mind wandering reports have been consistent across studies regardless of the methodology used, whether participants provided a response verbally to the experimenter or through button press (Christoff et al., 2009; Kam et al., 2011; Kirschner et al., 2012; Smallwood et al., 2008).   Behavioral Ratings Towards confirming that the painful images were in fact perceived as painful, participants responded "painful" on 84% of the painful images presented, but only 12% of the neutral images presented.  We also examined the extent to which these judgments varied as a function of whether attention was on-task (Painful: M=85.8, S.D.=7.0; Neutral: M=90.1, S.D.=9.0) or off-task (Painful: M=84.2, S.D.=9.5; Neutral: M=87.7, S.D.=11.6), and found that the accuracy of the ratings did not significantly differ between attentional states for either image type (p > .050). 
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 Electrophysiology ERP waveforms are shown in Figure 2.2.  ERP data analyses focused a priori on two portions of these waveforms––an initial positive-going response to painful images beginning by 140 ms post-stimulus at frontal/central midline electrode sites, and the subsequent P3 response beginning about 300 ms post-stimulus, consistent with Fan and Han's (2008) early and late responses to pain, respectively.  In examining our waveforms, we observed that there were two parts to the late response: 1) the ascending phase of the P3 at frontal/central midline sites, in line with Fan and Hanʼs (2008) choice of electrode sites and time window for their cartoon-ized stimuli, and 2) the P3 component itself maximal over central/parietal midline sites. Repeated-measures ANOVAs included factors of attentional state (on task vs. mind wandering) and image type (painful vs. neutral), as well as electrodes; however, for brevity, no main effects or interactions involving electrodes are reported. The specific electrodes included in each analysis are stated in the corresponding sections below.  The mean amplitudes and standard errors of the mean for the initial response to both image types across a 140-180 ms post-stimulus time window at electrode sites Fz, FCz, and Cz are shown in Table 2.1.  Neither the main effects of attention nor image type were significant (p > .500). Consistent with our hypothesis, the interaction was also not significant (F(1,18) = 1.56, p = .227). 
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Figure 2.2. ERP waveforms in response to painful and neutral images.  Averaged ERP waveforms for each image type are presented as a function of on-task and mind wandering states at electrode sites Fz, FCz, Cz, CPz, and Pz. Only the ascending phase of the later component (300-500ms) elicited by painful stimuli was significantly attenuated during periods of mind wandering relative to on-task, however this was not observed for neutral stimuli.     
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The mean amplitudes and standard errors of the mean for both parts of the later response to both image types across a 300-500ms post-stimulus time window at Fz, FCz, and Cz, as well as a 550-700ms post-stimulus time window at electrode sites Cz, CPz, and Pz are also shown in Table 2.1. In examining the ascending phase of the later response at 300-500ms, it appeared that there was a significant positive-going response in this time window that was specific to painful images in the on-task condition.  This data pattern was confirmed by an omnibus ANOVA that revealed significant main effects of attentional state  (F (1,18) = 7.26, p = .015) and image type (F (1,18) = 7.08, p = .016), as well as a significant interaction (F (1,18) = 7.57, p = .013). Follow-up analyses demonstrated an effect of attentional state for painful (F (1,18) = 17.53, p < .001, ηp2 = .493), but not for neutral stimuli (F (1,18) = 1.06, p = .317, ηp2 = .056).  In short, the initial positive-going response to painful images was significantly attenuated in the period immediately preceding mind wandering vs. on-task attentional reports.   We performed the same analysis on the P3 component of the late response across a 550-700 ms post stimulus time window. Like the ascending phase of this late response to painful images, it appeared that there was a reduced positive deflection, resembling a late latency P3, in this time window that was specific to painful images in the mind wandering condition.  However, neither of the main effects of attentional state (F (1,18) = 3.23, p = .089) and image type (F (1,18) = 2.58, p = .126), nor the interaction were significant (F (1,18) = 3.17, p = .092).   While there was a trend towards reduced 
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attention to painful images in this time window during mind wandering, the effect did not reach significance.   Table 2.1. Amplitudes of ERP components in response to painful and neutral images in Experiment 1. The mean amplitudes (and standard errors of the mean) of the early and late components of empathetic response, at electrode sites Fz, FCz, Cz, and Cz, Cpz, and Pz respectively, as a function of on-task and mind wandering states are reported below.     Attentional State Component Stimuli Electrodes On Task Mind Wandering      Early  Painful Fz -1.07 (0.330) -1.45 (0.487) (140-180ms)  FCz -0.86 (0.327) -1.11 (0.507)   Cz -0.44 (0.296) -0.57 (0.514)  Neutral Fz -1.44 (0.483) -1.18 (0.441)   FCz -1.10 (0.460) -0.64 (0.445)   Cz -0.65 (0.417) -0.43 (0.419)      Painful Fz -0.28 (0.422) -1.52 (0.536)  FCz 0.38 (0.471) 0.77 (0.546) Late,  ascending slope  (300-500ms)  Cz 1.91 (0.485) 0.75 (0.508)  Neutral Fz -1.35 (0.524) -1.73 (0.679)   FCz -0.76 (0.529) -0.90 (0.690)   Cz 0.83 (0.526) 0.20 (0.615) 
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   Attentional State Component Stimuli Electrodes On Task Mind Wandering      Painful Cz 6.84 (0.895) 5.84 (0.912)  CPz 7.06 (0.863) 6.25 (0.815) Late, P3 (550-700ms)   Pz 6.39 (0.912) 5.60 (0.904)  Neutral Cz 6.11 (1.017) 5.65 (0.879)   CPz 6.30 (0.975) 6.14 (0.913)   Pz 5.54 (1.004) 5.29 (0.907)  Discussion The results from Experiment 1 thus indicated that only the later response to perceiving othersʼ pain as identified by Fan and Han (2008) is labile to attenuation during periods of mind wandering. That the early response did not show any attentional modulations suggests the more automatic processing of affectively-salient stimuli occurs regardless of whether attention was on-task or off-task. Importantly, the attentional effects in the ascending phase of the later response was measured via modulations in the ERPs elicited by the painful but not neutral visual images, indicating this effect was not simply due to general sensory attenuation present during mind wandering states (e.g., Kam et al., 2011). While there was only a trend in the effect of mind wandering on the P3 component of the later response to the painful images, the overall data pattern supports the hypothesis that mind wandering can in fact modulate at least some 
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aspects of affectively-salient stimulus processing. Further, that the accuracy rate of the painful judgments did not significantly differ between the two attentional states suggests that this neural response reflects the processing of images recognized as painful vs. neutral, instead of simply the recognition of an ambiguous situation.   This finding, however, also raised an important question: to what extent does mind wandering actually affect our subjective perception of other peoplesʼ pain?  In particular, neither the ERP nor behavioral data from Experiment 1 provide much insight into the depth or range of the subjective affective experience of the observed pain in others, and specifically, how mind wandering may have altered it. As such, the goal of our second experiment was to examine whether the effect of mind wandering extends to self-report measures of perceived pain in others.  Specifically, in Experiment 2, participants were asked to rate the painfulness of hand images on a 5-point Likert scale while we again asked for task-related attentional reports at trial block completion.  If mind wandering can in fact modulate our sensitivity to pain in others, it predicted that pain ratings should selectively decrease for painful images immediately preceding mind wandering vs. on-task attentional reports.   Experiment 2    Methods Participants  37 participants (25 females; M=22.3 years, S.D.=3.31) completed the experiment 
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in exchange for $5. All were all right handed, had normal or corrected-to-normal vision, and gave written informed consent.  All procedures and protocols of this study were approved by the UBC Behavioral Review Ethics Board.   Stimuli and Paradigm   The visual stimuli were similar to those used in Experiment 1, with the following exceptions: there were a total of 400 images (200 of hands in painful positions, 200 of hands in comparable but neutral conditions) that were actual photographs rather than cartoon-ized images, and each image was 13.1 cm by 9.6 cm, as shown in Figure 2.1B. We created this larger image set for two reasons. First, we wanted to provide the participants with a variety of images to rate, rather than having them continuously rate the same 40 images used in Experiment 1. Second, given that Fan and Han (2008) reported a larger affective response using more naturalistic vs. cartoon-ized images, and that this effect impacted the ratings of perceived pain, we decided to use images that would capitalize on these effects.  Each trial began with an image presented for 400 ms, followed by a rating screen for 2400 ms that prompted participants to rate how painful the image looked on a scale of 1 ("not painful") to 5 (“very painful”).  The inter-trial interval was randomly varied from 150-250 ms. As in Experiment 1, participants were asked to report their attentional state at the end of each trial block as either being on-task or mind wandering, and given definitions of these states prior to testing. Each testing session lasted approximately 30 
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minutes, and each of the 400 images was presented only once.  The session itself was broken down into 20 trial blocks, with each block varying in duration from 45 to 75 s (i.e. 15 and 25 trials).  Results Attentional Reports To ensure that we had reliable ratings estimates in each condition for each participant, we limited data analysis to only those subjects who reported three or more on-task and mind wandering reports, which in the minimum case would correspond to approximately 20 ratings in the given attentional condition.  This criterion excluded 14 participants, reducing actual data analyses to a final sample of 23 participants (14 females; M=22.4 years, S.D.=3.41).  Averaged across these participants, 65% of trial blocks ended with an on-task report and 35% ended with a mind wandering report (SD=15%). Based on the assumption that our neutral stimuli were indeed perceived as non-painful, and that they would be predominantly rated as “not painful”, we predicted a floor effect of the neutral stimuli a priori based on our results from Experiment 1. As such, while we report both omnibus ANOVAs showing interactions as well as follow-up analyses, our interpretation would primarily focus on the painful images only.    Behavioral Ratings As in Experiment 1, we averaged the ratings separately for painful and neutral stimuli within 12 s preceding attention reports, as shown in Table 2.2. It appeared that 
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there was a reduction in ratings during mind wandering states for the painful but not neutral images. While the main effect of stimulus type was significant (F(1,22) = 82.66 p < .001), the main effect of attentional state (F(1,22) = 3.15, p = .090) did not reach significance. Further, the interaction was only near significant (F(1,22) = 3.57; p = .072).  Nevertheless, planned t-tests indicated behavioral ratings significantly decreased during mind wandering for painful images (t(22) = 2.17; p = .041, d = .46) but not neutral images (t(22) = 0.14, p = .889, d = .03).    Table 2.2. Behavioral ratings of painful and neutral images in Experiment 2. The mean behavioral ratings (and their standard errors of the mean) of each image type as a function of attentional state are reported below. The ratings were averaged across 9 s and 12 s immediately preceding an attentional report.     Attentional State Time Window Stimuli On Task Mind Wandering 9 seconds Painful 3.32 (0.151) 3.01 (0.174)  Neutral 1.58 (0.121) 1.57 (0.141)     12 seconds Painful 3.29 (0.144) 3.05 (0.167)  Neutral 1.60 (0.122) 1.59 (0.144) Note: Behavioral ratings ranged from 1 (not painful) to 5 (extremely painful).  Although our initial behavioral results suggest that mind wandering appears to reduce our sensitivity to othersʼ pain, there was only a trend towards an interaction between attention and stimulus type despite a significant attention effect for painful 
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images shown in the 12 s preceding attention reports. We then wanted to examine how the effect of mind wandering on ratings may change as data are averaged across a decreasing distance in time from the attentional report at trial block completion. While we initially used a 12 s analysis time window to maintain consistency with Experiment 1, we were no longer constrained by the need to compute ERP waveforms requiring large numbers of events for averaging. As such, in order to maximize the validity of the attentional reports for images analyzed, we restricted the analysis of ratings to images presented in the 9 s preceding attentional reports as shown in Table 2.2. This reduction in time (i.e. 3 s) corresponds to one less trial/rating to be included in the averaged data. We therefore repeated our initial analyses using ratings averaged 9 s preceding attentional reports, and found significant main effects of stimulus type (F(1,22) = 83.87 p < .001) and attentional state (F(1,22) = 4.13, p = .054), as well as a significant interaction (F(1,22) = 6.03, p = .022). Follow-up t-tests confirmed a reduction in ratings during mind wandering for painful images (t(22) = 2.53, p = .019, d = .54) but not neutral images (t(22) = 0.18, p = .862, d = .04).   Control Analysis As an additional control issue, we wanted to consider task fatigue over time as a possible confound in our data. In particular, with task fatigue undoubtedly growing over time, might participants have been more inclined towards mind wandering states later during testing than earlier, thereby confounding "mind wandering" with "habituation" or "general fatigue" states (and by extension, biasing "on-task" data towards the outset of 
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the testing session, prior to the onset of task fatigue)?  To examine this possibility, we broke down the percentage of on-task reports by quartile across the testing session, which were 84%, 75%, 53% and 46%, respectively.  This indicated that there was indeed an increase in mind wandering reports over time, a finding consistent with the possibility of increased task fatigue over the testing session. Nevertheless, given the relatively equal distribution of subjective reports in the third and fourth quartiles of data collection, we did a sub-analysis of the pain ratings in the second half of this experiment and found a significant decrease for painful images during mind wandering (on-task = 3.15, mind wandering = 2.68; p < 0.001) but not for neutral images (on-task = 1.41, mind wandering = 1.37; p > .05).  This control analysis replicates our main finding, while suggesting that although fatigue effects may indeed be present in our data, they can not alone account for the effects of mind wandering we report.  Discussion These results were thus consistent with the hypothesis that mind wandering significantly attenuates our sensitivity to the perceived pain of others. We observed that the average ratings for neutral images were slightly above 1 (“not painful”; M=1.65, SD=0.65), confirming our assumption and validating the neutral stimuli. This suggests that some neutral photos were given a rating higher than 1, which is understandable because the position of the hand in some of these photos may look slightly uncomfortable to some participants even though they may be significantly less painful than the painful images.   Importantly, that there was variability in this rating and 
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specifically that not all neutral images were rated consistently as 1 (“not painful”) during the mind wandering state indicate that participants were actually performing the task; that is, if they were just mindlessly rating the images, they would have just given the same response to all neutral images, and the identical, higher pain ratings to all painful images. Given these findings, it thus suggests that mind wandering does in fact decrease our sensitivity to the observed pain or discomfort of others.  We discuss the broader implications of our findings below.  General Discussion  The current study examined whether mind wandering modulates the processing of affectively-salient stimuli.  Towards addressing this issue, in Experiment 1 we found that the late ERP response to painful images was selectively attenuated immediately preceding subjective reports of mind wandering, relative to when attention was on-task.  In Experiment 2 we found that subjective ratings of painful images were also reduced in the moments immediately preceding reports of mind wandering.  Taken together, our findings support the notion that our processing of affectively-salient stimuli associated with sensitivity to othersʼ pain is subject to direct modulation by transient fluctuations in task-related attentional states. In light of these data and conclusions, several important questions and issues follow.  First, why do we become less sensitive to the physical discomfort of others when we mind wander?  One explanation is based on the executive function model of mind 
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wandering (Smallwood & Schooler, 2006), whereby mind wandering decouples executive resources from the external environment and directs them internally to facilitate the inner trains of thoughts. If resources are allocated elsewhere, then the photos may not be fully processed. An incomplete representation of the situation in the photo may thereby reduce its perceived intensity, an interpretation consistent with the P3-level effect of mind wandering we report for painful images. Nevertheless, this model would predict a similar attenuation in processing of both painful and neutral images. That only our response to painful stimuli was reduced during mind wandering states suggests other mechanisms may be involved.  Another possibility is that it might be related to the impact of disembodied mental states on our neurocognitive functioning. For example, in a recent study of autobiographical memory, Eich and colleagues (2009) found that when we recalled past personal events from a 3rd-person visual perspective, there was a decrease in the number of physical sensations ascribed to the memory (e.g., noting butterflies in one's stomach or sweat on one's palms), relative to when recalling the same memory from a 1st-person visual perspective.  Moreover, this effect was associated with a significant decrease in neural activity in the insula, a cortical region closely associated with visceral, physiological monitoring of one's bodily state.   Given such evidence, mind wandering may impact physical monitoring of the body in a manner akin to a disembodied, 3rd-person perspective during memory recall.  Not only is this possibility consistent with our findings from Experiment 1 showing a specific attenuation in the later 
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response to pain, but it aligns with the notion that mind wandering is akin to a form of mental time travel, where we mentally disengage from the physical here-and-now in order to reminisce about the past or fantasize about the future (e.g., Schooler et al., 2011; Smallwood et al., 2011).  In other words, the evidence converges on mind wandering as a cognitively-disembodied state.  Second, our findings also demonstrate a critical expansion in the scope of neurocognitive processes susceptible to modulation by mind wandering.  Specifically, using impoverished, affectively-neutral stimuli, prior studies have shown that mind wandering reduces the sensory and cognitive processing of both visual and auditory stimuli (e.g. Kam et al., 2011; OʼConnell et al., 2009; Smallwood et al., 2008).  In our data, however, we found that the responses to observing pain in others is also attenuated when we mind wander, as measured via both ERPs and subjective ratings of pain. This suggests that the impact of mind wandering is not restricted to the sensory and cognitive processing of affectively-neutral stimuli, but it extends to the processing of stimuli with some measure of affective saliency as well.  Consistent with this finding, mind wandering has previously been associated with increased negative moods (e.g., Killingsworth & Gilbert, 2010; Smallwood, Fitzgerald, Miles & Phillips, 2009). Our data thus indicate that the effects of mind wandering extend beyond mood per se. Notably, there appears to be a tight link between task-related attention and the intensity of our affect-related responses to external stimuli.  
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A third, related issue concerns the absence of decoupling from the neutral images during mind wandering episodes. While past studies have shown an attenuation of the P3 to affectively-neutral stimuli in mind wandering states (Barron et al., 2011; Smallwood et al., 2008), we did not observe the same P3 attenuation to our neutral images. What might explain this difference? For one, the nature of stimuli used in past studies (i.e. numbers, and geometric shapes) is notably different from that in the current study (i.e. naturalistic photos), even though they may all be considered as affectively neutral. Further, the lack of P3 modulation in neutral images in this study is in fact consistent with our previous findings of an absence of P3 attenuation to the target when task-irrelevant probes were presented in the upper and lower visual field (Kam et al., 2011). Both findings suggest that mind wandering-related P3 modulations appear to decrease as stimulus set or task becomes more complicated or engaging, at least for affectively-neutral stimuli.   As a final point, the ERP-based responses to pain have been dissociated into two components, as previously noted––an initial automatic simulation of the pain, followed by a more evaluative analysis of the pain and its context (Fan & Han, 2008; Gu & Han, 2007).  Within the context of this model, we found an effect of mind wandering only on the later, evaluative response to observed pain.  This result is consistent with those of Fan and Han's (2008), who found the later affective response was subject to modulation by top-down attentional control, but the earlier sensory-related response was not. In Fan and Han (2008), attention was manipulated by invoking a dual-task situation, such that 
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some proportion of attentional resources were deliberately directed away from processing affective images.  In the current study, we were studying a distinct form of attention – in particular, natural, transient fluctuations in whether one is paying attention to the task at hand (e.g., Dosenbach et al., 2008).  The collective evidence thus indicates that only the later stage of the affective response to perceived pain are susceptible to varying forms of attentional modulation. 
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CHAPTER 3: Mind Wandering and Motor Control: Off-task Thinking Disrupts the Online Adjustment of Behavior  Introduction  Mind wandering, or those transient periods of time during which our attention momentarily drifts away from our on-going task and perceptual milieu, is fundamental to human neurocognitive function.  In terms of neural architecture, mind wandering episodes have been strongly associated with activation of the brainʼs default mode network (e.g., Christoff et al., 2009; Kirschner et al., 2012; Mason et al., 2007), while in terms of cognitive processes, mind wandering has been tied to fluctuations in executive control (e.g., McVay & Kane, 2009; 2012).  Such findings have supported the hypothesis that regular oscillations in the depth of our neurocognitive engagement with the external environment is normative to healthy human brain function (e.g., Schooler et al., 2011; Smallwood, 2013; Smallwood & Schooler, 2006), and that a variety of clinical and sub-clinical cognitive pathologies may be linked to altered patterns of mind wandering (e.g., Elua et al., 2012; Helton, 2009; Shaw & Giambra, 1997; Smallwood et al., 2009).   Given that mind wandering is central to our neurocognitive make-up, there has been growing interest in understanding the functional consequences of slipping into a mind wandering state.  For example, when we mind wander, we now know that there is a systematic reduction in the extent to which we process external stimulus events at 
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both the sensory and cognitive levels (e.g., Hu et al., 2012; Kam et al., 2011; OʼConnell et al., 2009; Smallwood et al., 2008; Smilek et al., 2010), effects that can arise regardless of whether the events were task-related or not (e.g., Barron et al., 2011).  In a corresponding manner, behavioral motor performance reliably shifts to a more automatic and/or degraded state (e.g. Carriere et al., 2008; Cheyne et al., 2006; Reichle et al., 2010; Schooler et al., 2004: Smallwood et al., 2008; Weissman et al., 2006); for example, reaction times (RTs) tend to speed up and error rates are increased during mind wandering vs. on-task states in vigilance tasks (e.g. Smallwood et al., 2004).     Yet despite such findings, our understanding of how mind wandering may directly impact motor behavior remains incomplete at best.  Considered from a motor perspective, the range of potential mind wandering effects on behavioral control concerns more than just the speed and accuracy of response selection and the degree of response automaticity.  In addition, the normal control of movement also involves the ability to adaptively monitor and adjust our motor outputs on a moment-to-moment basis as needed.   Given that mind wandering attenuates the sensory and cognitive processing of external stimulus inputs, the goal of our study was to determine whether this may have a corresponding effect on our ability to dynamically adjust our motor behavior on-line in response to shifting, unpredictable environmental conditions.   In our first experiment we addressed the question using a canonical visuomotor tracking task that allowed us to measure the magnitude of continuous tracking error as a 
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function of whether or not participants were in a mind wandering state.  Results indicated that tracking error did in fact increase during mind wandering.  In the second experiment we examined whether this effect of mind wandering on behavior would generalize to a qualitatively distinct form of response monitoring and control––namely, feedback learning in the context of a time estimation task.  We again found behavioral evidence of the impact of mind wandering on the dynamic control of motor outputs, an effect that co-occurred with attenuations in direct, event-related potential (ERP) measures of performance monitoring processes in cortex.   In the first experiment, participants performed a visuomotor tracking task. They were stopped at unpredictable intervals and asked to report on whether their attention at that moment was on-task or whether they were mind wandering. To examine the influence of mind wandering on motor control, we compared the error in tracking performance between on-task and mind wandering states. Given that disruptive effects of mind wandering extend beyond perceptual and cognitive processes to response selection, we predicted there would be more errors during mind wandering relative to on-task attentional states.  Experiment 1  Methods Participants 22 participants completed the experiment in exchange for one course credit.  
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They were all right handed, with no history of neurological problems and had normal or corrected-to-normal vision. Participants provided written informed consent to the experimental procedure. The Research Ethics Board at the University of British Columbia approved this study.  Paradigm and Procedures  Participants performed a visuomotor tracking task (Boyd & Linsdell, 2009; Boyd & Winstein, 2004), in which they continuously tracked a target moving in sine-cosine waveform on a computer monitor by controlling the position of a cursor using a joystick. The target appeared as an open white circle and participant's movements were represented as a filled red dot on the monitor. The paradigm is shown in Figure 3.1. The task was to track the vertical path of the target with the joystick as accurately as possible.  Joystick position sampling and stimuli presentation were both at 60 Hz, using custom software developed on the LabView platform (v. 7.1; National Instruments Co.).   Figure 3.1. Task paradigm of Experiment 1. Participants were instructed to continuously track a moving target across the computer monitor using a joystick.    
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 There were 14 blocks of varying duration; lasting from 48 s to 192 s. Each trial was 32 s long, requiring participants to track the target from left to right across a 17” computer screen. Trials contained a 2 s baseline and 30 s of tracking a unique sine-cosine segment; each 30 s waveform was unique and could not be learned, thus participants were required to attend to the visual stimuli in order to track accurately. The pattern of target movement was predefined and modified from Wulf and Schmidtʼs method (1997). Waveforms were generated using the polynomial equation with the following general form (c.f. Wulf & Schimdt, 1997), using randomly inserted coefficients ranging from -5 to 5:  ƒ(x) = b0 + a1sin(x) + b1cos(x) + a2sin(2x) + b2cos(2x) + … + a6sin(6x) + b6cos(6x). Importantly, neither the target or participantsʼ movements left a trail, thus participants could not visualize the entire target pattern.  To control for waveform difficulty across participants, each practiced the same set of random waveforms.   Our primary behavioral measure was the changes in root mean squared error (RMSE), which reflects the overall tracking error in the kinematic pattern. It is the average difference between the target pattern and participant movements (c.f. Boyd & Winstein, 2004). The RMSE is calculated as follows:     
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Task-Related Attention In both experiments we relied on experience sampling as a means of determining the attentional state of our participants over time (e.g., Schooler et al., 2011; Smallwood  & Schooler, 2006).  Considered to be a direct measure of mind wandering, experience sampling relies on the fact that if prompted, we can reliably report on the content of our thoughts at any given moment and further, determine whether they center on the on-going task being performed (referred to as an on-task state), or alternatively, whether they have drifted off to other times, places, or issues (referred to as an off-task or mind wandering state).  Although the act of reporting on oneʼs attentional state may interfere with the content of consciousness itself (e.g., Filler & Giambra, 1973), by using the report to categorize a participantʼs attentional state in the 10-15 seconds immediately prior to the report, the methodology has been used to demonstrate reliable differences in neurocognitive functioning between on-task and mind wandering states (e.g., Christoff et al., 2009; Franklin et al., 2011; Kam et al., 2011; Kirschner et al., 2012; Mason et al., 2007; McKiernan et al., 2006; Smallwood et al., 2004; Smallwood et al., 2008;  Starwarczk et al., 2011).  As such, in adopting this methodology here, our approach to defining attentional states aligned with widely-accepted norms in the field of mind wandering research.  Accordingly, to measure task-related attention, participants were instructed to report their attentional state at the end of each block.  They were asked to identify their state immediately prior to the block termination as either being on-task or mind 
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wandering. Importantly, participants were provided with descriptions and examples of these two attentional states prior to the testing session. On-task states were defined as when oneʼs attention was firmly directed towards the task, whereas mind wandering states were described as when oneʼs attention has drifted away from the task. Attentional reports were recorded at the conclusion of each block, and these reports were then used to sort behavioral data into on-task vs. mind wandering conditions. As mentioned above, block duration randomly varied between 48 to 192 seconds in order to minimize predictability of block completion and maximize variability of attentional state at the time of block completion. The duration of the task itself was approximately 30 minutes.  Statistical Analysis In terms of comparing on-task vs. mind wandering states, the periodicity of shifts in these attentional states tends to approximate 10-15 seconds (e.g. Christoff et al., 2009; Sonuga-Burke & Castellanos, 2007). We thus examined the movement data in the last 12 seconds prior to the subjective report of each attentional state prompted by the probes (c.f. Kam et al., 2011; Smallwood et al., 2008). Specifically, we conducted paired-samples t-tests to compare the RMSE by averaging together data in the 12 seconds preceding each of the two attentional states (on-task vs. mind wandering) report. Although we had no knowledge as to how long participants had actually been in a particular attentional state at the time a subjective report was given, our analyses were based on the assumption and aforementioned evidence (Christoff et al., 2009; 
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Sonuga-Burke & Castellanos, 2007) that the 10-15 seconds prior to each report would, on average, reliably capture the given attentional state.   Results Attentional Reports Participants completed 14 trial blocks, of which 43% were reported as on-task and 57% as mind wandering – a typical breakdown of attentional states in a cognitive laboratory task (e.g. Kam et al., 2011; Smallwood et al., 2008).   Tracking Performance The motor tracking performance, indexed by the RMSE, was examined as a function of participantsʼ attentional states. The RMSE of trials preceding reports of mind wandering (M = 4.71, SD = 1.90) appeared to be much greater than those preceding on-task reports (M = 3.93, SD = 0.70). This was confirmed by a significant paired-samples t-test (t(21) = -2.23, p = .034, d = 0.55).   Discussion  In Experiment 1, we found greater error in motor tracking just preceding mind wandering relative to on-task reports. This suggests that mind wandering does impair the precision at which we control our motor behavior on a moment-to-moment basis. Given the lack of external feedback on the participantsʼ performance however, it is unclear whether the increased tracking error during mind wandering was due to visual 
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sensory attenuation per se (Kam et al., 2011), or whether mind wandering can also down-regulate behavioral/performance monitoring. We addressed this question in Experiment 2.  Experiment 2  Introduction We recorded participantsʼ EEG as they performed a time-estimation task during which they received trial-by-trial feedback on the accuracy of their responses and were occasionally asked to report their attentional state at that moment as on-task or mind wandering. To determine the impact of mind wandering on performance monitoring, we measured the feedback error-related negativity (fERN) elicited by task feedback in the intervals immediately preceding on-task vs. mind wandering reports.  In particular, the fERN is an ERP component that indexes the extent to which we are monitoring the accuracy of our responses, such that its amplitude positively covaries with the magnitude of behavioral assessment (e.g. Holroyd & Krigolson, 2007; Krigolson, Pierce, Holroyd, & Tanaka, 2009; Miltner, Braun & Coles, 1997).  If mind wandering attenuates performance monitoring, then it predicted that the fERN would be lower in amplitude during periods of mind wandering vs. on-task attentional states.     
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Methods  Participants 15 participants (9 females; M=24.8 years, S.D.=2.20) completed the experiment in exchange for $20 (Canadian dollars).  They were all right-handed, with no history of neurological problems and had normal or corrected-to-normal vision.  Participants provided written informed consent to the experimental procedure. This study was approved by the Research Ethics Board at the University of British Columbia.  Stimuli and Paradigm We recorded EEG and behavioral data while participants performed a time-estimation task (c.f., Holroyd & Krigolson 2007; Miltner et al., 1997). On each trial, participants were required to estimate the duration of one second following an initial auditory cue by pressing a button. The cue was presented at 3000 Hz for 25 ms. Following the participantʼs estimate, a feedback stimulus was visually presented for 1000 ms at fixation to indicate the accuracy of their response. After the offset of the feedback stimulus, a blank screen was presented for 400, 500 or 600 ms. Therefore, each trial lasted approximately between 2400 ms and 2600 ms (i.e. on average 2500 ms). A trial was considered correct if a participantʼs response occurred within a window of time centered around one second (+/- 100 ms), and was considered incorrect otherwise. In order to maintain a global probability of approximately 0.5 for correct and incorrect feedback stimulus, the size of the response window decreased by 10 ms each 
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time a participant was correct, and increased by 10 ms each time a participant was incorrect.   Behavioral Measure We determined the mean absolute change in response time following correct and error feedback as a function of participantsʼ attentional states. That is, the absolute difference in time estimates between the current and previous trial was calculated in percentages for each participant (c.f. Holroyd & Krigolson, 2007), separately for correct and error feedback during on-task and mind wandering states. This measure allows us to examine participantsʼ sensitivity to their own behavioral performance as a function of attentional state.  Task-Related Attention Attention reports were recorded at the conclusion of each trial block, and these reports were then used to sort ERP data into on-task vs. mind wandering conditions. The protocol for measuring task-related attention is identical to Experiment 1 with the following exceptions. The block duration itself was randomly varied between 30 to 90 seconds (i.e. 12 to 36 trials), and the duration of the task itself was approximately 65 minutes.  Electrophysiological Recording and Analysis During the task, electroencephalograms (EEGs) were recorded from 32 active 
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electrodes mounted on a cap in accordance to the International 10-20 system using a Biosemi Active-Two amplifier system. All EEG activity was recorded relative to two additional electrodes located over medial-frontal cortex (Common Mode Sense and Driven Right Leg) using a high-pass filter of 0.05Hz, digitized on-line at a sampling rate of 256 samples-per-second. To ensure proper eye fixation and allow for the correction and/or removal of events associated with eye movement artifacts, vertical and horizontal electrooculograms (EOGs) were also recorded – the vertical EOGs from an electrode inferior to the right eye, and the horizontal EOGs from two electrodes on the right and left outer canthus. Offline, computerized artifact rejection was used to eliminate trials during which detectable eye movements and blinks occurred. These eye artifacts were detected by identifying the maximum voltage values on all recorded EOG channels from -200 to 600 ms post visual feedback stimulus for each event epoch, and then removing the trial from subsequent signal averaging if that value exceeded 150 µV, a value estimated to capture blinks and eye movements. Subsequently, all ERPs were algebraically re-referenced to the average of the left and right mastoid signals, and filtered with a low-pass Gaussian filter at 25.6 Hz to eliminate any residual high-frequency artifacts in the waveforms.  The resulting ERPs were used to generate grand-averaged waveforms.  Statistical Analysis Statistical quantification of ERP data was based on minimum peak and mean amplitude measures relative to a -200 to 0 ms pre-stimulus baseline. In particular, we 
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derived difference waves for the on-task and mind wandering conditions by subtracting the correct feedback averaged waveforms from the incorrect feedback averaged waveforms for each attentional state and participant at electrode site FCz, where the fERN is typically maximal (e.g., Holroyd & Krigolson, 2007; Krigolson et al., 2009), as it was in our data. The fERN was then subsequently identified by an automated computer algorithm as the maximal negative voltage between 250 and 350 ms on the difference waveforms following feedback stimulus onset (see Holroyd & Krigolson (2007) for more on this peak-picking methodology).   Here we compared both behavioral and ERP responses in the last 15 seconds prior to the subjective report of attentional state. That is, the behavioral and ERP data for each condition of interest (correct vs. error) were based on averaging together the 6 trials preceding each of the two attentional states (on-task vs. mind wandering) report. We extended the analysis period to 15 seconds prior to each attentional report as an attempt to maximize the number of events to include in each waveform average while not extending the window back so far in time as to consistently capture the preceding attentional state or transition period between states.  Results Attentional Reports Participants completed an average of 63 blocks of trials, of which 44% were reported as on-task and 56% as mind wandering.  
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Behavioral Performance To examine how mind wandering affected behavioral performance, we conducted an omnibus ANOVA that had attentional state (on-task vs. mind wandering) and feedback valence (correct vs. error) as within-subject factors. The percentage of absolute change in time estimates and the variance of these time estimates appeared to be much greater during mind wandering periods than on-task periods, as shown in Figure 3.2. This data pattern was confirmed via a significant main effect of attentional state (F(1,14) = 39.51, p < .001). The main effect of feedback valence was not significant (F(1,14) = 1.03, p = .328). However, there was a significant attentional state x feedback valence interaction (F(1,14) = 8.95, p = .010). Follow-up analyses revealed that the absolute change in time estimates following error feedback was significantly greater than that following correct feedback during on-task states (t(1,14) = -2.35, p = .034), but not during mind wandering states (t(1,14) = 1.93, p = .074). While the adjustment in time estimates during mind wandering appears to be insensitive to feedback valence, this difference was nonetheless near significant. Along with the relatively small behavioral effect during on-task states, this set of findings makes it difficult to draw definitive conclusions about the task-related attention effect on behavioral performance on this task.  
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Figure 3.2. The absolute change in time estimate (in percentage), with standard errors. There was a significant difference between absolute change in time estimate following error and correct feedback during on-task states (as indicated by *), but not mind wandering states.   Electrophysiology Although the behavioral results showed evidence of decreased sensitivity to feedback during mind wandering, we wanted to first confirm normative mind wandering effects in our ERP findings, prior to assessing the fERN.  In particular, the P3 elicited by target stimuli has been shown to reliably attenuate in amplitude immediately preceding reports of mind wandering relative to on-task (e.g., Kam et al., 2011; OʼConnell et al., 2009; Smallwood et al., 2008).  To confirm the reliability of our subjective reports, we thus wanted to determine that there was in fact a general attenuation of the P3 amplitude elicited by feedback signals immediately preceding mind wandering vs. on-task reports. *
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Thus, we first conducted repeated-measures ANOVA on P3 with factors of attentional state (on-task vs. mind wandering), feedback valence (correct vs. incorrect), and electrodes (Cz and Pz) to establish the reliability of subjective reports of attentional state. For brevity, we only report effects associated with attentional state and feedback valence. The P3 elicited by the correct and error feedback as a function of attentional state is shown in Figure 3.3. This ERP component was measured at different time points between the two feedback stimulus types because it peaked at different time points for correct vs. error feedback, as can be seen in the figure. Mean amplitude measures were therefore taken across a 290 – 410 ms time window for correct feedback, and 330 – 450 ms time window for error feedback. We examined electrode sites Cz and Pz, where the P3 is typically maximal (e.g., Polich, 2007). There was a significant main effect of attentional state (F (1,14) = 12.06, p = .004) such that regardless of feedback valence, the P3 amplitude elicited by feedback signals was significantly greater immediately preceding on-task vs. mind wandering attentional reports. There was no main effect of feedback valence, nor an interaction between attentional state and feedback valence (p > .800). Importantly, this main effect of attentional state on P3 amplitude is consistent with previous findings in the mind wandering literature (Kam et al., 2011; OʼConnell et al., 2009; Smallwood et al., 2008).  
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Figure 3.3. P3 in response to correct and error feedback during on-task and mind wandering attentional states. The amplitude of P3 at both Cz and Pz time-locked to the visual feedback stimulus was significantly reduced regardless of feedback valence during periods of mind wandering relative to periods of on-task.    We then examined the impact of mind wandering on feedback processing, as measured via the fERN on the difference waveforms shown in Figure 3.4. The waveforms elicited by correct and error feedback stimulus as a function of attentional 0 200 400Amplitude (?V)Time (ms)-200-4-20246810Cz0 200 400Amplitude (?V)Time (ms)-200-4-202468100 200 400Amplitude (?V)-4-20246810-200121412141214On-Task0 200 400Amplitude (?V)-4-20246810-2001214Time (ms)Time (ms)PzMind WanderingCorrectError P3 P3P3 P3
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states are shown in Figure 3.5. As can be seen in Figure 3.4, the fERN appeared to be attenuated during mind wandering periods relative to on-task periods. To confirm this, two single-sample t-tests first confirmed the presence of a fERN in both the on-task (t(14) = -5.43, p  = .000, d = -2.90) and mind wandering (t(14) = -3.75, p  = .002, d = -2.00) conditions. Next, a comparison of the difference waveforms between on-task and mind wandering conditions revealed that the amplitude of the fERN was significantly reduced during mind wandering relative to on-task periods (t(14) = 2.22, p  = .044, d = 0.61).   Figure 3.4. fERN in difference waveforms (error – correct) as a function of on-task versus mind wandering states. The amplitude of fERN at FCz was significantly attenuated during periods of mind wandering relative to periods of on-task.    
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 Figure 3.5. Conditional waveforms of on-task and mind wandering attentional states in response to correct and error feedback. The difference between correct and error feedback appears to be greater during on-task states relative to mind wandering states.    While definitive conclusions about the fERN can only be made with difference waveforms, we wanted to determine whether this fERN attenuation during mind wandering may be driven by a differential attentional modulation of the processing of correct and error feedback. As such, we compared the ERP waveforms of both correct and error feedback at FCz between on-task and mind wandering states, using the same individually-specified time windows as were used to identify the fERN in each individualʼs difference waveforms. In particular, we conducted repeated-measures ANOVAs with factors of attentional state (on-task vs. mind wandering), and feedback valence (correct vs. error). We found a significant interaction between attentional state and feedback valence (F (1,14) = 4.907, p = .044). It appears that mind wandering was specifically attenuating fERN-related activity for correct feedback signals. This FCz0 200 400Amplitude (?V)Time (ms)-200-4-202468100 200 400Amplitude (?V)-4-20246810-200Time (ms)CorrectErrorOn-Task Mind Wandering
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interpretation was confirmed via separate paired-samples t-tests, which revealed a significant main effect of attentional state in response to correct feedback (t(14) = 2.691, p = .018, d = 0.51), but not error feedback (t(14) = 0.158, p = .877, d = 0.04). Specifically, while the processing of the correct feedback was significantly attenuated immediately preceding mind wandering (M = 6.16, SEM = 1.21) vs. on-task (M = 8.63, SEM = 1.31) attentional reports, the processing of error feedback did not significantly differ between mind wandering (M = 2.85, SEM = 1.09) and on-task (M = 3.04, SEM = 1.55) attentional states.  Discussion Using both behavioral and electrophysiological measures, Experiment 2 examined the question of whether mind wandering impacts the monitoring and adjustment of behavioral performance. We found decreased behavioral sensitivity accompanied by a reduced P3 to feedback stimulus during periods of mind wandering. Our data also revealed a reduced fERN during mind wandering compared to on-task attentional states. Consistent with the finding that correct trials appear to modulate the fERN amplitude (e.g. Holroyd, Pakzad-Vaezi, & Krigolson, 2008), the reduced fERN in our data was specifically driven by a significant mind wandering effect on correct, but not error, feedback.    
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General Discussion The purpose of this study was to examine the effects of mind wandering on the online adjustment of behavior. Using a visuomotor tracking task in Experiment 1, we observed greater errors in tracking performance during periods of mind wandering. Using a time estimation task in Experiment 2, we found reduced behavioral and neural sensitivity to performance feedback during mind wandering states, suggesting that the disruption in behavioral control could not be solely attributed to sensory attenuation per se. Extending previous research showing that mind wandering states decouple our attention from incoming sensory and cognitive stimuli (e.g. Kam et al., 2011; OʼConnell et al., 2009; Smallwood et al., 2008), these results suggest mind wandering also disengages us from the monitoring and adjustment of our behavior.   That mind wandering was associated with increased error in a continuous tracking task is not surprising given mind wandering has been implicated in performance failures in vigilance tasks (Smallwood et al., 2004) and response selection tasks (Franklin et al., 2011; Schooler et al., 2004). Interestingly, Boyd and Linsdell (2009) have implemented the motor tracking task over four practice sessions to induce motor sequence learning, and found that tracking performance at retention did improve as indexed by reduced RMSE (Boyd & Linsdell, 2009). Given this finding, if mind wandering increases tracking error as we have shown in our study, this would not only lead to disruption in task performance and accordingly the learning of the sequence in 
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the current testing session but it may also have a disruptive long term effect on the learning of motor skills over time.   If mind wandering is impacting behavioral feedback processing as measured via the fERN, how does this actually affect behavioral outputs? The fERN is time-locked to external signals of response accuracy, and is generated by a high-level error evaluation system that is tasked with performance optimization (e.g. Holroyd & Coles, 2002). As such, the fERN not only involves detecting the relative accuracy vs. inaccuracy of a response, but also reflects the extent to which we use that information for the modification of behavior (e.g. Krigolson et al., 2009). Given that mind wandering leads to transient reductions in the extent to which we process behavioral feedback signals, this suggests the functional consequences are two-fold.  On the one hand, as our data confirm, the transient phases of mind wandering lead to direct disruption on the moment-to-moment adjustments in motor behavior. However, given that the cortical processes indexed by the fERN are associated with reinforcement learning (Holroyd & Coles, 2002), this would imply over time, mind wandering may also directly affect the trajectory or efficacy of motor learning itself. Together, findings from both experiments would suggest that the more we mind wander, the slower and less efficient motor learning may become.    Our report of a mind wandering effect on feedback processing manifest in the fERN raises the question to what extent might our findings be driven by these sensory 
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and/or more general cognitive effects of mind wandering?  In terms of possible visual sensory confounds, prior studies have found visual sensory attenuation for visual stimuli in the upper visual hemifield (Kam et al., 2011) but not for visual stimuli at fixation (Smallwood et al., 2008). As the visual feedback stimuli used in our study were at fixation, this suggests sensory attenuation is an unlikely explanation for our fERN results.  Likewise, when we examined the P3 component in our study, we found attenuation in amplitude during mind wandering that was insensitive to the valence of feedback.  In contrast, we found that the attenuation in fERN amplitude during mind wandering was associated with a selective effect of mind wandering for correct feedback signals. This functional dissociation between the P3 and fERN findings thus suggests that the effect of mind wandering on the latter can not simply be ascribed to its effect on the former.  Rather, it would appear that mind wandering can have a direct, independent influence on feedback processes in cortex.  Finally, given our results, itʼs also important to consider what our data are not showing. In particular, the fERN reflects an evaluation of oneʼs preceding trial performance, based on delayed external feedback signaling whether or not behavior needs to be modified for improved performance. While the external feedback is typically presented in the form of a visual stimulus, the nature of this feedback and its implications in behavioral performance makes it qualitatively distinct from task-relevant sensory stimulus. In contrast, the response ERN is another error-related component that reflects the implicit aspect of response monitoring, whereby the internal evaluation of 
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performance is based on the response itself (Gehring et al., 1993).  While our findings suggest that mind wandering impacts the continuous adjustment of motor behavior in the absence of feedback (as shown in Experiment 1) as well as behavioral control associated with external feedback (as shown in Experiment 2), whether it also affects the implicit evaluation of on-line performance as captured by the response ERN elicited by correct vs. incorrect responses remains to be directly investigated.   Given our findings, an important issue concerns how if at all this relates to the attentional lapse literature. While mind wandering and attentional lapses capture a similar neurocognitive phenomenon, they do seem to occur at different temporal levels. In particular, mind wandering is a phenomenon that spans an extended period of time (i.e. fluctuations of 10-15 seconds) exceeding a given single event, whereas attentional lapses tend to occur during a much narrower time window capturing the lapse at a single event level. Several recent theoretical and empirical papers have supported and validated these two related models of attention (e.g. Dosenbach et al., 2008; Esterman, Noonan, Rosenberg, & DeGutis, 2012). Specifically, at a theoretical level, Dosenbach and colleagues (2008) have suggested there are multiple controlling systems operating at multiple scales of time. Further, in terms of empirical evidence, the findings of Esterman and colleagues (2012) suggested the occurrence of two types of attentional states – one tied to the default mode network (reflective of mind wandering) that is more stable and less error prone in terms of behavioral measures, and a second one tied to the dorsal attention network (reflective of attentional lapses) that requires more effortful 
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processing. That the effects of mind wandering appear to parallel effects of attentional lapses actually lends support to the notion that task-related attention (or mind wandering) and selective attention (or attentional lapses) may exert similar forms of top-down attentional control on other neurocognitive processes. In the case of attentional control of sensory response, Dosenbach and colleagues (2008) have suggested that there are at least two distinct control systems operating in parallel – one associated with rapid shifts of selective visual attention (e.g. Mangun & Hillyard, 1991; Woldorff et al., 1997) and another one associated with slower fluctuations in task-related attention (e.g. Kam et al. 2011; OʼConnell et al., 2009). In the case of behavioral control, that Weissman and colleagues have demonstrated that attentional lapses impair goal-directed behavior and are associated with reduced pre-stimulus activation in the anterior cingulate cortex (Weissman, Roberts, Visscher, & Woldorff, 2006) and that we found impaired adjustment of behavioral control are consistent with the idea that distinct attentional control systems can have similar impact on various neurocognitive processes. Taken together, mind wandering and attentional lapses do appear to be related conceptually, but future work needs to be done to disentangle the overlaying attentional influences linked to dissociable neural systems.  
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CHAPTER 4: Mind Wandering and the Adaptive Control of  Attentional Resources  Introduction One of the odd quirks of human cognition is that we frequently get lost in our own trains of thought, even when doing attention-demanding tasks.  When driving, for example, many people have had that unsettling experience of suddenly realizing that they have been completely tuned out for the past few miles, with little recollection of the traffic and terrain that's been navigated in the interim. But this raises a striking question regarding our natural propensity to have our thoughts drift off-task––how is it that our minds can regularly wander like this during on-going tasks, yet we still seem to retain some capacity to monitor and respond to the external environment?  Is some ability to selectively attend to salient events in the outside world actually preserved when in mind wandering states?  The question is all the more perplexing given what we know about the effect of mind wandering on stimulus processing in cortex.  When in mind wandering states, there is a significant reduction in the extent to which we cognitively analyze or process task-relevant events, relative to when in on-task attentional states (e.g., Barron, Greer, & Smallwood, 2011; OʼConnell et al., 2009; Smallwood, Beach, Schooler, & Handy, 2008).  Likewise, the initial sensory-evoked cortical activity engendered by task-irrelevant events also decreases, an effect observed in both the visual and auditory 
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domains (e.g., Braboszcz & Delorme, 2011; Kam et al., 2011).  Such evidence has suggested that mind wandering facilitates the production and maintenance of internal trains of thought by transiently decoupling neurocognitive systems from external stimulus inputs (e.g., Barron et al., 2011; Schooler et al. 2011; Smallwood et al., 2003; 2011).   But if our thoughts become decoupled when mind wandering, do our attentional systems decouple as well?   Given this question, the goal of our first study was to examine whether controlled or volitional attentional functions change as we drift in and out of mind wandering states, and if so, how this compares to the possible effect of mind wandering on automatic or reflexive attentional functions. In the context of our experiment, controlled/volitional attentional orienting refers to the voluntary shifting of attention in a top-down manner, whereas automatic/reflexive attentional orienting refers to oneʼs attention being involuntarily drawn in a bottom-up manner  (e.g. Corbetta & Shulman, 2002). Hereafter we refer to the controlled shifting of attention as volitional spatial orienting, and the automatic shifting of attention as reflexive spatial orienting. Regardless of the type of attentional orienting, the outcome of the shifting of attention to a particular location in our visual space is that both reaction times and perceptual processing in visual cortex are enhanced for stimuli at the attended location (e.g. Mangun, Hillyard, & Luck, 1993; Posner, 1980).   
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In our first experiment we thus asked participants to perform two different visual-spatial cuing tasks that required making manual responses to lateralized targets.  One task involved volitional spatial orienting (e.g., Mangun, Hillyard, & Luck, 1993; Posner, 1980) and the other task involved reflexive spatial orienting (e.g., Friesen & Kingstone, 1998; Friesen, Ristic, & Kingstone, 2004; Tipper, Handy, Geisbricht, & Kingstone, 2008). At unpredictable intervals during task performance we stopped participants and asked them to report on their task-related attentional state as either on-task or mind wandering. We then examined the reaction times (RTs) to targets as a function of whether they were in cued or uncued spatial locations, and whether they immediately preceded an on-task or mind wandering report.   If mind wandering disrupts the spatial orienting of attention, it is predicted that RTs should be faster for cued vs. uncued targets just prior to on-task reports, but not just prior to reports of mind wandering. We predicted that these effects would be observed in both volitional and reflexive spatial orienting.   Experiment 1  Methods Participants  A total of 32 individuals participated (17 females; M= 20.83 years, S.D.=1.42), with 17 subjects completing the volitional spatial orienting task and 15 subjects completing the reflexive spatial orienting task.  All were right handed, and had normal or corrected-to-normal vision. They all gave written informed consent, and were given 
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course credits for their participation. This study was approved by the UBC Review Ethics Board.  Stimuli and Paradigm Stimuli were presented on an 18 in. color monitor, placed 80 cm away from the subjects. In both tasks, a fixation dot first appeared in the center of the screen for 2000 ms. Following this, a cue was presented at fixation, and remained on the screen for 1300 ms. The target, which was an X (1.1° × 0.9°), appeared either in the left or right visual field (5.2° from the left/right edge of the screen). It was presented 800 ms after the onset of the cue, and lasted for 100 ms. The inter-trial interval was randomly jittered between 1500-1700 ms, during which a response was made. For both tasks, subjects were instructed to press a designated button as quickly and accurately as possible when the target appeared, regardless of its location. Task stimuli and paradigm are shown in Figure 4.1.  In the volitional spatial orienting task, participants were instructed to attend to the left visual field if the inner circles were green, and attend to the right visual field if the inner circles were red. The cue was made of two circles (2° × 2°) stacked vertically on top of each other, with two smaller inner circles (0.7° × 0.7°) colored either in green or red. Given this arbitrary stimulus-response pairing, orienting to the cued location has been shown to require a voluntary shift of attention (e.g. Posner, 1980). The cue on each trial was predictive of the upcoming target location (left vs. right visual field) with 
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0.8 probability, thereby providing the incentive to volitionally orient attention to the cued location. In the reflexive spatial orienting task, an eye-gaze cue was made of two circles (1.9° × 1.9°) presented horizontally next to each other with small, black inner circles (0.9° × 0.9°), to mimic eyes. These “eyes” would either look to the left or to the right, as shown in Figure 4.1.  The direction of eye gaze was non-predictive of the upcoming target location, in that the target was only presented at the gazed-at location with 0.5 probability (across the trial block). Our eye-gaze paradigm is a canonical one that has been previously used to elicit reflexive shifting of attention towards location of the eye gaze (e.g., Friesen & Kingstone, 1997; Friesen, Ristic, & Kingstone, 2004; Tipper et al., 2007, etc.). Participants were not given instructions as to where to attend, as previous evidence suggests that individuals reflexively orient their attention to the direction of othersʼ eye gaze (e.g. Friesen & Kingstone, 1998; Friesen, Ristic, & Kingstone, 2004).   Figure 4.1. Task paradigm of Experiment 1. The stimulus and timing of the (a) volitional orienting task and (b) reflexive orienting task are shown.  a) Volitional Spatial Orienting Taskb) Reflexive Spatial Orienting TaskITI: 1500-1700ms800 ms 100 msXXITI: 1500-1700ms800 ms 100 ms400 ms400 ms
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Task-related Attention To measure task-related attention, subjects were instructed to report their attentional state as either being on-task or mind wandering at the end of each trial block (c.f. Kam et al., 2011; Smallwood et al., 2008). Subjects were provided with definitions of these two attentional states prior to starting the testing session. On-task states were defined as when oneʼs attention was firmly directed towards the task, whereas mind wandering states were described as when oneʼs attention has drifted away from the task. At the conclusion of each trial block, participants verbally reported their attentional states, which were recorded by the investigator. These reports were then used to sort the RT data based on on-task vs. mind wandering states. The block duration itself was randomly varied between 30 to 90 s in order to minimize predictability of block completion and maximize variability of attentional state at the time of block completion. Given one trial lasted approximately 2900 ms, each block would have consisted between 10 and 30 trials.  Statistical Analysis We conducted an omnibus ANOVA that included orienting condition (volitional vs. reflexive) as a between-subject factor and both selective attention (cued vs. uncued) and task-related attention (on-task vs. mind wandering) as within-subject factors. The behavioral data for both cued and uncued conditions were based on averaging together RT to the 4 targets preceding each of the two attentional state reports (on-task vs. mind wandering). Our analyses were based on the assumption that the 12 seconds prior to 
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each report would on average reliably capture the reported attentional state (c.f. Kam et al., 2011; Smallwood et al., 2008), given recent evidence suggesting that the time course of attentional states fluctuation approximates this time window (e.g. Christoff, Gordon, Smallwood, Smith & Schooler, 2009; Sonuga-Burke & Castellanos, 2007). The number of events included in the averages was an attempt to maximize the number of events per each average without extending the window so far back in time as to consistently capture the preceding attentional state.  Results Attentional Reports  Participants completed an average of 30 trial blocks, of which 43.2% ended with an on-task report and 56.8% ended with a mind wandering report (SD=19.4%).   Reaction Time  The RT data as a function of orienting conditions, selective attention and task-related attention are shown in Figure 4.2. They suggest that attentional orienting effects were indeed attenuated when in mind wandering states in both orienting conditions.  This pattern was confirmed statistically by a significant interaction between selective and task-related attention (F(1,30) = 6.33; p = .017). We also found significant main effects of selective attention (F(1,30) = 15.38; p < 0.001) and task-related attention (F(1,30) = 5.87; p = .022). There were no significant interactions involving orienting condition, nor a main effect of this factor (all p > .10).  
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Figure 4.2. Reaction times for both volitional and reflexive spatial orienting tasks. Reaction times and standard errors to cued and uncued targets are shown as a function of on-task and mind wandering attentional states. Reaction times to cued targets were significantly faster than uncued targets during on-task states only (* p < .05).  Towards understanding precisely how attentional orienting was affected by mind wandering, we conducted two planned follow-up analyses for the significant selective attention x task-related attention interaction.  The first examined whether selective attention effects were individually present under the on-task and mind wandering states.  This analysis revealed a significant main effect of selective attention in on-task states  (t(31) = 6.65; p < .001; d = 0.42) but not in mind wandering states (t(31) = 0.93, p = .358; d = 0.11). The second planned analyses examined whether RTs changed between on-task vs. mind wandering states for cued trials, uncued trials, or both.  This analysis revealed that RTs were significantly faster during on-task states vs. mind 
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wandering states for cued trials (t(31) = -4.33; p < .001; d = 0.48) but not uncued trials (t(31) = -0.78; p = .441; d = 0.11).  Anticipatory Responses We also examined the participantsʼ anticipatory responses of targets preceding an on-task vs. mind wandering report. Across both tasks, we found that responses made within the cue-target interval during on-task periods (M: 3.56) did not significantly differ from those made during mind wandering periods (M: 2.62; t(31) = 1.02 p = .32). Similarly, responses made within 150 ms of target onset during on-task periods (M: 8.66) did not significantly differ from those made during mind wandering periods (M: 7.94; t(31) = 0.58, p = .57).   Discussion Our findings from Experiment 1 indicate that visual-spatial attentional orienting attenuates during periods of mind wandering. This effect was observed in both volitional orienting elicited by arbitrary stimulus-response associations, as well as reflexive orienting elicited by eyes-mimicking stimuli. Of relevance, this raises the question of how exactly mind wandering might disrupt attentional orienting, and whether this effect could be driven by sensory attenuation of the cue itself – a point to which we return in the General Discussion. Importantly, a key aspect of the data pattern suggests that this finding can not simply be dismissed as participants having a reduced will or motivation to orient their visual-spatial attention during mind wandering attentional states.   That is, 
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the attenuation in orienting during mind wandering states was observed for both volitional and reflexive orienting conditions.  If the effect of mind wandering we found was solely an issue of decreased motivation, it is difficult to understand how that would affect reflexive orienting, which is presumably not under volitional control. As such, our data support the hypothesis that both volitional and reflexive forms of visual spatial orienting are diminished when mind wandering.   That mind wandering attenuated volitional visual-spatial orienting is certainly consistent with what is known about the cortical regions involved in both attentional control and mind wandering.  In particular, volitional spatial orienting engages left dorsolateral prefrontal cortex (e.g., Corbetta, Miezin, & Dobmeyer, Shulman, & Petersen, 1991; Hopfinger, Bounocore, & Mangun, 2000), one of the key brain regions that has been shown to up-regulate activity during periods of mind wandering (e.g., Christoff et al., 2009; Mason et al., 2007). Likewise, neural areas involved in reflexive spatial orienting to eye-gaze cues, including temporaparietal junction and superior temporal sulcus (Hooker et al., 2003; Tipper et al., 2008), are also activated during mind wandering (Christoff et al., 2009; Mason et al., 2007). If mind wandering and spatial orienting do indeed engage a common set of neural regions, then this would explain the absence of visual spatial orienting during mind wandering attentional states––when mind wandering, the cortical areas necessary for visual spatial orienting are unavailable to support that function.   
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   While these data suggest that mind wandering does in fact disrupt visual-spatial attentional orienting, one of the questions driving our study remained unanswered:  Are there any attentional functions that may be preserved during mind wandering, functions that might allow us to adaptively respond to the outside environment despite our "cognitively decoupled" state?  In Experiment 2 we thus examined the impact of mind wandering on a second form of attention that is qualitatively distinct from attentional orienting – that of deviance detection.     Experiment 2  Introduction The occurrence of a stimulus that deviates from the prevailing situational context unavoidably captures our attention. For example, when we hear a physically deviant sound embedded in a sequence of repetitive or standard sounds, it automatically triggers a deviance detection mechanism, the activity of which is indexed by an event-related potential (ERP) component known as the mismatch negativity (MMN; Escera, Alho, Winkler, & Näätänen, 1998; Escera & Corral, 2007; Näätänen, Gaillard, & Mantysalo, 1978; Näätänen, 1990).  Importantly, the major region that contributes to the generation of the MMN, namely bilateral supratemporal cortex (Giard et al, 1995; Giard, Perrin, Pernier, & Bouchet, 1990; Scherg, Vajsar, & Picton, 1989), has not been implicated in mind wandering nor stimulus-independent thoughts (Christoff et al., 2009; Mason et al., 2007). Accordingly, in Experiment 2 we examined the ERP responses to standard and deviant auditory tones as a function of whether or not participants were in 
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a mind wandering state.  If deviance detection is indeed preserved during mind wandering, it predicted that mind wandering should differentially affect the ERP responses to the standard vs. deviant tones.  Methods Participants  20 participants (13 females, 7 males; M=24.6 years, S.D.=7.1) completed the experiment in exchange for $20 Canadian dollars.  They were all right handed, with no history of neurological problems and had normal or corrected-to-normal vision.  Participants provided written informed consent to the experimental procedure, according to the guidelines of the UBC Behavioral Review Ethics Board.   Stimuli and Paradigm   Participants were presented auditory stimuli while they read a book, which is a part of the MMN protocol commonly used as a control condition (Escera et al., 1998; Näätänen, Paavilainen, Tiitinen, Jiang, & Alho, 1993; Sussman, Winkler, & Wang, 2003). The book that each participant read was Francis Baconʼs “Essays”. This book presented the authorʼs perspective on various topics and participants were allowed to read whichever chapter interested them. They were told to concentrate on reading the book and to ignore the tones presented throughout the experimental session.    
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Each trial consisted of an auditory stimulus that was either a standard or deviant tone, and presented for 200 ms at 80dB SPL through headphones. The inter-trial interval was randomly jittered between 500-700ms, thus stimulus onset asynchrony was approximately 800ms. Standard tones were 600Hz and deviant tones were 800Hz, both presented in a random order with probabilities of 0.8 and 0.2, respectively.   Task-Related Attention Our measure of task-related attention is identical to the methods used in Experiment 1. Specifically, participants were asked to report their attentional state as either being on-task or mind wandering at the end of each trial block. Given that each trial lasted approximately 800ms, and that each block lasted 30 s to 90 s, each trial block consisted between 38 and 112 trials. Each subject was tested in a single session lasting approximately two hours. The EEG setup and the experimental task each lasted about 60 minutes. There were no official breaks during the task, however participants were allowed to rest briefly if they had requested.   Electrophysiological Recording and Analysis   During task performance, electroencephalograms (EEGs) were recorded from 64 active electrodes mounted on a cap in accordance to the International 10-20 system using a Biosemi Active-Two amplifier system. Two additional electrodes located over medial-parietal cortex (Common Mode Sense and Driven Right Leg) were used as ground electrodes.  All EEG activities were recorded using a high-pass filter of 0.05Hz, 
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digitized on-line at a sampling rate of 256 samples-per-second. To ensure proper eye fixation and allow for the removal of events associated with eye movement artifacts, vertical and horizontal electrooculograms (EOGs) were also recorded – the vertical EOGs from an electrode inferior to the right eye, and the horizontal EOGs from two electrodes on the right and left outer canthus.  Offline, computerized artifact rejection was used to eliminate trials during which detectable eye movements and blinks occurred. These eye artifacts were detected by identifying the maximum voltage values on all recorded EOG channels from -200 to 600 ms post-stimulus for each event epoch, and then removing the trial from subsequent signal averaging if that value exceeded 150 µV, a value estimated to capture blinks, saccades and other eye movements. An average of 17% of the total number of trials across participants were rejected due to these signal artifacts. The percentage of trials rejected in the on-task vs. mind wandering state did not significantly differ from each other (t(19) = 0.53, p = .700). For each participant, all ERPs were algebraically re-referenced to the average of the left and right mastoid signals, and filtered with a low-pass Gaussian filter at 25.6 Hz to eliminate any residual high-frequency artifacts in the waveforms.  The resulting ERPs were used to generate grand-averaged waveforms.  All ERP data analyses were based on mean amplitude measures using repeated-measures ANOVAs, with specific time-windows of analyses identified below as per each reported ANOVA.  These analysis time-windows were centered on the peak of the relevant component as identified at each electrode site in the grand-averaged 
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waveform. Statistical quantification of ERP data was based on mean amplitude measures relative to a -200 to 0 ms pre-stimulus baseline. The ERP waveforms for each condition of interest were based on averaging together the EEG epochs for the 15 tones (approximately 12 seconds) preceding each of the two attentional state reports (on-task vs. mind wandering). Our analyses were based on the same assumption we made in Experiment 1 that the 12 seconds prior to each report would reliably capture the given attentional state (c.f. Kam et al., 2011; Smallwood et al., 2008).  Results Attentional Reports  Participants completed an average of 41 trial blocks, of which 61% ended with an on-task report and 39% ended with a mind wandering report (SD=10.1%).   Electrophysiology Analysis of the ERP data focused on two issues a priori.  First, we wanted to determine how, if at all, mind wandering affected the automatic detection of deviant auditory signals, as measured via the MMN.  Second, in order to assess whether participants were in fact reliably reporting their on-task vs. mind wandering states, we wanted to examine the amplitude of the sensory-evoked auditory N1 component, a component known to attenuate in amplitude during mind wandering states (Kam et al., 2011). To address these issues, we conducted repeated-measures ANOVAs that included factors of attentional state (on task vs. mind wandering) as well as electrodes; 
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however, for brevity, no main effects or interactions involving electrodes are reported. The specific electrodes included in each analysis are stated in the corresponding sections below. The ANOVA examining the N1 component also included a within-subject factor of tones (standard vs deviant).   Mismatch Negativity. The main focus of ERP data analysis was to assess the effects of task-related attention on deviance detection, as indexed by the MMN elicited by deviant tones. ERP waveforms for on-task and mind wandering states were based on averaging standard/deviant tones separately presented within the last 12 s of each trial block, as described above. As such, we first derived difference waveforms by subtracting the standard tones averaged waveforms from the deviant tones averaged waveforms  (e.g., Escera et al., 1998; Näätänen et al., 1993; Sams, Paavilainen, Alho, & Näätänen, 1985) for on-task and mind wandering states. Next, the MMN mean amplitude of the difference waveforms was then statistically compared between on-task and mind wandering states at midline fronto-central scalp electrode sites, Fz and Cz, where the amplitude of the MMN is typically maximal (e.g., Escera et al., 1998; Sams et al., 1985). The MMN is shown in Figure 4.3c, and the MMN mean amplitudes and standard errors of the mean are shown in Table 4.1. Mean amplitude measures were taken across a 90-150 ms post-stimulus time window. We found a significant main effect of attentional state  (F (1,19) = 7.29, p = 0.01), such that the amplitude of the MMN was greater when attention was in mind wandering vs. on-task state. 
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Figure 4.3. ERP waveforms in response to (a) standard tones and (b) deviant tones, as well as the (c) difference waveforms in Experiment 2. The ERP waveforms are shown as a function of on-task and mind wandering states at electrode sites Fz and Cz. The MMN amplitude appears to be greater during periods of mind wandering relative to on-task. While the amplitude of N1 to standard tones was significantly greater during on-task relative to mind wandering states, this difference was not observed in the N1 to deviant tones.   
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Table 4.1. Mean amplitudes of MMN (90-150ms) and N1 components (95-115ms). Mean amplitudes and standard errors of each component at electrode sites Fz and Cz are presented below as a function of attentional states (on-task vs. mind wandering).      Attentional State Component Tones Electrodes On Task Mind Wandering MMN  Fz -0.98 (0.30) -2.20 (0.38)   Cz -0.53 (0.28) -1.19 (0.23)      N1 Standard Tones Fz -1.93 (0.20) -1.20 (0.35)   Cz -1.87 (0.16) -1.30 (0.19)  Deviant Tones Fz -2.91 (0.32) -3.11 (0.46)   Cz -2.44 (0.28) -2.61 (0.29)  N1. We wanted to examine whether there was a normal sensory attenuation of the standard auditory stimuli during mind wandering periods, as would be predicted by our previous finding (Kam et al., 2011). As such, we compared the N1 component to both tones during on-task vs. mind wandering states. Specifically, we conducted repeated-measures ANOVAs that included factors of attentional state (on-task vs. mind wandering) and tones (standard vs. deviant) to examine the interaction between the two. The N1 elicited by both standard and deviant tones as a function of attentional state are shown in Figure 4.3a and 4.3b, and was examined at midline fronto-central scalp electrode sites, Fz and Cz, where the amplitude of the N1 is typically maximal 
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(e.g., Woldorff, & Hillyard, 1991). The N1 mean amplitudes and standard errors of the mean are shown in Table 4.1. Mean amplitude measures were taken across a 95-115 ms post-stimulus time window. While the main effect of attentional state was not significant (F < 1.00), the main effect of tones was significant (F (1,19) = 38.24, p < .001). Importantly, we found a significant interaction between attentional state and tones (F (1,19) = 5.06, p = .037). Separate analyses revealed a significant main effect of attentional state for standard tones (F (1,19) = 10.84, p < .005), but not deviant tones (F (1,19) = 0.19, p = .669). Specifically, while the N1 elicited by standard tones was significantly greater during on-task states relative to mind wandering states, this difference was absent for deviant tones.   Control Analyses.  Given the results reported above, we wanted to examine an additional control issue concerning our findings. Specifically, we observed residual noise in the pre-N1 portion of the ERP waveforms, and the waveforms for the deviant tones in particular, which may have impacted the reliability of our results. As such, we bootstrapped our current findings with 20 subjects to empirically create more samples in order to establish the reliability of our data. Bootstrapping is a nonparametric approach to analyze ERP data without assuming normality of the sampling distribution (e.g. Keselman et al., 2003; Wasserman & Bockenholt, 1989). This procedure requires resampling of data with replacement, and leads to more accurate inferences (Fox, 2002).  
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First, we determined the average N1 amplitude at both electrodes (ie: Fz and Cz) in the specified time window used in our analyses for each subject, for both standard and deviant tones during both on-task and mind wandering states. Subsequently, we computed the difference between on-task and mind wandering states to represent the attentional difference for both standard and deviant tones. We then performed a bootstrap simulation that involves resampling our subjectsʼ data with replacement for both the standard and deviant tones difference scores. This process involves creating a large number of “bootstrap samples” of 20 data points, with each data point chosen randomly and independently from the original set of 20 data points with replacement. Each subjectʼs data has an equal chance of being chosen at each random draw, and each data point can be selected more than once in each bootstrap sample dataset (Wilcox, 2001).   In generating 4999 bootstrap samples (Fox, 2002) and computing a mean of the scores from each sample, a bootstrapped sampling distribution is formed and the 95th percentile confidence intervals allow one to make inferences about the statistic at hand. In our case, we wanted to determine whether the difference between on-task and mind wandering states is significant for the standard tone, as well as the deviant tone. Since we created bootstrap samples on a difference score, a confidence interval that includes 0 suggests that there was no difference between the two attentional states, thereby leading to the conclusion of retaining the null hypothesis. On the other hand, if the 
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confidence interval does not include 0, then the two attentional states were significantly different from each other, and thus one can reject the null hypothesis.   The 95th percentile confidence interval for standard tones was [-1.224, -0.141] at Fz, and [-1.042, -0.360] at Cz, whereas the 95th percentile confidence interval for deviant tones was [-0.938, 1.243] at Fz, and [-0.485, 0.855] at Cz. That the confidence intervals at both electrodes for standard tones do not include 0 suggests there was a significant difference in the N1 amplitude between on-task and mind wandering states. On the other hand, the confidence intervals for deviant tones did include 0, suggesting the difference in N1 between the two attentional states was not significant at either Fz or Cz. Both conclusions are consistent with our N1 omnibus ANOVA and follow-up analyses.  Discussion   In Experiment 2 we found that the response to deviant stimuli was not only preserved but actually heightened during mind wandering, relative to during on-task attentional states, as measured via the MMN. Moreover, there was an increase in the relative processing of deviant events regardless of attentional state, and a decrease in sensory-related processing of standard events during mind wandering, as measured in the N1 ERP component. This suggests that despite the characteristic down-regulation of sensory processing in auditory cortex during mind wandering (Kam et al., 2011), there is 
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a preserved ability to detect deviant events. We discuss this set of findings below, and expand on these conclusions in the General Discussion.  In examining the difference waveform, we observed that the peak of the MMN (90-150 ms) coincided with the peak of N1 (95-115 ms). That our MMN peaked earlier than the typical MMN at 150-250ms is consistent with past studies that suggested peak latency does become shorter with greater magnitude of stimulus change (Tiitinen et al., 1994; Näätänen et al., 1989; Amendo & Escera, 2000). That is the case for our standard (600Hz) and deviant (800Hz) stimuli, whereas in other studies, the difference in tones is around 100Hz (e.g. Escera et al., 1998). Nevertheless, given the overlap in time window, and owing to the fact that in subtraction waveforms like the MMN, the variance of the waveform is the sum of the variance in the two parent waveforms (e.g., Picton et al., 2000), we draw conclusions below mostly from our data on the N1 waveforms.  General Discussion  The present study examined whether some ongoing attentional functions are preserved while we are mind wandering. In Experiment 1, using traditional performance measures, we found that mind wandering appears to disrupt both volitional and reflexive orienting of visual spatial attention. In Experiment 2, however, using ERP-based measures, we found that mind wandering maintained the detection of unexpected or deviant auditory events but decreased the sensory responses to standard tones, as measured via the N1 ERP component. Taken together, what our data suggest is that 
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our attentional systems adaptively respond to mind wandering, such that under conditions leading to a down-regulation of strategic spatial orienting, there is a preservation of more automatic deviance detection. Given our conclusion, several key issues and questions arise.   First, to what extent is the task-related attention effect in Experiment 1 due to sensory and/or cognitive attenuation of the cue during mind wandering, as opposed to a direct impairment of attentional orienting per se, as we would prefer to conclude? Specifically, if mind wandering did attenuate sensory processing of the cue, the disruption in attentional orienting could be a result of the cue not being processed sufficiently at a sensory (e.g., Kam et al., 2011) and/or cognitive (e.g., Smallwood et al., 2008) level.  Two lines of evidence suggest, however, that this may be a less than complete account of our behavioral findings. For one, the fMRI-based findings of Christoff and colleagues (2009) indicate that periods of mind wandering are also associated with a down-regulation of activity in the same left prefrontal cortical regions consistently associated with the top-down control of selective visual attention (e.g., Corbetta et al., 1993; Hopfinger et al., 2000).  This indicates that mind wandering has the capacity to directly impact the top-down control of attention itself, regardless of any concomitant effects on the sensory and/or cognitive processing of external stimuli. Second, the sensory attenuation reported in Kam et al. (2011) was found specifically for task-irrelevant stimuli in the upper visual periphery, whereas Smallwood et al. (2008) found no sensory attenuation for task-relevant stimuli at fixation. Given that our 
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attention-directing cues were presented at fixation, this would appear to mitigate at least purely sensory-level accounts of our Experiment 1 findings. Regardless of these specific possibilities though, the broader implication is that mind wandering is likely able to exert an impact on attentional orienting in both ways—via reducing the extent to which attentionally-imperative stimuli are processed, as well as directly down-regulating activity in attentional control regions of left prefrontal cortex.   Second, our finding of an attenuation of reflexive attention orienting during mind wandering may be limited to the specific stimulus set used in our study. In order to facilitate reflexive spatial orienting, we used cues that mimic eyes. This particular set of orienting stimulus has successfully elicited automatic shifts of attention in past studies (e.g., Friesen & Kingstone, 1997; Tipper et al., 2007). Notably, such reflexive orienting can be elicited by various types of stimuli, in both visual (e.g. arrows) or auditory (e.g. loud sounds) modalities. Therefore, future studies are needed to elucidate the effects of mind wandering on these other types of stimuli.   A third issue concerns what our findings specifically reveal about the adaptive nature of attentional control during mind wandering. Our data may shed light on why our minds can frequently wander, and yet we are still capable of monitoring and responding to the external environment. In particular, that deviance detection is preserved during mind wandering suggests that we seem to be able to operate on autopilot during mind wandering, but become more automatically vigilant for things out of the ordinary. This 
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would imply, for example, that when mind wandering while driving, we would continue to mind wander so long as the traffic patterns and behaviors of other drivers remain somewhat normal and expected.  However, if something unusual were to happen, such as a driver suddenly veering ahead, our data predict that such events should be readily detected, thereby allowing us to emerge out of our state of automaticity and instead adaptively respond to the deviant event.  Fourth, we found that the N1 to deviant events was preserved during mind wandering, whereas Braboszcz and Delorme (2011) reported an attenuation of the MMN to deviant events during mind wandering compared to breath focus. While this appears to stand in contrast with our finding of a preserved N1 during mind wandering, a notable difference in methodology between our studies may account for our different results. In their study, the condition of mind wandering was compared to the condition of ʻbreath focusʼ; that is, subjects were instructed to count their breath cycles with their eyes closed and to ignore the auditory tones (Braboszcz & Delorme, 2011). On the other hand, we compared between conditions of attention directed towards the reading task and attention away from the task. In other words, our main task required attention to an external, visual stimulus, whereas the ʻbreath focusʼ condition required attention to an internal, non-visual stimulus.   A fifth issue concerns whether mind wandering differentially modulates processing of stimuli presented in different modalities. In Experiment 1, mind wandering 
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disrupted spatial orienting to the visual stimuli, whereas in Experiment 2, mind wandering preserved the detection of the deviant auditory stimuli. At first glance, this may seem to suggest visual and auditory processing are differentially altered by mind wandering. Nevertheless, previous evidence has suggested that mind wandering attenuates sensory processing regardless of whether the stimuli were presented in the visual or auditory modality (Kam et al., 2011). Perhaps the crucial factor modulating stimulus processing does not lie in the modality of stimulus per se, but whether the external stimulus was considered more important or salient than our internal thoughts at any given time. In particular, it has been suggested that our minds generally shield us from mundane sensory events to facilitate internal thoughts (e.g. Barron et al., 2011; Schooler et al., 2011). This is consistent with findings from Experiment 1, where processing of the anticipated stimuli was attenuated. However, when a change occurs in the environment, for example an unexpected stimulus that is potentially harmful or dangerous, we may automatically shift attention from our internal thoughts to the external environment, as observed in Experiment 2 in our response to the deviant stimuli.  Together, this suggests that our minds may engage in an ongoing evaluation of the importance or salience of both external and internal stimuli, the outcome of which affects the allocation of our attention. I will discuss potential mechanisms and control networks that may be involved in this evaluation in Chapter 6.  Lastly, our findings from Experiment 1 raise an important issue concerning the interaction between task-related attention and selective attention. Canonical models of 
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both spatial- (e.g., Posner et al., 1980; Corbetta & Shulman, 2002) and object-based (e.g., Desimone & Duncan, 1995; Duncan, Humphreys, & Ward, 1997) attentional selection implicitly assume that we are always selecting something from the external environment for higher levels of cognitive analysis.  But if mind wandering disrupts selective attention, it would suggest that there are systematic periods of time when we select nothing from the external environment. This is consistent with our previous finding that mind wandering attenuates initial level sensory processing of stimuli (Kam et al., 2011). Our study furthers this idea in two ways. First, we demonstrated that during mind wandering we donʼt simply shut down sensory processing per se, but we do in fact shut down the attentional spotlight. Second, we also showed that we compensate for this dampened spotlight by preserving our sensitivity to the unusual events. In other words, when we donʼt deliberately select for anything to process in the external environment, we maintain vigilance instead.     
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CHAPTER 5: General Discussion   My dissertation aimed to examine how mind wandering alters oneʼs neurocognitive engagement with the external environment. Thus far, I presented three sets of studies that collectively support the conclusion that mind wandering disengages our affective processing, behavioral monitoring and attentional processing of external events. Specifically, in Chapter 2, I examined whether task-related attention modulates the affective processing of naturalistic stimuli with a moderate degree of affective salience. In two experiments, I found that mind wandering attenuated the cognitive response, as measured by the P3, and reduced saliency ratings to affectively salient stimuli. This suggests that being disengaged from the external environment not only leads to transient reductions in our cognitive analysis of contextually impoverished stimuli, but affectively salient stimuli as well. The set of experiments in Chapter 3 demonstrated that periods of mind wandering were associated with increased visuomotor tracking errors, as well as impaired performance monitoring as indexed by a reduced fERN. Findings from these two experiments indicate that mind wandering disengages us from monitoring and adjusting our behavioral outputs, providing a potential mechanism underlying the disruptive effects of mind wandering on behavioral performance. Chapter 4 examined whether mind wandering impacted attentional orienting and deviance detection in a unitary manner. I found that mind wandering was associated with impairments in both volitional and reflexive attentional orienting, as indexed by slower reaction times. Importantly, detection of rare, deviant events in the 
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environment was preserved during periods of mind wandering. These findings point toward a differential impact of mind wandering on our attentional systems, such that under conditions leading to a down-regulation of spatial orienting our sensitivity to unusual events is preserved, possibly allowing us to respond to threatening external stimuli despite our decoupled state.  Collectively, these experiments provide support for the executive function model (Smallwood & Schooler, 2006), suggesting that the attenuation of processing of stimuli in the immediate environment plays an important role in facilitating the production and maintenance of ongoing decoupled thoughts. In particular, mind wandering appears to disengage executive resources from our immediate environment and direct them to inner streams of thoughts via this wide-ranging neurocognitive attenuation. While it remains uncertain whether the reported attenuation effects are in fact driven by the engagement of these neurocognitive processes by decoupled thoughts as the model predicts, the attenuation itself is consistent with that prediction. One exception to this global pattern of attenuation of external processing is the detection of stimuli in our environment that deviates from our expectations. This particular finding shed light on the issue of resource competition among not only external, but also internal stimuli, an issue to which we return below.  This chapter provides a discussion of these findings by integrating across the series of experiments presented. I first provide several points of consideration relevant 
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to my research in mind wandering, with an emphasis on its potential value in elucidating neurocognitive processes underlying clinical disorders. I then conclude with an overview of outstanding questions for future examinations in this emerging field of research.   Points of Consideration  The limitations of each specific experiment have been described in their corresponding chapters. Below, I outline general points of consideration relevant to my research included in this dissertation.  1. Validity of Subjective Reports of Attention  While subjective verbal reports provide a straightforward measure of oneʼs attentional state, a commonly raised concern with this particular method is that it may increase the risk of demand characteristics, and thereby potentially affecting the validity of the reports. There are several reasons why this concern may not be warranted. First, the proportion of on-task vs. mind wandering reports have been relatively consistent across studies regardless of the methodology used, whether participants provided a response verbally or through button press (Christoff et al., 2009; Kam et al., 2011; Kirschner et al., 2012; Smallwood et al., 2008). Second, several lines of research converge on systematic differences between these two attentional states. These studies have revealed reliable differences in activation of neural regions (Christoff et al., 2009; Christoff, 2012; Mason et al., 2007), electrophysiological processing of external stimuli (Kam et al., 2011; 2012; Barron et al., 2011; OʼConnell et al., 2011), ocular patterns 
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(Reichle et al., 2010; Schad et al., 2012), as well as behavioral performance (Kam et al., 2013; Smallwood et al., 2004; 2006). As with any subjective measures in other areas of research, the possibility of demand characteristics remain; nevertheless, that demand characteristics alone may have contributed to the patterns of findings across mind wandering studies seem highly unlikely.   2. Allocation of Executive Resources during Mind Wandering In the context of attentional processing, I found that despite their irrelevance to the ongoing task, the sensory processing of rare, deviant events in the environment was in fact preserved during mind wandering. These findings suggest that the magnitude of sensory-evoked cortical activity during mind wandering episodes may be dependent upon the nature and importance of the external stimulus. That is, if external and internal stimuli compete for executive resources (Smallwood & Schooler, 2006), much in the way stimuli in our visual field compete for selective spatial attention (e.g. Posner, 1980), then perhaps there is a constant evaluation of which stimulus is more important or salient at any given time. While the underlying mechanisms and control networks involved in this evaluation are far from being fully understood, I will discuss potential candidates in the following section. Notably, this is consistent with views on competition of available resources in the context of selective attention. For example, given a single, finite pool of attentional resources, one model suggests that we can only attend to one stream of input at a time, and therefore few if any resources remain for the unselected input (Kahneman, 1970). Alternatively, one perspective relates to the integrated 
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competition hypothesis, which suggests that different objects compete to be represented in consciousness (Desimone & Duncan, 1995). Once selected, the perceptual processing of the selected object in our visual fields is facilitated by the cooperation of multiple brain systems working together to analyze the different properties of that object  (Duncan, Humphreys & Ward, 1997). More generally, this competition process was proposed to occur in not only the sensory domain as suggested by Desimone and Duncan (1995), but also in the emotion and memory domains (Miller & Cohen, 2001).  Importantly, these views apply to the competition between internal and external inputs as well (Smallwood & Schooler, 2006). On one hand, it has been suggested that our minds are shielded from mundane sensory events to facilitate internal thoughts (Schooler et al., 2011). However, when an unexpected event occurs in the environment, one that is potentially dangerous, we may ascribe that event with higher priority and consequently shift our attention to the external environment. Taken together, this suggests even when our mind is wandering, it appears we are still clever about how we selectively disengage from the external environment –– we remain vigilant for deviant or unusual events that may require re-engaging our neurocognitive resources with the external sensory environment.     
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3. Relationship between Attentional Lapse and Mind Wandering  In terms of functional consequences, the phenomenon of decoupling from the external environment appears to be comparable to the phenomenon of attentional lapses. This related line of research revealed that attentional lapses show similar neural and behavioral correlates as mind wandering. In particular, while mind wandering and attentional lapses occur at different temporal levels as described in chapter 3, they appear to capture a similar neurocognitive phenomenon.    First, reduced sensory attention to task-relevant stimuli has been found during brief lapses of attention, as indexed by measures of increased RT (Weissman et al., 2006; Weissman et al., 2009).  This is in line with findings of reduced sensory processing of stimuli during periods of mind wandering (Kam et al., 2011). Second, self-report measures of the tendency to experience attentional lapses have been correlated with measures of memory failures in everyday life (Carriere, Cheyne, & Smilek, 2008), a relationship that further predicted the emotional well-being of individuals. This latter finding is consistent with the observation that dysphoric individuals more frequently reported mind wandering (Smallwood, OʼConnor, Sudberry & Obonsawin, 2007), potentially due to intrusion of negative thoughts. Further, individuals induced with a negative mood were more likely to engage in task-unrelated thoughts than those induced with a positive mood (Smallwood, Fitzgerald, Miles, & Phillips, 2009). Third, the tendency of brief lapses in attention was found to correlate with proneness to boredom (Cheyne, Carriere, & Smilek, 2006). Similarly, task conditions that are repetitive and 
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undemanding are more likely to engender episodes of mind wandering (Smallwood & Schooler, 2006). That the effects of mind wandering appear to parallel effects of attentional lapses supports the notion that task-related attention (or mind wandering) and selective attention (or attentional lapses) may exert similar forms of top-down attentional control on other neurocognitive processes. This is consistent with Dosenbach and colleagues (2008)ʼs hypothesis of the existence of multiple controlling systems operating at multiple scales of time, in our case, allowing for the transient decoupling of neurocognitive system from the external environment.  4. Relationship between the Default Mode Network and Mind Wandering  The Default Mode Network (DMN) is a resting state network of regions that includes the precuneus, posterior cingulate cortex, medial prefrontal cortex and bilateral temporoparietal junction, and has been shown to be more active at rest than during task performance (e.g. Greicius et al., 2003; Gusnard & Raichle, 2001). The functional significance of the DMN includes its role in self-referential thought (e.g. Fingelkurts & Fingelkurts, 2011; Northoff et al., 2006) and autobiographical memory retrieval (Kim, 2012; Svoboda, McKinnon, & Levine, 2006). Given that mind wandering periods have been associated with activations of the DMN (Christoff et al., 2009; Kirschner et al., 2012), to what extent can research in DMN inform us about the functions of mind wandering? For instance, based on the aforementioned roles of DMN and its relationship with mind wandering, one may speculate that we tend to engage in self-referential thoughts or autobiographical memory retrieval during mind wandering. In fact, 
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this has been shown in studies that examined the content of decoupled thoughts (Andrews-Hanna, Reidler, Huang, & Buckner, 2010; Klinger & Cox, 1987; Klinger, 2009). Consistent with these findings, a recent review paper reported that different types of internally guided thoughts tend to differentially activate two subsystems of the DMN, such that the dorsal-medial prefrontal cortex subsystem tends to be recruited during introspection about mental states whereas the medial temporal lobe subsystem is activated during episodic memory retrieval or future-oriented thoughts (Andrews-Hanna, 2013).  Undoubtedly, additional studies are necessary to determine the specific conditions in which we can draw direct inferences about mind wandering based on research findings of DMN.  Potential Clinical Implications  Until now, my discussion of mind wandering has concerned its association with the attenuation of neurocognitive processing of external stimulus inputs, effects that are consistent with the predictions of the decoupling hypothesis (e.g., Schooler & Smallwood, 2006; Smallwood, 2013).  Importantly, the value of describing the impacts of mind wandering on neurocognitive processing goes beyond clarifying and informing on extant models of mind wandering itself to providing a potentially valuable way to consider clinical pathologies.      In particular, several clinical disorders – such as attention deficit/hyperactivity disorder and schizophrenia – have been associated with abnormal patterns of 
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neurocognitive attenuation.  This suggests that attenuation of external environment is not necessarily specific to mind wandering itself, but rather, reflects a general capacity or mechanism we have to insulate ourselves from stimulus events in the outside world as necessary or so desired.  In this section, I speculate how such attenuation relates to clinical disorders of neurocognitive function, with the goal of highlighting the way in which the profile of attenuation itself, or the range of neurocognitive processes attenuated and the magnitude of those effects, can be engaged in a non-normative manner.  This characteristic of abnormal attenuation may provide a novel perspective on an otherwise broad class of clinical disorders.    In previous chapters, I demonstrated that when our minds wander off there is an attenuation of a broad array of neurocognitive processing of stimulus events in the external environment. Similarly, some clinical populations have also shown attenuation of external processing akin to that observed during mind wandering states. Here, I consider the possibility that certain clinical conditions and mind wandering states may engage a common mechanism of neurocognitive attenuation, a possibility that aligns not just with observations of attenuation itself, but its association with activation of the brainʼs DMN (e.g., Greicius, Krasnow, & Menon, 2003; Gusnard & Raichle, 2001). Specifically, not only has the DMN been shown to up-regulate activity during mind wandering (e.g. Christoff et al., 2009; Kirschner et al., 2012; Mason et al., 2007), but this up-regulation has been associated with down-regulation of activity in sensory cortices (e.g. Weissman et al., 2006). While abnormally-heightened levels of DMN 
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activity have been well-recognized in various clinical populations (e.g. Chai et al., 2011; Tian et al., 2006), of relevance here is their potential links to neurocognitive attenuation.  Although these associations are admittedly speculative, by considering how patterns of neurocognitive attenuation may systematically vary with patterns of DMN activity, we may gain newfound leverage in our ability to understand both normal and pathological neurocognitive function.    On the one hand, attention deficit/hyperactivity disorder (ADHD) is a psychiatric disorder characterized by inattention, hyperactivity and restlessness.  Traditionally, one neuropsychological model of ADHD proposed that deficits of executive functions play an important role in the symptom manifestation of this disorder (e.g., Barkley, 1997; Willcutt et al., 2005). This model was supported by evidence of impaired performance on executive function tasks found in ADHD patients (e.g., Faraone & Biederman, 1998; Willcutt et al., 2005). Yet, not all ADHD patients exhibit executive function deficits, indicating that these deficits can explain but are not necessary for the symptoms to occur (e.g., Castellanos, Sonuga-Barke, Tannock, & Milham, 2007). If so, what else might account for the impairment of sustained attention in ADHD?    One hypothesis is that it may reflect functional abnormalities in the DMN; (Sonuga-Barke & Castellanos, 2007). For example, ADHD patients showed greater functional connectivity among regions within the DMN during rest as measured by fMRI (Tian et al., 2006), evidence indicating a hyperactive default mode. Likewise, ADHD 
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patients also showed greater variability in performance measures (Castellanos et al., 2005; Klein et al., 2006), which has been proposed to index attentional lapses in task performance (Sonuga-Barke & Castellanos, 2007). Collectively, these observations suggest comparatively higher levels of activity within DMN in patients diagnosed with ADHD relative to neurologically healthy individuals.    Despite a hyperactive DMN however, ADHD patients showed a heightened propensity for distraction by external stimuli (e.g., Arnsten, 2006; Fassbender et al., 2009).  From the perspective of neurocognitive attenuation, this suggests that they may show a reduction in the magnitude and/or extent of attenuation of external stimuli during DMN activation, relative to non-clinical populations. For example, whereas deviance detection stands out as the sole primary neurocognitive function identified to date that appears to be relatively preserved during mind wandering states (Kam et al., 2013), the profile of "preserved" neurocognitive functions in individuals diagnosed with ADHD may be broader in extent and/or greater in magnitude than what has been normatively defined.  Although admittedly speculative, this possibility represents a clinically-important hypothesis for future investigation.   Critically, however, abnormal profiles of sensory-motor attenuation – including both the range of neurocognitive processes involved and the magnitude of their attenuation – are not theoretically limited to less attenuation than what is normative.  From a clinical standpoint, there could be pathologies tied to increased neurocognitive 
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attenuation relative to non-clinical or mind wandering norms, a possibility that aligns with what has already been observed in some clinical populations.   In schizophrenia, for instance, hyperactivity and hyperconnectivity have both been observed in the DMN (Chai et al., 2011; Ongür et al., 2010; Whitfield-Gabrieli et al., 2009), akin to what has been found in ADHD patients.  Yet at the same time, schizophrenia patients also show reduced top-down attentional modulation of external auditory signals, such that sensory-evoked N1 ERP responses are attenuated relative to nonpsychiatric controls (e.g., OʼDonnell et al., 1994; Salisbury, Collins, & McCarley, 2010).  To date this finding has not been directly linked to mind wandering per se, but they raise the possibility that the broader array of neurocognitive attenuation effects, and/or a greater magnitude of these effects, found in this clinical population can be conceptualized in the context of subjective mental experiences.  Regardless of whether that proves to be the case, the crucial point is that the same general mechanism of neurocognitive attenuation engaged by mind wandering is also implicated in at least several clinical disorders; further, these population-specific patterns of attenuation may be an important – and heretofore underappreciated – functional marker of the given pathology.   While it may prove to be fruitful to consider these clinical pathologies in the context of attenuation profiles, there are clearly other factors involved specific to the disorder that lead individuals to develop one disorder but not the other. For example, 
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executive function capacities appear to play an important role in the development of ADHD, but to a lesser extent for schizophrenia. The interaction between these factors and decoupling abnormalities may together account for more variability in the symptom manifestation and severity of each disorder. These findings highlight the value of considering subjective experiences in understanding the neural processes underlying clinical pathologies.  Future Directions  1. Is Decoupling an Incidental or Necessary Process? An important issue regarding the decoupling process concerns whether its occurrence is incidental or necessary. On the one hand, perceptual decoupling is thought to be necessary to support the continuity of internal thoughts (Smallwood, 2013). In contrast, Franklin and colleagues (2013) question the extent to which perceptual decoupling needs to be actively engaged to insulate these inner thoughts. While this debate is beyond the scope of this dissertation, the literature I presented thus far appears to support the notion that an attenuation of external processing is necessary for the maintenance of our internal trains of thoughts. This is in line with various attention (Kahneman, 1973) and executive function models (Smallwood & Schooler, 2006), suggesting that our finite attentional or executive resources limit the amount of information we can attend to at any given point in time.    
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2. What is the Time Course of Mind Wandering and its Associated Attenuation? An important and related question regarding the mechanism underlying this neurocognitive attenuation concerns the temporal characteristic/order of internal thoughts and attenuation of external environment. That is, does the internal thought appear in consciousness first, followed by the attenuation? Or does the attenuation occur first in order for the internal thought to creep into consciousness? This issue relates to a recent theoretical paper proposing a distinction between occurrence and process of self-generated thought (Smallwood, 2013). Specifically, he argued the process that initiates an episode of self-generated thought is different from the process that maintains the integrity of that thought. Given Smallwoodʼs (2013) proposal that attenuation of external processing occurs to maintain the continuity of internal thoughts, one would predict the attenuation follows the initiation of the thought. However, this question remains to be empirically tested. To address this question would require the identification of the onset of internal thoughts and the temporal characteristic of the attenuation.  3. Can we Predict an Individualʼs Attentional State Online?  My research has demonstrated attenuations in our attentional, cognitive and motor responses to external stimuli during periods of mind wandering (Kam et al., 2011; 2013; 2014), disruptive effects of mind wandering that have been confirmed by other studies (Braboszcz & Delorme, 2011; Barron et al., 2011; OʼConnell et al., 2008). Given 
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these relatively stable neural signatures of mind wandering, can we reliably predict an individualʼs attentional state based on their neural response in the moment?   One approach is to first establish a neural marker of mind wandering specific to the individual. That marker can then be used as an indicator of mind wandering when evaluating neural responses in real time on an individual-by-individual basis. One could presumably compare whether the current incoming neural response matches the previously established neural markers of mind wandering for that particular individual to determine whether their attention was focused externally or internally in real time.    Based on a similar approach, preliminary evidence has revealed that specific patterns of reaction times during reading can be used to predict attentional states online at the individual level (Franklin, Smallwood, & Schooler, 2011). Although these behavioral patterns are specific to the context of mindless reading, they nevertheless suggest the possibility of predicting oneʼs attentional state in real time, a measure that can be used to corroborate individualsʼ subjective reports.  Therefore, future efforts need to be directed towards identifying and using neural markers to predict attentional states rather than simply characterizing neural responses associated with subjectively reported attentional states.    
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4. Is there a Neural Network that Regulates these Attentional Fluctuations? Both theoretical models (Sonuga-Barke & Castellanos, 2007) and empirical evidence (Christoff et al., 2009; Kam et al., 2011; Smallwood et al., 2004; 2008) point to the existence of slow fluctuations between attention towards internally guided thoughts and external environment. There are two main stages involved in these fluctuations, the processing of which seem to require the effort of a superordinate system. First, given that internal and external inputs compete for executive resources (Smallwood & Schooler, 2006), the importance of each incoming stream is likely evaluated and compared. Second, the stream deemed as more important or salient presumably enters consciousness. As distinct as these experiences are, the transition between these attentional states at times appear to be seamless. As such, is there a network of regions that regulates this evaluative process and facilitates the transitions?  One potential candidate is the salience network, comprised of the anterior insula and anterior cingulate cortex, which was proposed to distinguish the most salient stimuli among internal and external inputs in order to guide behavior (Menon & Uddin, 2010). Consistent with the salience network, the frontal parietal network (FPN), which consists of the rostrolateral and dorsolateral prefrontal cortex, anterior insula, dorsal anterior cingulate cortex, precuneus, and the anterior inferior parietal lobule, has also been proposed to serve a similar function (Smallwood, Brown, Baird, & Schooler, 2012). This network of regions was suggested to act as a “global workspace” (Baars, 1988), which adjudicates between internal stimuli and external stimuli competing for access to 
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consciousness (Smallwood et al., 2012). Of relevance, the FPN was hypothesized to work with the DMN in facilitating internally directed thoughts by protecting them from disruptions by the external environment. Evidence supporting this hypothesis comes from neuroimaging research showing that parts of the FPN were activated during periods of mind wandering (Christoff et al., 2009; Christoff, 2012). Taken together, emerging evidence suggests that these networks are potential candidates as regulatory bodies of attentional fluctuations; nevertheless, the exact function of these networks as well as the direction of causality between these networks and the DMN have yet to be determined.   5. What are Functionalities of Mind Wandering? The disengagement of executive resources from the external world during mind wandering appears to play an important role: it allows us to focus in our internal trains of thought. But what exactly is the purpose of engaging in our internal trains of thoughts? Several lines of research have shed light on the functionalities of mind wandering.   For one, both task-unrelated thoughts and the DMN have been positively associated with levels of creativity (Baird et al., 2012; Ellamil et al., 2012). In particular, the greatest improvement in creativity test performance was observed in the experimental condition that elicited the highest levels of mind wandering (Baird et al., 2012).  This finding suggests that periods of mind wandering may act as incubation 
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intervals necessary for creative solutions to be generated, a positive feature that highlights the potential value of mind wandering.   Moreover, the thoughts that individuals engage in tend to be oriented towards the future (Smallwood et al. 2011). Consistent with this finding, the neural regions implicated in future thinking (Schacter et al., 2007) overlap with regions associated with mind wandering (Christoff et al., 2009; Mason et al., 2007). Interestingly, a significant portion of the content of our inner trains of thoughts has been associated with our current concerns, which are considered as goals to be achieved (Klinger & Cox, 1987). Together, these findings suggest that thoughts about current concerns during mind wandering may be used to plan future behavior.   In addition, mind wandering may restore attentional capacity by offering an escape from the task-at-hand. Based on the Attention Restorative Theory, effortful attention can become exhausted over time in an urban environment, but can be restored in an environment that facilitates “fascination” or effortless attention (Kaplan & Kaplan, 1989). For example, a natural environment has been rated as highly effective in restoring attention (Herzog et al., 1997), an effect that was confirmed by findings that a walk in nature allows for recovery of directed attentional abilities (Berman, Jonides, & Kaplan, 2008). While a walk in nature may not always be plausible, might a “mental walk” away from the current task provide a similar albeit potentially smaller effect of attentional restoration? 
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 Accordingly, it appears that mind wandering is not only associated with costs but also with benefits, (e.g., Smallwood & Andrews-Hanna, 2013), an issue that seems to depend on the context in which it occurs, as determined by a host of variables that transcend the actual attenuation itself. While these adaptive features of mind wandering shed light on the practical or functional purpose of mind wandering, the purpose of mind wandering from an evolutionary standpoint remains an open question.  6. Is it Possible to Control these Fluctuations of Attention? If so, how? Despite the negative connotations commonly associated with mind wandering, its aforementioned functionalities make it apparent that the issue is not in the experience itself, but instead lies in the control over the occurrence and duration of this experience. That is, if we engage in our own thoughts in a safe environment when disrupting the task at hand is not detrimental, then mind wandering can afford multiple useful functions. As such, it appears that being in control of when and for how long to mind wander is the key to capitalizing on its benefits while avoiding its disruptive effects.  One approach that has recently emerged with the aim to enhance the control over oneʼs attention is mindfulness training. Preliminary evidence suggests that mindfulness training does indeed improve oneʼs sustained attention (Jha, Krompinger, & Baime, 2007; Tang et al., 2007). Notably, additional research is needed to determine whether these techniques only prolong attention once engaged, or whether they actually allow us to exert explicit control over what enters consciousness.  
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Conclusion In conclusion, my dissertation suggests that decoupled thoughts are facilitated by an attenuation of a broad array of neurocognitive processes that are involved in responding to the immediate physical environment. This attenuation serves the important purpose of buffering our internal trains of thoughts from external distractions and allowing one to fully engage in our decoupled thoughts, whether they reflect "normal" everyday content or are tied to a clinical pathology. That mind wandering occupy much of our awake time, and are tied to a variety of neurocognitive functions highlight the importance of this growing field of research. In order to better understand this ubiquitous phenomenon of mind wandering, it is crucial for future studies to examine the mechanism underlying the occurrence and maintenance of decoupled thoughts, as well as the neural networks involved in the switching between attentional states. Ultimately, a deeper understanding of this phenomenon may inform strategies for regulating this mental experiences so as to maximize thoughts with a productive outcome, and minimize their occurrence during which they may be detrimental to our daily functioning and well-being.   
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