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Are those who [mind]wander lost? : examining the mechanism of mind wandering through its relationship… Pricop, Diana F. 2016

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ARE THOSE WHO [MIND]WANDER LOST? EXAMINING THE MECHANISM OF MIND WANDERING THROUGH ITS RELATIONSHIP WITH SLEEP AND COGNITIVE PERFORMANCE   by   DIANA F. PRICOP B.A. (Hons), Simon Fraser University, 2014   A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF  THE REQUIREMENTS FOR THE DEGREE OF  MASTER OF ARTS in The Faculty of Graduate and Postdoctoral Studies (Psychology)    THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver)   August 2016   © Diana F. Pricop, 2016 ii  Abstract  Whether we like it or not, we spend a large portion of our day thinking introspectively instead of paying attention to what is around us. This is known as mind wandering, and we do it often, are unaware we are doing so, and are impaired cognitively and perceptually during the process. However, it is also true that we sleep often, are unaware we are doing so, and are impaired cognitively and perceptually during the process. Yet we would not conclude that, like mind wandering, sleep is detrimental. There is a newly growing literature providing evidence for potential benefits of mind wandering, as well as suggesting various similarities between mind wandering and sleep. The present study aimed to add to this literature by examining whether the similarity between mind wandering and sleep extends to effects on dissociated aspects of cognitive functioning. Based on two studies showing the pattern of relationship between sleep and mind wandering, and the effect of sleep deprivation on cognitive functioning, the present study examined whether, studied together, sleep and mind wandering had comparable effects on these cognitive performance measures. It was found that sleepiness was a significant predictor of both mind wandering and cognitive performance, replicating the findings of the two previously mentioned studies using a combination of their measures. However, mind wandering, unlike sleep, was not a significant predictor of cognitive functioning. Alternatively, the results suggested that cognitive performance, either executive or non-executive, was not affected by mind wandering, even though both cognitive performance and mind wandering were affected by sleep. This finding suggests that mind wandering does not affect performance on a global level the way it does on an acute level. However, limitations of the study which may potentially undermine this interpretation of the results, as well as consequent future directions, are also discussed.  iii  Preface  All of the data collection for the work presented henceforth was conducted in the Attentional Neuroscience Laboratory at the University of British Columbia, Point Grey campus. All data collection protocols and associated methods were approved by the University of British Columbia’s Research Ethics Board. [BREB Certificate #H09-03295]  This thesis contains solely independent, original, unpublished work conduced by myself, the author. I developed the research question, designed the study, conducted the statistical analyses, and interpreted the results. I also organized and composed the entirety of the thesis manuscript. This work represents a novel approach to the study of the variables of interest.  Dr. Todd Handy, my research supervisor, made intellectual contributions at various stages of the research.   Sumeet Mutti, Tatyana Romeus-Kebe, Fiona Wu, Evelyn Chan, and Aswathi Neelakandan assisted with data collection as well as data entry and cleaning for the study.   The research presented in this thesis was supported by a University of British Columbia Faculty of Arts Graduate Fellowship Award and an NSERC Canada Graduate Scholarship.    iv  Table of Contents  Abstract .................................................................................................................................... ii Preface .....................................................................................................................................  iii Table of Contents ................................................................................................................... iv List of Tables ............................................................................................................................v List of Figures ......................................................................................................................... vi List of Abbreviations ............................................................................................................ vii Acknowledgments ................................................................................................................ viii Dedication ...............................................................................................................................  ix Chapter 1: Introduction .........................................................................................................  1             1.1 The Conceptualization of Mind Wandering ............................................................2             1.2 Evidence for Benefits to Mind Wandering ..............................................................5             1.3 Comparison of Sleep and Mind Wandering.............................................................6             1.4 Overview of Study ...................................................................................................9             1.5 Hypotheses .............................................................................................................13 Chapter 2: Method .................................................................................................................14             2.1 Participants .............................................................................................................14             2.2 Apparatus ...............................................................................................................14             2.3 Materials ................................................................................................................15             2.4 Procedure ...............................................................................................................25 Chapter 3: Results..................................................................................................................27             3.1 Participant Exclusion Criteria ................................................................................27             3.2 Psychometric Properties and Descriptive Measures ..............................................27             3.3 Type I and Type II Error Control ...........................................................................29             3.4 Relationship Between Sleep and Mind Wandering ...............................................32             3.5 Relationship Between Sleep and Cognitive Performance .....................................34             3.6 Relationship Between Cognitive Performance, Mind Wandering, and Sleep .......38 Chapter 4: Conclusion ...........................................................................................................41             4.1 Overview of Findings ............................................................................................41             4.2 Integration Within Current Literature ....................................................................45             4.3 Implications............................................................................................................46             4.4 Limitations .............................................................................................................47             4.5 Future Directions ...................................................................................................49 References ...............................................................................................................................52 Appendix A .............................................................................................................................73 Appendix B .............................................................................................................................77 Appendix C .............................................................................................................................79 v  List of Tables  Table 3.1. Summary of Descriptive Statistics for the Three Measures of Sleep Tendencies .......................................................................................................28 Table 3.2. Summary of Descriptive Statistics for the Measures Associated  with Mind Wandering ......................................................................................28 Table 3.3. Summary of Descriptive Statistics for the Three Measures of  Cognitive Performance ....................................................................................30 Table 3.4. Correlation Matrix of the Relationships Between Mind Wandering  and Sleep Variables..........................................................................................33 Table 3.5. List of Coefficients of the Multiple Linear Regression Model  Predicting Mind Wandering from Sleep Measures ..........................................33 Table 3.6. Correlation Matrix of the Relationships Between Cognitive  Performance and Sleep Variables ....................................................................36 Table 3.7. List of Coefficients of the Multiple Linear Regression Model  Predicting Non-Executive Cognitive Functioning from Sleep  Measures ..........................................................................................................36 Table 3.8. List of Coefficients of the Multiple Linear Regression Model  Predicting Executive Cognitive Functioning from Sleep  Measures ..........................................................................................................37 Table 3.9. Correlation Matrix of the Relationships Between Cognitive  Performance, Centered Mind Wandering, and Centered Sleep  Variables ..........................................................................................................39 Table 3.10. List of Coefficients of the Multiple Linear Regression Model  Predicting Non-Executive Cognitive Functioning from Mind  Wandering and Sleep Measures .......................................................................39 Table 3.11. List of Coefficients of the Multiple Linear Regression Model  Predicting Executive Cognitive Functioning from Mind Wandering  and Sleep Measures..........................................................................................40    vi  List of Figures  Figure 2.1.      Procedural concept of the Sustained Attention to Response Task ..................18 Figure 2.2.      Procedural Concept of the Modified Sternberg Task ......................................21 Figure 2.3.      Procedural Concept of the Probed Recall Task ...............................................23    vii  List of Abbreviations  ESS       Epworth Sleepiness Scale ESS-C       Epworth Sleepiness Scale (Centered) MEQ       Morningness-Eveningness Questionnaire MST       Modified Sternberg Task MW ACC      Mind Wandering Accuracy OT ACC      On-Task Accuracy PRT       Probed Recall Task PSQI       Pittsburgh Sleep Quality Index PVFT       Phonemic Verbal Fluency Task SART       Sustained Attention to Response Task SART-C      Sustained Attention to Response Task (Centered)  viii  Acknowledgments  First and foremost, I express gratitude to my supervisor, Dr. Todd Handy, for all of his support and encouragement. Thank you for giving me the freedom to grow and learn in the way that I chose, but at the same time always being there if ever I needed guidance.  I am grateful to my wonderful research assistants, without whom there would be no data. Sumeet Mutti, Tatyana Romeus-Kebe, Fiona Wu, Evelyn Chan, and Aswathi Neelakandan, thank you for putting in the seemingly countless hours, at the seemingly crazy times of the day, to ensure I had what I needed. Your diligence and dedication have been invaluable.  Thank you to my programmer, Hubert Ngu, who was able to turn the crazed ramblings of a tired graduate student into a powerful program which consistently collected excellent data.  Thank you to Grace Truong and Simon Ho for all of your insight and wisdom, and of course patience, while answering incessant questions. I started in the Attentional Neuroscience Lab viewing you as my senior colleagues; I will finish this journey viewing you as dear friends.   Without the devotion of Shane Virani, I would have emerged from this experience being an empty shell of a person. I cannot convey what your loving companionship has meant to me.  Most importantly, I declare my deepest gratitude to my family. Your unconditional love and support is the reason I have endeavored to achieve my dreams. I will never be able to express how grateful I will forever remain, and how blessed I am that you are always by my side.  ix         To Shane Virani  The first time ever I saw your face I thought the sun rose divine in your eyes And the moon and the stars were the gifts you gave To the dark and the endless skies, my love To the dark and the endless skies  1 Chapter 1: Introduction  Take a moment to think back to what you were doing before you began to read this sentence. Can you remember your state of mind? Perhaps you read the abstract on a previous page. Do you remember what it said? Do you recall the order of the sentences presented, or what was the gist of each? Ultimately, can you say for certain that you paid attention to the words that were on the page? Or, is it perhaps possible that your mind, let’s say… wandered?  The answer to this question is almost certainly “yes”, and it was likely true more than once. However, as evidenced by this example, you likely didn’t realize this was the case until you thought about it, or until you “snapped out” of a mind wandering episode and noticed that you have no clue about the contents of the paragraph you just read even though your eyes have been scanning each sentence word by word. This unawareness of being in the state of mind wandering is one reason among many that mind wandering has been viewed largely negatively in the literature, which has described a variety of situations and abilities that are negatively impacted by this frustratingly ubiquitous and steady phenomenon. Seemingly unfortunately, it is safe to say that people mind wander often, are unaware they are doing so, and are impaired cognitively and perceptually during the process.    However, it is also safe to say that people sleep often, are unaware they are doing so, and are impaired cognitively and perceptually during the process. Yet we would be hard pressed to conclude that this should cast sleeping in a negative light: indeed, we need to sleep to be healthy, and do so readily. Might this also be true of mind wandering? If sleep and mind wandering are similar with respect to the above qualities and with the brain’s predilection to facilitate both on a recurring basis despite their potentially negative 2  consequences, might they be more similar than we realize? Consequently, if sleep is beneficial and necessary, might this be true of mind wandering as well?  The present paper takes steps towards answering these questions in a study which explores the relationships between previously explored aspects of sleep, mind wandering, and cognitive function. The study aims to establish a functional similarity between mind wandering and sleep by comparing their influences on dissociated executive and non-executive aspects of cognitive functioning. If it is the case that mind wandering and sleep perform similar functions or have similar underlying purposes, then we might find evidence of this in the pattern in which they influence different components of cognitive functioning.  1.1 The Conceptualization of Mind Wandering  Mind wandering occurs predictably and often, accounting for up to an estimated 50% of our mental activity during waking hours (Klinger & Cox, 1987; Klinger, 1999; Killingsworth & Gilbert, 2010). It regularly occurs without conscious awareness (Schooler, 2002), and episodes of mind wandering vary with respect to their length, mental contents, and frequency of occurrence (time between episodes). Mind wandering can be broadly defined as the disengagement of attention from the external environment in favor of attentional focus being directed inwards to internal mental processing. These episodes of internally focused cognition, also referred to as stimulus independent (Antrobus, Singer & Greenberg, 1966) or, more recently, task-unrelated (Giambra, 1989) thoughts, are believed to be disjointed from the external perceptual environment (Bastian & Sackur, 2013; Mittner et al., 2014; Hawkins, Mittner, Boekel, Heathcote & Forstman, 2015). In contrast to a state of perceptual coupling, in which attention is directed towards the (external) sensory input currently being perceived, mind wandering is said to represent a state of perceptual 3  decoupling, in which there is a disconnect between the focus of attention and the perception of external stimuli (Smallwood, 2011; Smallwood, 2013).  If the mind is in a state of perceptual decoupling, then it is not constrained to the time and place of the task at-hand, or the here-and-now, so to speak (Wegner, 1997; Mason et al., 2007; Schooler et al., 2011). Research has shown that a bias exists towards thinking about the future during episodes of mind wandering in both laboratory and naturalistic settings (Baird, Smallwood & Schooler, 2011; Song & Wang, 2012; Iijima & Tanno, 2012; Ruby, Smallwood, Sackur & Singer, 2013). Past-focused thinking also occurs, although this tends to be correlated more with low mood (Smallwood & O’Connor, 2011; Ruby, Smallwood, Engen & Singer, 2013; Poerio, Totterdell & Miles, 2013), while mind wandering in general has been correlated with unhappiness (Killingsworth & Gilbert, 2010; Smallwood et al, 2004). Indeed, mind wandering represents an instance in which the internal focus of attention allows for thoughts to occur unbounded about any time, place, or subject. While this can be an adaptive strategy when the nature of the current external environment is inconsequential (Smallwood et al., 2011), it may prove otherwise when the opposite is true (e.g. when one is trying to focus on an external task or stimulus), especially given that mind wandering often occurs without conscious attention or conscious intention.  Indeed, it is because mind wandering is specifically characterized by a decoupling of attention from the immediate perceptual demands of the task at-hand to internal, unrelated mental concerns (Schooler et al., 2011) that it has long been associated with a plethora of disruptions or impairments affecting a wide spectrum of activities and abilities. The “costs of mind wandering” have thus been described with respect to sensory and cognitive processing (O’Connell et al., 2009; Braboszcz & Delorme, 2011; Kam et al., 2011; Smallwood et al., 4  2011; Barron, Riby, Greer & Smallwood, 2011; Smallwood et al., 2013), working memory (Smallwood, McSpadden & Schooler, 2007; McVay, Kane & Kwapil, 2009; He, Becic, Lee & McCarley, 2011; Kam et al., 2012; Mrazek et al., 2012), sustained attention and attentional orienting (Stawarczyk, Majerus, Maj, Van der Linden & D’Argembeau, 2011; Hu, He & Xu, 2012; McVay & Kane, 2012; Szpunar, Khan & Schacter, 2013; Farley, Risko & Kingstone, 2013; Kam, Dao, Stanciulescu, Tildesley & Handy, 2013; Mrazek, Franklin, Phillips, Baird & Schooler, 2013), memory (Smallwood, Baracaia, Lowe & Obonsawin, 2003; Smallwood, O’Connor, Sudbery & Obonsawin, 2007; Riby, Smallwood & Gunn, 2008; Risko, Anderson, Sarwal, Engelhardt & Kingstone, 2012), mood (Smallwood, Fitzgerald, Miles & Phillips, 2009; Killingsworth & Gilbert, 2010; Smallwood & O’Connor, 2011), and, notably, reading comprehension (Schooler, Reichle & Halpern, 2004; Smallwood, McSpadden & Schooler, 2008; Reichle, Reineberg & Schooler, 2010; Franklin, Smallwood & Schooler, 2011; Uzzaman & Joordens, 2011; Schad, Nuthmann & Engbert, 2012; Unsworth & McMillan, 2012; Dixon & Bortolussi, 2013; Feng, D’Mello & Graesser, 2013).   While the perception may be that mind wandering is a generally negative experience given the multitude of research highlighting its costs, it is important to realize that the context in which the mind wanders is an important aspect of this phenomenon. Indeed, it has been suggested that the costs of mind wandering will likely be better understood by taking this approach (Smallwood & Andrews-Hanna, 2013; Andrews-Hanna, Smallwood & Spreng, 2014). For instance, mind wandering is less detrimental in tasks in which performance can be automated (Teasdale et al., 1995), or when monitoring and encoding immediate input is not as important (Ruby et al., 2013). Further understanding the mechanics of how mind 5  wandering varies in different contexts might allow us to make sense of why such a seemingly deleterious phenomenon is so ubiquitous, and would allow a more seamless integration with findings demonstrating the various benefits that mind wandering has been shown to have.  1.2 Evidence for Benefits to Mind Wandering  Despite the numerous examples of mind wandering being detriment to performance, it remains odd that such a frequently occurring phenomenon would be a solely negative facet of mental life. Indeed, since the inception of the study of mind wandering there has been speculation that it must serve some useful purpose (Singer & Antrobus, 1963), although this idea was not popular at the time. Today, an increasing body of literature is beginning to reveal various situations and aspects of mind wandering which have been shown to be positive, if not legitimately useful.   It has been shown that mind wandering episodes largely feature thoughts geared towards the future (Smallwood, Nind & O’Connor, 2009; D’Argembeau, Renaud & Van der Linden, 2011), especially when the environmental context facilitates these episodes (Baird, Smallwood & Schooler, 2011). Thus, one popular theorized benefit of mind wandering is that it facilitates anticipating the future and planning of future goals (Mooneyham & Schooler, 2013) given its future-directed orientation and the often personal nature of the spontaneous thoughts that occur (Klinger, 1999; Smallwood, O’Connor, Sudberry, Haskell & Ballantyne, 2004; McVay & Kane, 2010). This prospective function can certainly be seen as beneficial to daily life (Baumeister & Masicampo, 2010; Baumeister, Masicampo & Vohs, 2011), especially when paired with the facilitation of an ability to concurrently weigh the potential obstacles and benefits surrounding goals, such as mental contrasting (Oettingen & Schworer, 2013). 6   An increasingly well-documented benefit of mind wandering has been its involvement with creativity. Given that mind wandering represents an attentional disconnect from the external environment, it creates a situation in which self-generated thoughts can occur which have no spatial or temporal bounds. That is to say, anything can “come to mind”. This provides a mental environment which has been shown to give a greater incubation benefit (Sio & Ormerod, 2009; Baird et al, 2012) and lead to faster problem-solving (Ruby et al., 2013).  In a similar manner as incubation, mind wandering may also provide the situation needed to contemplate one’s experiences and put them into meaningful contexts. It has been shown that finding meaning in personal experience can nurture well-being (Janoff-Bulman, 1992) and even ameliorate health outcomes (Taylor et al., 2000). Given that mental time travel has been shown to increase self-reported feelings of meaning in life (Waytz, Hershfield & Tamir, 2015), and mind wandering episodes facilitate this activity by their spatially and temporally decoupled nature, this might prove to be an especially important benefit.   In a more passive context, mind wandering may simply be a good way to take mental breaks from the external environment when it becomes monotonous or boring (Ruby et al., 2013). Further, by enabling dishabituation, it may also facilitate learning by ameliorating the negative effects of hindrances such as semantic satiation (Mooneyham & Schooler, 2013).  1.3 Comparison of Sleep and Mind Wandering Despite its increasing prominence in the cognitive literature, it is important to realize that mind wandering is not the only phenomenon which has been associated with a shifting of attention away from the external environment. Specifically, sleep deprivation has also been associated with reduced cognitive functioning (Kronholm et al., 2009; Nebes, Buysse, 7  Halligan, Houck & Monk, 2009; Xu et al., 2009), lapses in attention (Durmer & Dinges, 2005; Akerstedt, 2007; Alhola & Polo-Kantola, 2007), and even impaired speech (Harrison & Horne, 1997). Individuals who consistently sleep less than the recommended amount of eight hours per night often are at a higher risk of suffering from chronic diseases such as depression and hypertension (Ohayon & Roth, 2001; Shahar et al., 2001; Cole & Dendukuri, 2003; Perlis et al., 2006; Launois, Pepin & Levy, 2007). Interestingly, a departure in sleep time of just a few hours from this recommended amount over time has also been associated with poorer performance on a number of cognitive tests (Faubel et al., 2009; Ferrie et al., 2011) and even increased risk of mortality (Kripke, Garfinkel, Wingard, Klauber & Marler, 2002; Ayas, White & Manson, 2003; Tamkoshi & Ohno, 2004). While a causal role for reduced sleep duration leading to adverse health outcomes is still being debated in the literature, more and more evidence has emerged which suggests that a lack of sleep leads to a variety of different adverse physiological conditions (Tochikubo, Ikeda, Miyajima & Ishii, 1996; Spiegel, Leproult & Van Cauter, 1999; Kato et al., 2000; Rogers, Szuba, Staab, Evans & Dinges, 2001; Taheri, Lin, Austin, Young & Mignot, 2004; Spiegel et al., 2004; Gangwisch, Malaspina, Boden-Albala & Heymsfield, 2005; Banks & Dinges, 2007). In addition to physiological consequences, numerous cognitive conditions such as experiencing marked difficulties in concentrating on daily activities (Doran, Van Dongen & Dinges, 2001; Balkin, Bliese & Belenky, 2004; Durmer & Dinges, 2005), and even impacts on overall health and cognitive function (Roth, 2015) have also been associated with sleepiness. Among adolescents in particular, sleepiness results in decrements in executive function (Andersen & Teicher, 2008; Anderson, Stofer-Isser, Taylor, Rosen & Redline, 2009). This has also been shown in clinical populations (Rauchs et al., 2008; Claassen et al., 2010; Yaffe et al., 2011). 8  Unsurprisingly, sleepiness has similar negative effects to mind wandering on cognition, and studies have recently begun investigating the relationship between these two phenomena. Evidence has emerged showing that sleepiness results in increased reaction time and lower accuracy in a variety of attentional paradigms (Durmer & Dinges, 2005; Akerstedt, 2007; Alhola & Polo-Kantola, 2007; Killgore, 2010; Short & Banks, 2014). Additionally, evidence from studies using EEG has shown that functionally, mind wandering states present in a similar manner to those of low alertness (Braboszcz & Delorme, 2011). Further, despite the recency of direct investigation on this topic, many previous studies have mentioned ways in which mind wandering and sleep appear to be similar (Antrobus, Singer & Greenberg, 1966; Kunzendorf, Brown & McGee, 1983; Mikulincer, Babkoff, Caspy & Weiss, 1990; Ottaviani, Shapiro & Couyoumdjian, 2013; McVay, Kane & Kwapil, 2009).  Adding to the similarity of these two phenomena is evidence which suggests that both dreaming during REM sleep and mind wandering may engage similar thought processes. The REM stage of sleep involves the recruitment of a variety of higher cortical brain regions in which the brain is generally more active, and occurs approximately every 1.5 hours throughout the sleep cycle (Fox, Nijeboer, Solomonova, Domhoff & Christoff, 2013). Incidentally, the prefrontal cortex is often negatively affected when an individual suffers from sleep deprivation, due to a subsequently decreased brain metabolism (Muzur, Pace-Schott & Hobson, 2002). This is problematic, as the prefrontal cortex engages in increased activity during attention tasks (Drummond, Gillin & Brown, 2001) and, as stated previously, the prefrontal cortex benefits from sleep. Thus, sleep deprivation leads to prefrontal cortex-related neurological impairments (Horne, 1993). The neural substrates of the default mode network, which is a network of prefrontal brain regions which exhibit increased activation 9  with decreased task demands recently shown to be associated with mind wandering (Christoff, Gordon, Smallwood, Smith & Schooler, 2009), have also been associated with REM sleep (Fox et al., 2013). Reduced cognitive performance caused by sleep deprivation subsequently reduces activity in the fronto-parietal regions of the brain which are largely used to support attentional control. However, this also leads to increased activity in the default mode network (Fox, Spreng, Ellamil, Andrews-Hanna & Christoff, 2015).  Interestingly, research has also shown that during an incubation interval, being engaged in REM sleep allows for an enhanced ability to integrate unassociated information and solve problems (Cai, Mednick, Harrison, Kanady & Mednick, 2009). In addition, the finding that associative networks can lead to incubation effects during REM sleep parallels similar processes associated with mind wandering (Smallwood, Obonsawin & Heim, 2003). It has also been observed that mind wandering and sleepiness frequently co-occur (Ottaviani & Couyoumdjian, 2013), and mind wandering has been proposed to occur when the ability to control and maintain attentional processes diminishes (Thomson, Besner & Smilek, 2015; Smallwood & Schooler, 2015). Therefore, it is possible that those top-down control processes which are supported by the frontal and parietal regions of the brain can be impaired during times of sleep deprivation, and this in turn may cause an increase in mind wandering (Smallwood, 2013; Stawarczyk & D’Argembeau, 2016).  1.4 Overview of Study While there is growing evidence that mind wandering might be more beneficial than has been traditionally believed, and that there are similarities between sleep and mind wandering, the relationship between sleep and mind wandering in the context of their influence on cognitive functioning and the implications that may be drawn from such a 10  relationship have not been directly explored. The present study takes an exploratory look at such a relationship between sleep and mind wandering, and aims to add to the literature outlining positive aspects of mind wandering by showing that it is similar to sleep in the way that it affects dissociated components of cognitive functioning. By doing so, it aims to show that mind wandering, a recurring state which has been shown on its face to have a number of attentional disadvantages when it occurs, may yet be functionally useful if it exhibits the same patterns of influence on cognitive performance as sleep, a state which also renders one functionally useless when it occurs but yet has undisputed benefits and is indeed biologically necessary.  The study collected scores on measures relating to sleep, mind wandering, and cognitive function and analyzed key relationships utilizing multiple linear regression. The study was thus correlational in nature and aimed to find a similarity in the way that dissociated aspects of cognitive performance were affected (predicted) by measures of sleep and mind wandering. It principally built upon the measures and findings of two unrelated studies which provided the conceptual basis on which the study was designed. The first study, examining the relationship between sleep and mind wandering, provided the measures used to assess sleep and a benchmark for the relationship between sleep and mind wandering that the present study aimed to replicate. The second study, examining the relationship between sleep and dissociated components of cognitive function, provided the measures used to assess cognitive function and again provided a benchmark for the relationship between sleep and cognitive function which the present study also aimed to replicate. The methods and results of these studies are outlined below. 11  In the first paper, examining sleep and mind wandering, Carciofo, Du, Song and Zhang (2014) conducted an exploratory study which assessed the relationship between sleep and mind wandering (and daydreaming), and how these might be affected by chronotype and affect. Their study was correlational, and employed questionnaire measures of all of the variables of interest. Specifically, they assessed morningness and eveningness using the Morningness and Eveningness Questionnaire (MEQ: Horne & Ostberg, 1976), daytime sleepiness using the Epworth Sleepiness Scale (ESS: Johns, 1991), and subjective sleep quality using the Pittsburgh Sleep Quality Index (PSQI: Buysse, Reynolds, Monk, Berman & Kupfer, 1989). These three measures were subsequently used in the present study. Carciofo et al. (2014) found that higher frequencies of mind wandering and daydreaming (assessed in their study using questionnaires not used in the present study) were associated with poorer subjective sleep quality and increased daytime sleepiness and dysfunction, and that these influences were not as robust in those that showed greater morningness. Overall, their results showed that the more their subjects reported they mind wandered (and daydreamed), the poorer they reported their sleep and daytime function to be.  The present study aimed to find a similar pattern of results with the same questionnaires assessing sleep. However, instead of the questionnaire measure of mind wandering used by Carciofo et al. (2014), the Sustained Attention to Response Task (SART) was used as the measure of mind wandering in the present study in order to measure mind wandering propensity in a novel, behavioural way in this context. In the second paper, examining sleep and cognitive performance, Tucker, Whitney, Belenky, Hinson and Van Dongen (2009) conducted an experiment in which they observed the effects of sleep deprivation on dissociated components of cognitive performance. 12  Specifically, Tucker et al. aimed to determine whether conflicting findings regarding sleep and cognition in the literature may be due to the way in which cognitive performance is measured. Therefore, the cognitive performance tasks they used in their study were able to dissociate between executive components (e.g. working memory scanning efficiency, suppression of irrelevant information) and non-executive components (sustained attention) in order to show that it was the latter component which was responsible for previous results showing the former component being impaired by sleep deprivation, as executive cognitive performance is dependent on non-executive performance (Doran, Van Dongen & Dinges, 2001). These tasks were the Modified Sternberg Task (MST: Sternberg, 1966), the Probed Recall Task (PRT: Bunting, 2006) and the Phonemic Verbal Fluency Task (PVFT: Benton, Hamsher & Sivan, 1994), and were subsequently used in the present study. Tucker et al. (2009) measured performance on these tasks in a control condition and a condition in which participants were sleep deprived for 51 hours. They found that executive cognitive performance was consistently spared across all tasks, while the MST showed significantly impaired non-executive cognitive performance following sleep deprivation, and concluded that executive functions may not be vulnerable to sleep loss in the way that non-executive functions are.  The present study aimed to find a similar pattern of results with the same cognitive performance tasks and the sleep questionnaire measures used by Carciofo et al. (2014). This was done in order to connect the findings of Carciofo et al. with those of Tucker et al (2009), and to determine whether the latter findings could be replicated using self-report measures of sleep.   13  1.5 Hypotheses  The aim of the present study was to create a framework in which the findings of the two studies presented could be integrated to answer questions about the nature of mind wandering as it relates to sleep. Measures of sleep and cognitive performance derived from two studies of interest, as well as a behavioural measure of mind wandering, were combined to examine the similarity of the relationship between sleep and mind wandering as they influence cognitive performance, resulting in the following hypotheses:   1. The relationship between sleep and mind wandering found by Carciofo et al. (2014) will be reflected in the present study with a behavioural measure of mind wandering. Specifically, scores on the SART measuring mind wandering will be predicted by scores on the MEQ, ESS, and PSQI measuring sleep. 2. The relationship between sleep and cognitive function found by Tucker et al. (2009) will be reflected in the present study with self-report measures of sleep. Specifically, scores on the MST, PRT, and PVFT representing non-executive, but not executive, components of cognitive function will be predicted by scores on the MEQ, ESS, and PSQI. 3. Integrating the two studies, the relationship between mind wandering and cognitive performance will mirror the relationship between sleep and cognitive performance. Specifically, scores on the MST, PRT, and PVFT representing non-executive, but not executive, components of cognitive function will be predicted by scores on the SART, and this pattern of results will mirror that of Tucker et al. (2009).  14  Chapter 2: Method 2.1 Participants A total of 358 participants were recruited for participation in this study. Of these participants, 310 were recruited through UBC’s Human Subject Pool in exchange for partial course credit, and the remaining 48 were recruited through an advertised posting on the UBC Psychology Paid Participant Studies List website in exchange for a cash remuneration of $10. All participants indicated normal or corrected-to-normal vision and provided written informed consent before participation in the study. Ethics approval for the present study was granted by the University of British Columbia’s Behavioural Research Ethics Board. After the application of exclusion criteria (outlined in the Results chapter), the data from a total of 270 participants (204 female) were ultimately used in subsequent analyses (Age: M = 21.03, SD = 5.32).    2.2 Apparatus Testing sessions for all participants were held in the same small, well-lit room which accommodated the testing of two participants at a time, separated by a sound-insulated wall. Participants were sat at a small desk comprising a computer, keyboard and mouse. All stimuli were presented on an LG Flatron L1942T 19-inch LCD monitor with 1280x1024 resolution. The mouse and keyboard were used by participants to navigate the questionnaire portion of the study, which was facilitated by Google™ Forms on the Google™ Chrome web browser. The Enter, spacebar, and < > keys on the keyboard, specifically, were used by participants to complete the mind wandering and cognitive performance portion of the study, facilitated by a custom (Elliott, Ngu & Rensink, 2015) Java™ Applet (hosted by GitHub) and Microsoft® Notepad. All testing was done using the Microsoft® Windows® 7™ operating system. 15  2.3 Materials Morningness-Eveningness Questionnaire (MEQ). The morningness-eveningness questionnaire, developed by Horne & Ostberg (1976), is a self-assessment measure designed to determine one’s chronotype. Chronotype, in this capacity, is defined as the time of day that a person is at their peak alertness (e.g. in the morning or in the evening) and is dependent on the circadian rhythm. This has been correlated with peak body temperature, as morning-types tend to have their body temperature peak early in the day whereas the opposite is true for evening-types, and intermediate-types have their peak sometime in-between. Scores are grouped into three chronotype categories, with each participant falling into one category depending on their questionnaire responses: evening (4-11), neutral (12-17), and morning (18-25). An overview of this questionnaire can be found in Appendix A.  Epworth Sleepiness Scale (ESS). The Epworth Sleepiness Scale (Johns, 1991) is a self-assessment measure designed to determine one’s level of daytime sleepiness by asking participants how likely they are to fall asleep while in the context of eight different day-to-day scenarios. Given that asking about the likelihood of falling asleep during the day has not proven to be an effective method of assessing daytime sleepiness, the ESS circumvents this by asking participants how likely they are, on a scale of 1-3, to doze off during more – or less – soporific daytime activities. It is assumed that people at each end of the spectrum will answer certain questions in predictable, yet telling, ways and as such the ESS has been shown to be a valid measure of daytime sleepiness. Participants can receive a tallied score between 0 and 24, with a higher score indicating a greater sleep propensity. An overview of this questionnaire can be found in Appendix B.  16  Pittsburgh Sleep Quality Index (PSQI). The Pittsburgh Sleep Quality Index, developed by Buysse et al. (1989), is a self-assessment measure designed to evaluate the quality of sleep and sleep disturbances in normal and clinical populations over a one-month period. Participants respond to 19 individual items which are used to generate individual scores on seven sub-components: subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleeping medication, and daytime dysfunction. A global overall sleep quality score is generated by summing the scores of the equally-weighted sub-components. Each sub-component provides a score on a scale of 0-3, with higher scores indicating more problems on each component. The global score can range from 0 to 21, again with a higher score indicating more (general) sleep problems. An overview of this questionnaire can be found in Appendix C.  Sustained Attention to Response Task (SART). The sustained attention to response task (SART) was originally developed as an assessment tool for victims of traumatic brain injury, capable of reliably measuring the frequency of “attentional slips” (Robertson, Manly, Andrade, Baddeley & Yiend, 1997). Given its success as a measure of sustained attention partly, though not in spite of, as a result of its monotonous nature (Cheyne, Carriere & Smilek, 2006; Allan Cheyne, Solman, Carriere & Smilek, 2009; Smallwood & Schooler, 2015), it has been combined with self-report measures of mental state (Smallwood & Schooler, 2006; Schooler et al., 2011; Stawarczyk, Majerus, Maquet & D’Argembeau, 2011; Mittner et al, 2014) to be used as a method of assessing one’s propensity to mind-wander (Smallwood et al., 2008; Christoff et al., 2009; Kirschner, Kam, Handy & Ward, 2012). These self-report measures, namely experience-sampling, are regarded as direct measures of mind-wandering by relying on one’s ability to self-report their mental state when prompted. 17  This technique allows participants to declare whether they are on-task, defined by focusing their attention to the task at-hand, or whether they are mind-wandering, defined as having had their “minds drift” to other mental states or thoughts (e.g. Giambra, 1995; Smallwood et al., 2004; Seli, Jonker, Cheyne, Cortes & Smilek, 2015). This has been shown to even be effective in the scope of establishing differences in neurocognitive functioning between these two states (McKiernan, D’Angelo, Kaufman & Binder, 2006; Mason et al., 2007; Andrews-Hanna, Reidler, Huang & Buckner, 2010; Stawarczyk et al., 2011). Thus, the SART can dissociate when participants are externally focused (on-task) from when they are internally focused (mind-wandering) and can therefore provide a measure of one’s propensity to mind-wander.  The SART requires participants to make a response (e.g. key press or button press) to a range of visual targets (e.g. digits) presented serially above a fixation point, while simultaneously withholding response to a non-target (e.g. the letter X). While there is a continuous stream of targets to respond to, the non-target is presented occasionally, thus requiring the participants to maintain attentional focus despite the relative ease of the task in general. The aim of the task is to be tedious enough to induce mind-wandering states, yet engaging enough (as the participant needs to pay attention for the appearance of the non-target) to maintain motivation to sustain attention. At the end of every block of trials, subjects are asked to report their mental state. Specifically, they are asked whether they were on-task (explicitly focused on performing the task) or mind-wandering (thinking about something other than the task) in the few seconds before the block ended. The ratio of the number of blocks they indicated they were mind-wandering to the total number of blocks in the task constitutes their rate of mind-wandering.  18  Participants were presented with written instructions and could additionally ask for oral instructions from the research assistant. A spacebar press navigated away from the instruction page and began the task (a procedural depiction of which is located in Figure 2.1). Participants completed a total of 40 blocks of trials. Stimuli in the task were single digits (0-9) and the uppercase letter X, presented serially. Participants were instructed to press the spacebar key every time a digit was presented, but to make no response when X was presented. Blocks consisted of between 15 and 25 trials. At the end of each block, participants were presented with a screen asking the question “Were you mind-wandering?”.    Figure 2.1. Procedural Concept of the Sustained Attention to Response Task.  19   They were instructed to press the < key for “yes”, or the > key for “no”. Once they made their response, a new block of trials began. Their answer to this question, their response time to the targets and non-targets given their answer, and their accuracy for not responding to the non-targets given their answer were recorded, and the propensity to mind wander (ratio of mind-wandering trials to total trials) was calculated. Upon completion of the task, participants were presented with a screen instructing them to alert the research assistant.  Modified Sternberg Task (MST). In the classic version of the Sternberg Task (Sternberg, 1966), participants are given a “memory set” of visual objects which they are asked to hold in working memory for several hundred milliseconds. Subsequently, they are shown a probe item and asked if the probe appeared in the memory set. The set size (number of items in the memory set) is varied across trials, and reaction time from the onset of the probe to the participant’s answer is recorded. The linear relationship between set size and reaction time provides a measure of working memory scanning efficiency (Sternberg, 1969). The slope of this relationship is considered to be a measure targeting the executive function aspects of working memory scanning efficiency. Alternatively, the intercept of the linear relationship captures the remaining, non-executive component processes though to be involved in performing the task. Together, these two dissociated components of the linear relationship provide measures of both executive and non-executive functions which can subsequently be directly compared.  The version of the Sternberg Task used for the present study, the Modified Sternberg Task (Monsell, 1978; Bunge et al., 2001; Whitney, Jameson & Hinson, 2004), used the same properties as the original with an added dissociative component in order to separate out a 20  further aspect of executive function. Along with the set size, the recency of the probe was also manipulated. On trials in which the probe was not present in the memory set (referred to as a negative probe), it was possible that the probe had been presented in the pervious trial’s memory set (referred to as a recent probe). This manipulation measures the resistance to proactive interference by comparing the reaction time difference between recent and non-recent negative probes, providing an additional measure of executive function.  Participants were presented with written instructions and could additionally ask for oral instructions from the research assistant. A spacebar press navigated away from the instruction page and began the task. Participants completed a total of 110 trials. Stimuli in the task were upper-case consonant letters. On every trial, participants were presented with a memory set composed of a random subset of (non-repeating) letters. After a brief period, the memory set disappeared and was replaced with the memory probe, which consisted of a single letter. Upon the presentation of the memory probe, participants had 2000 milliseconds to respond before the next trial began. If they did not respond in this time-window, the trial was considered a miss (constituting an error of omission). Participants used the < and > arrow keys to respond “yes” or “no”, respectively, to the question “Was this item in the set?”. Half of the trials contained a memory set of 2 letters, and the other half contained a memory set of 4 letters. The memory probe was present in the memory set on 50% of the trials. Additionally, on the trials in which the memory probe was not present in the memory set, it had been present in the previous trial’s memory set 50% of the time. Participants’ accuracy, reaction time, and omissions were recorded for the four primary conditions of interest: 2-item set, 4-item set, recent negative probe, and non-recent negative probe. Figure 2.2 depicts an 21  example of the procedural concept of the task. Upon completion of the task, participants were presented with a screen instructing them to alert the research assistant.     Figure 2.2. Procedural Concept of the Modified Sternberg Task.    Probed Recall Task (PRT). The Probed Recall Task was developed to demonstrate that interference is an important aspect of the relationship between general fluid intelligence and working memory span (Bunting, 2006), but is more generally used to study working memory and is a robust measure of proactive interference and resistance to proactive interference. In this task subjects are shown and asked to remember, in order, a set of 12 22  serially-presented items (similar to the SART). The items are either neutral words (e.g. ribbon) or single digits. At the end of the serial stream, a probe is also shown (the letter A, B, or C). Subsequently, subjects are asked to recall a subset of the serially presented items corresponding to the probe shown. If they see the letter A at the end of the serial list, they are to recall the first 4 items. If they see the letter B, they are to recall the middle 4 items, and similarly recall the last 4 items if they see the letter C. They must recall these items in the order they were shown, and are asked to do this as quickly and as accurately as possible.  The frequency that words and digits are displayed is counterbalanced. On 50% of the trials, all the items presented in the serial list are the same (i.e. all digits or all words). This induces proactive interference as subjects must remember 12 similar items and the order in which they were presented. On the other half of the trials, the last 4 items are different (i.e. 8 words and 4 digits, or 8 digits and 4 words), which releases interference as the last four presented items are different and thus more easily remembered. Thus, the difference in scores between the “interference-maximum” trials (where all the items are the same) and the “interference-release” trials (where the last four items are different) provide a measure of a participant’s resistance to proactive interference.  Participants were presented with written instructions and could additionally ask for oral instructions from the research assistant. Figure 2.3 depicts an example of the procedural concept of the task. A spacebar press navigated away from the instruction page and began the task. Participants completed a total of 28 trials. The digit stimuli were the numbers 0-9, which occurred with equal frequency and appeared randomly save for not being repeated side-by-side or within the 4-block subsets the participants were to memorize (on the trials in which all the stimuli were digits which required two repetitions; the other trials had no 23    Figure 2.3. Procedural Concept of the Probed Recall Task.   repetitions). The word stimuli were chosen from a pool of neutral, high-frequency, unrelated words composed or one or two syllables from the MRC Psycholinguistic Database: Machine Usable Dictionary (Coltheart, 1981; Wilson, 1988). A total of 500 words were used, and none were repeated during the trials of any given participant. After the presentation of the serial list, participants were given a blank screen on which they were asked to use the keyboard to type the stimuli they were prompted by the probe. A press of the Enter key separated each item they typed in, and the next trial continued (following a brief pause) after the Enter key was pressed following the 4th item being input. Participants could score a total of 4 points on every trial; an item had to be input correctly and in the correct order to receive 24  a point. The average score (out of 4) was calculated for each type of trial, and the average difference between scores on the interference-maximum and interference-release trials provided the main dependent measure for this task. Upon completion of the task, participants were presented with a screen instructing them to alert the research assistant.   Phonemic Verbal Fluency Task (PVFT). The Phonemic Verbal Fluency Task used in the present study is a version of the Controlled Oral Word Association Test (Benton, Hamsher & Sivan, 1994), which is used to measure verbal fluency and has been used in previous sleep studies (Harrison & Horne, 1997). Subjects are given a letter and are asked to come up with as many words as possible beginning with that letter within a 1-minute window. The task comprises three trials, with one letter per trial. The letters used in this version are F, A, and S. Thus, the task comprises three 1-minute trials, one for each of the letters, in which participants are asked to record as many words beginning with that letter as they can think of.  Participants were given oral instructions by the research assistant before the test began. Upon indicating they understood the instructions, the research assistant verbally gave them the letter for that trial and used a timer to measure a period of one minute. After this time had elapsed, the research assistant told the participants to stop what they were doing and prepare for the next trial. This was repeated thee times, save for the last trial for which the end of the one-minute period signified the end of the task. The participants used the keyboard to record the words they generated on every trial, each separated by a press of the Enter key and typed on a new line. This was done on a blank Microsoft® Notepad file save the words “Set 1”, “Set 2”, and “Set 3”, which indicated where on the document the participants were to type the words for each respective trial. 25  The number of words that were generated for each of the letters was recorded as it has been in previous studies. An additional two variables which represent dissociable components of fluency performance were measured: average phonemic cluster size and number of switches between phonemic clusters (Troyer, Moscovitch & Winocur, 1997). Phonemic clusters were defined as groups of successively generated words which were homonyms (e.g. seen, scene), rhymed (e.g. around, abound), began with the same first two letters (e.g. ask, asp), or only differed by a vowel sound (e.g. far, for, fear), and were counted beginning with the second word in each cluster. Average phonemic cluster size was calculated as the average number of phonemically related words minus one, and it is believed to be a measure of non-executive (automatic) processing. Phonemic cluster switches were calculated as the number of transitions between clusters, including single words, errors, and repetitions. Contrary to phonemic cluster size, cluster switches are believed to represent executive processing as it is indicated by mental set shifting and cognitive flexibility. Additionally, the number and type of errors made were also measured and recorded. These included perseverative errors, which occur when the subject repeats the same word, and non-perseverative errors, which include non-words, proper nouns, words being repeated with different endings (have the same root word), and words that begin with the wrong letter.   2.4 Procedure Upon arrival, participants were greeted by a research assistant and provided with written informed consent. Upon signing the consent form, they answered a number of demographic questions including their age, sex, handedness, and whether they had corrected vision. They were then ushered into the testing room and asked to be seated comfortably and have their mobile phones put away and turned off. The research assistant then introduced the 26  sleep questionnaires, which were each located on a separate Google™ Chrome browser tab on the computer’s monitor and were presented using Google™ Forms. The research assistant explained that each of the questionnaires had short written instructions and were to be filled out using each Google™ Forms page and saved upon completion. Participants were asked to be as honest as possible in their responses, and for privacy were left unattended with the proviso that they could reach out to the research assistant in the next room at any time for assistance. They were asked to alert the research assistant when they had finished the questionnaires. The research assistant subsequently introduced the cognitive portion of the study and launched the Java™ Application comprising the SART. Participants were asked to thoroughly read the instructions and again be as honest as possible with the responses. In an effort to facilitate this honesty, the research assistant again left the room while the participants completed this task with the same proviso as for the questionnaire portion of the study. Upon completion of the SART, participants were set up with the remaining tasks in the same fashion. This time the research assistant remained in the room while participants completed the tasks so as to monitor performance and allow easy switching from one task to the other, all done using the Java™ Application with the exception of the PVFT, which used Microsoft® Notepad. Upon completion of the tasks participants were thanked for their participation and orally debriefed, as well as receiving a debriefing form they were encouraged to take with them. The entire study, including the questionnaires, the SART, and cognitive tasks, took each participant between 50 and 60 minutes to complete.   27  Chapter 3: Results  3.1 Participant Exclusion Criteria Only those participants which successfully completed every questionnaire, mind wandering task, and cognitive performance task were included in the analyses. This involved answering every mandatory question on each of the three sleep questionnaires (MEQ, ESS, and PSQI) and completing every trial of the three cognitive performance (MST, PRT, and PVFT) and mind wandering (SART) tasks. These criteria eliminated 83 of the original 358 participants recruited. In addition, participants were also excluded if their accuracy for identifying digit targets on the SART was below 50% on tasks on which they responded that they were on-task (not mind wandering), as this indicated a lack of attention to the task above and beyond that attributable to sleepiness, which would have skewed subsequent results. This criterion excluded an additional five participants. Performance on all other (cognitive performance) tasks was sufficiently adequate to warrant inclusion; as a result, all analyses were ultimately performed on the remaining 270 participants.  3.2 Psychometric Properties and Descriptive Measures   Questionnaires. Questionnaires were scored according to their individual guidelines (outlined in the previous chapter) and each participant received one score per questionnaire, for a total of three questionnaire scores per participant (an MEQ score, an ESS score, and a PSQI score). Table 3.1 summarizes the descriptive statistics (mean, median, standard deviation, range, minimum, maximum, skewness, and kurtosis) for the scores of each questionnaire, as well as Cronbach’s Alpha as an assessment of each questionnaire’s internal consistency within the present context. The values of Cronbach’s alpha reported here are very similar to those reported by Carciofo et al. (2014).  28  Table 3.1  Summary of Descriptive Statistics for the Three Measures of Sleep Tendencies Scale M Mdn SD Range Min/Max Skew/Kurtosis Cronbach’s alpha MEQ 46.10 46 9.49 52 27/79 .52/.15 .82 ESS 8.96 9 3.59 19 0/19 .07/-1.18 .68 PSQI 6.03 6 2.69 17 1/18 .89/1.35 .55         Mind Wandering Task. Propensity to mind wander was assessed by scores on the SART. Specifically, each participant’s mind wandering score was calculated as the proportion of trials during the SART in which they indicated they were mind wandering. Higher proportions indicated elevated levels of mind wandering, and lower proportions indicated a higher tendency to remain focused (on-task). Table 3.2 summarizes the descriptive statistics of the propensity scores, as well as the accuracy scores (proportion correct identifying the target and ignoring the distractor) of participants when they indicated being on task (OT ACC) and when they indicated they were mind wandering (MW ACC).  Table 3.2  Summary of Descriptive Statistics for the Measures Associated with Mind Wandering Measure M Mdn SD Range Min/Max Skew/Kurtosis SART .51 .52 .22 .98 .00/.98 -.05/-.46 MW ACC .83 .87 .14 .96 .00/.96 -3.82/18.90 OT ACC .90 .92 .07 .42 .58/1.00 -1.65/3.36 Note. SART is the label of the mind wandering variable indicating the proportion of trials in which participants mind wandered; it is the single variable representing propensity to mind wander used in all subsequent analyses. 29  Cognitive Performance Tasks. The scores of the cognitive performance tasks were calculated according to the description of their variables outlined in the previous section, and a summary of their descriptive statistics for each task can be found in Table 3.3. Specifically, the variables representing the dissociated executive and non-executive components of cognitive functioning used in the subsequent analyses for each cognitive performance task are as follows: for the PRT, the difference between interference release and interference maximum trials (executive component); for the PVFT, phonemic cluster size (non-executive component) and the number of cluster switches (executive component); and for the MST, the intercept (non-executive component) and slope (executive component) of the linear relationship between reaction time and number of items to be recalled.   3.3 Type I and Type II Error Control To control for the incidence of Type I error, an alpha level of α = .05 was set for all inferential tests. To control for the incidence of Type II error, a priori and post hoc power analyses were conducted. A priori analysis indicated that, for a power of .95, sample sizes of 71, 137, and 863 were necessary to detect a large effect size of .26, a medium effect size of .13, and a small effect size of .02, respectively (Cohen, Cohen, West & Aiken, 2003). Ultimately, the sample size in the present study was 270 participants, and all analyses were conducted in the form of multiple linear regressions with a single dependent variable and three predictor variables. Given these parameters, post hoc power analysis determined that the resulting regression models had a power of 1.0 to detect a large R2 of .26, a power of 1.0 to detect a medium R2 of .13, and a power of .47 to detect a small R2 of .02 (Faul, Erdfelder, Buchner & Lang, 2009). This indicates that the statistical tools used in the present study had a good chance of rejecting the null hypothesis if indeed an effect were present.  30  Table 3.3 Summary of Descriptive Statistics for the Three Measures of Cognitive Performance Measure M Mdn SD Range Min/Max Skew/Kurtosis MST                 Intercept 578.55 564.14 151.96 938.54 257.67/ 1196.21 .96/1.96           Slope 66.42 61.76 38.57 236.08 -35.70/ 200.38 .53/.69      Accuracy                 2-Item Set .95 .96 .07 .58 .42/1.00 -3.75/19.62           4-Item Set .88 .89 .09 .53 .46/1.00 -1.46/3.02           Recent Neg.           Probe  .92 .94 .08 .46 .54/1.00 -1.57/3.20           Non-Recent           Neg. Probe   .94 .96 .09 .69 .31/1.00 -3.46/17.06      Reaction Time                 2-Item Set 711.40 701.65 132.46 983.03 439.22/ 1422.25 1.16/3.30           4-Item Set 844.24 822.82 154.60 1259.90 388.39/ 1648.28 .67/2.34           Recent            Negative             Probe  814.57 804.55 157.72 1306.60 406.40/ 1713.00 .87/3.46           Non-Recent           Negative            Probe   765.06 747.45 46.34 1129.60 459.66/ 1589.26 1.08/3.44      Omissions                 2-Item Set .01 .00 .03 .33 .00/.33 6.45/54.81           4-Item Set .02 .00 .03 .37 .00/.37 5.53/47.19 (table continues) 31  Measure M Mdn SD Range Min/Max Skew/Kurtosis           Recent            Negative             Probe  .02 .00 .04 .46 .00/.46 6.03/58.53           Non-Recent           Negative            Probe   .01 .00 .04 .41 .00/.41 5.53/42.12 PRT                 Total 2.65 2.75 .87 4.00 .00/4.00 -.68/-.01           Interference            Rel.  2.94 3.17 .83 4.00 .00/4.00 -1.02/.867           Interference            Max.  2.35 2.33 1.09 4.00 .00/4.00 -.33/-.83           Rel.-Max.              Difference  .59 .50 .93 5.67 -2.33/3.33 .30/.05 PVFT                 Total 52.16 51.00 13.25 86.00 8.00/94.00 .04/.56           Cluster            Size  .59 .55 .41 3.69 -1.00/2.69 .83/3.05           Cluster            Switches  31.80 32.00 8.31 51.00 5.00/56.00 .04/.47           Perseverative             Errors  .12 .00 .52 6.00 .00/6.00 7.27/68.61           Non-            Perseverative            Errors  .35 .00 .76 6.00 .00/.98 3.81/21.97           Proper Nouns 1.45 1.00 2.00 14.00 .00/14.00 2.40/8.34           Same Root 4.32 2.00 5.97 37.00 .00/37.00 2.67/8.75   32  3.4 Relationship Between Sleep and Mind Wandering  In order to determine whether the findings of Carciofo et al. (2014) could be replicated with our behavioural measure of mind wandering (the SART; versus their questionnaire measures), a multiple linear regression was conducted with mind wandering propensity as the dependent variable, and the three questionnaire measures of sleep (MEQ, ESS, PQSI) as predictor variables. This analysis served two purposes:   1. It determined whether the SART could be used in the place of a questionnaire measure of mind wandering to replicate the observed relationship between various aspects of sleep and mind wandering. 2. It determined to what extent mind wandering could be predicted by sleep, providing insight into the way in which the propensity to mind wander during the day might be explained by sleep tendencies at night and subsequent daytime sleepiness.  A matrix of the correlations between the SART, MEQ, ESS, and PSQI can be found in Table 3.4. The multiple linear regression model predicting propensity to mind wander as calculated by the proportion of mind wandering on the SART from the MEQ, ESS, and PSQI sleep measures was significant, R2 = .036, adjusted R2 = .026, F(3,266) = 3.35, p = .021. Of the predictor variables, only the ESS (β = .17, t266 = 2.81, p < .05) was found to be significant (Table 3.5).                                                                  1 Assumption checks for the form of the relationship, homoscedasticity, independence, and normality of errors, influential cases, and multicollinearity were carried out for all regression analyses.  33  Table 3.4  Correlation Matrix of the Relationships Between Mind Wandering and Sleep Variables Measure 1 2 3 4 1. SART     2. ESS .18*    3. MEQ          -.08         -.16*   4. PSQI           .06 .15* -.30*  *p < .05       Table 3.5 List of Coefficients of the Multiple Linear Regression Model Predicting Mind Wandering from Sleep Measures  Sleep Measure B SE B  β t p ESS .10 .00  .17 2.81 .01 MEQ .00 .00       -.04 -.66 .51 PSQI .00 .01  .03 .42 .68 Notes. Dependent variable is SART score, R2 = .04, adj. R2 = .03, p < .05  These results show that the propensity to mind wander, as measured by the SART, can be predicted by daytime sleepiness, as measured by the ESS. This implies that the sleepier one is, the more likely they are to mind wander. This is in line with the findings of Carciofo et al. (2014). It should be noted, however, that the ESS was only able to account for a relatively small amount of the variance of the SART score. This implies that, while there is 34  a significant relationship between sleepiness and mind wandering, sleepiness only accounts for a fraction of what leads to increased mind wandering propensity. Interestingly, and unlike the findings of Carciofo et al., the MEQ and the PSQI were not significant predictors of mind wandering, suggesting that one’s morningness or eveningness, and one’s sleep quality, do not account for one’s propensity to mind wander as measured by the SART (as opposed to its questionnaire counterpart).   3.5 Relationship Between Sleep and Cognitive Performance   In order to determine whether the findings of Tucker et al. (2009) could be replicated with our questionnaire measures of sleep (the MEQ, ESS, and PSQI; versus their behavioural sleep deprivation measures), further multiple linear regressions were conducted with the three questionnaire measures of sleep (MEQ, ESS, PSQI) as predictor variables, and executive and non-executive measures of cognitive function as dependent variables. Here, results of the regressions analyzing MST slope (representing executive functioning) and MST intercept (representing non-executive functioning) as the dependent variables are presented2. These analyses served similar purposes to the previous model:                                                               2 Additional multiple linear regression analyses were conducted with the following dependent variables: PVF phonemic cluster size (non-executive), PVF number of switches (executive), PRT interference release-interference maximum difference scores (executive). As none of these variables were significantly predicted by the sleep questionnaire predictors in this model and do not add theoretical significance beyond that of the MST findings, they will not be discussed in further detail. However, it should be noted that the lack of significant findings regarding PVF phonemic cluster size and PRT interference release-interference maximum difference scores are what would be expected given the theoretical framework proposed by Tucker et al. (2009), and indeed are similar to the results reported by Tucker et al. assessing the effect of sleep deprivation on the same cognitive variables.  35  1. They determined whether the questionnaire sleep measures taken from the Carciofo et al. (2014) study, albeit representing sleep patterns which differed between-subjects rather than induced disturbances in sleep, could be used in the place of sleep deprivation to replicate the relationship between sleep deprivation and cognitive performance reported by Tucker et al. (2009). 2. They determined to what extent executive and non-executive measures of cognitive performance could be predicted by various aspects of sleep as measured by questionnaires, providing insight into the way in which different aspects of cognitive performance might be explained by sleep tendencies at night and subsequent daytime sleepiness.  A matrix of the correlations between the sleep measures (MEQ, ESS & PSQI), and (independently) the slope and intercept of the MST can be found in Table 3.6. The regression model predicting non-executive cognitive functioning as measured by the intercept of the MST from the MEQ, ESS, and PSQI sleep measures was significant, R2 = .04, adjusted R2 = .03, F(3,266) = 3.80, p = .01. Of the predictor variables, it was again only the ESS (β = -.16, t266 = -2.67, p < .05) that was found to be significant (Table 3.7). Conversely, the regression model predicting executive cognitive functioning as measured by the slope of the MST from the MEQ, ESS, and PSQI sleep measures was not significant, R2 = .01, adjusted R2 = .00, F(3,266) = 1.07, p = .36 (Table 3.8).       36  Table 3.6  Correlation Matrix of the Relationships Between Cognitive Performance and Sleep Variables Measure 1 2 3 4 5 1. MST Intercept      2. MST Slope      3. ESS -.16* .09    4. MEQ        -09       .04 -.16*   5. PSQI        .06  .02  .15* -.30*  *p < .05        Table 3.7 List of Coefficients of the Multiple Linear Regression Model Predicting Non-Executive Cognitive Functioning from Sleep Measures  Sleep Measure B SE B  β t p ESS -6.91 2.59       -.16     -2.67 .00 MEQ 1.50 1.01 .09 1.48 .14 PSQI 6.12 3.57 .11 1.74 .08 Notes. Dependent variable is MST Intercept, R2 = .04, adj. R2 = .03, p < .05      37  Table 3.8 List of Coefficients of the Multiple Linear Regression Model Predicting Executive Cognitive Functioning from Sleep Measures  Sleep Measure B SE B  β t p ESS     1.05 .67 .10     1.58 .12 MEQ .26 .26 .06 .99 .32 PSQI .30 .92 .02 .33 .74 Notes. Dependent variable is MST Slope, R2 = .01, adj. R2 = .00, p = .36  These results show that non-executive components, but not executive components, of cognitive functioning, as measured by the MST, can be predicted by questionnaire measures of daytime sleepiness, as measured by the ESS. According to the observed results, this implies that the sleepier one is, the more impaired their non-executive capacities are on cognitive tasks, while their executive capacities on the same task appear to be spared. These findings are in line with those reported by Tucker et al. (2009), which found that sleep deprivation affected non-executive, but not executive, performance on cognitive tasks. While these findings hint at the ability to use questionnaire measures of sleep, given a large enough sample size, in the place of legitimate behavioural sleep deprivation to detect sleep-related effects in behavioural domains such as cognitive function, it should be noted that the effects found in the present study are relatively small. While this is not unexpected given the likely large degree of difference between a subject’s self-reported daytime sleepiness (which depends on unobserved factors outside of experimental control) and the effects of controlled sleep deprivation, the present findings should be interpreted with caution. 38  3.6 Relationship Between Cognitive Performance, Mind Wandering, and Sleep   In order to determine whether mind wandering and sleep have similar effects on cognitive functioning, two multiple linear regression analyses were conducted to establish whether the dissociated components of cognitive functioning could be predicted by mind wandering in the same pattern that they were predicted by sleep. Like the analyses conducted to determine the relationship between sleep and cognitive functioning, executive function was represented by MST slope and non-executive functioning was represented by MST intercept, each the dependent variable in the subsequent analyses. The predictor variables were mind wandering frequency (as measured by the SART) and, given the results of the previous analyses which found that the MEQ and PSQI were not significant predictors of either mind wandering or cognitive functioning, sleepiness only as measured by the ESS. An interaction term was also included in the form of SART x ESS to determine whether either predictor acted as a moderator for the relationship of the other with regards to cognitive function. These variables were centered about the mean to avoid multicollinearity.   A matrix of the correlations between the centered SART (SART-C) mind wandering scores and the centered ESS (ESS-C) sleep measure scores, and (independently) the slope and intercept of the MST can be found in Table 3.9. The regression model predicting non-executive cognitive functioning as measured by the intercept of the MST from the SART-C, ESS-C, and SART-C x ESS-C interaction term was not significant, R2 = .03, adjusted R2 = .02, F(3,266) = 2.57, p = .06 (Table 3.10). Likewise, the regression model predicting executive cognitive functioning as measured by the slope of the MST from the SART-C, ESS-C, and SART-C x ESS-C interaction term was not significant, R2 = .00, adjusted R2 = .00, F(3,266) = .77, p = .52 (Table 3.11). 39  Table 3.9  Correlation Matrix of the Relationships Between Cognitive Performance, Centered Mind Wandering, and Centered Sleep Variables Measure 1 2 3 4 5 1. MST Intercept      2. MST Slope      3. SART-C       -.07       .03    4. ESS-C       -.16*       .09 .18*   5. SART-C*ESS-C         .01  .00     -.05 .04    *p < .05         Table 3.10 List of Coefficients of the Multiple Linear Regression Model Predicting Non-Executive Cognitive Functioning from Mind Wandering and Sleep Measures  Measure B SE B β t p SART-C   -28.46    43.50      -.04      -.65 .51 ESS-C     -6.58 2.61      -.16    -2.52 .01 SART-C*ESS-C 2.96    11.92 .02 .25 .80 Notes. Dependent variable is MST Intercept, R2 = .03, adj. R2 = .02, p = .06     40  Table 3.11 List of Coefficients of the Multiple Linear Regression Model Predicting Executive Cognitive Functioning from Mind Wandering and Sleep Measures  Sleep Measure B SE B β t p SART-C     3.12   11.15 .02 .28 .78 ESS-C .94 .67 .09     1.41 .16 SART-C*ESS-C .14       3.0 .00 .05 .96 Notes. Dependent variable is MST Slope, R2 = .00, adj. R2 = .00, p = .52   These results show that neither non-executive nor executive components of cognitive function (as measured by the slope and intercept of performance on a cognitive task) can be predicted by propensity to mind wander (as measured by self-report during a cognitive task). Further, propensity to mind wander cannot predict cognitive performance even if sleepiness (which itself is a predictor of a non-executive component of cognitive function) is accounted for. As such, mind wandering propensity is not a predictor of cognitive functioning, and accounting for the possibility of sleep interacting with mind wandering (acting as a moderator, given its own unique relationship with cognitive functioning) does not make a relationship evident.    41  Chapter 4: Discussion  4.1 Overview of Findings The aim of the present study was to determine whether mind wandering and sleep are similar with respect to their influence on cognitive functioning in an effort to ameliorate the prevailing view in the mind wandering literature that it is a mostly costly phenomenon. It was expected that, like sleep, mind wandering would predict performance on non-executive, but not executive, components of cognitive performance tasks. In this endeavor, the present study also evaluated the use of different variables representing sleep and mind wandering than those used in the studies which formed the conceptual foundation upon which this study was conducted. As such, it was also predicted that the pattern of results examining the relationships between sleep and mind wandering and sleep and cognitive performance found in these studies would be replicated.  With regards to the latter prediction, the present study found that the pattern of results previously found between sleep and mind wandering (Carciofo et al., 2014) and between sleep and cognitive performance (Tucker et al., 2009) was replicated even when the variables used to represent these constructs differed. Specifically, when mind wandering was assessed using a behavioural measure (the SART) versus the questionnaire measures used by Carciofo et al. (2014), sleepiness as measured by the ESS was found to be a significant predictor of mind wandering, in line with the findings of Carciofo et al. Similarly, when sleep was assessed using questionnaire measures (the ESS, MEQ & PSQI) versus the behavioural method used by Tucker et al. (2010), sleepiness as measured by the ESS significantly predicted performance on non-executive (intercept) but not executive (slope) components of cognitive performance as measured by the MST, in line with the findings of Tucker et al. 42   While the above results broadly replicate those found by their respective studies, there were some differences to note. With respect to the relationship between sleep and mind wandering, only the ESS was found to be a significant predictor of the scores on the SART, whereas Carciofo et al. (2014) found significant relationships between questionnaire measures of mind wandering and the MEQ and PSQI as well. Interestingly, with respect to the relationship between sleep and cognitive performance, again only the ESS was found to be a significant predictor of non-executive cognitive functioning (as measured by the MST intercept). Not only were none of the other sleep measures significant predictors of cognitive performance, but none of the other cognitive performance measures were predicted by the questionnaire measures of sleep. However, Tucker et al. (2010) did find weaker results on components of the PRT and PVFT even with their behavioural measure of sleep deprivation, so at least in this respect the pattern of results is similar to that found in the present study.  Ultimately, these results demonstrate that both mind wandering and non-executive cognitive functioning are affected by one’s sleepiness. Specifically, increased sleepiness is associated with increased propensity to mind wander and decreased non-executive cognitive capacity. Conversely, propensity to mind wander and both non-executive and executive components of cognitive performance appear not to be influenced by whether one is more prone to morningness or eveningness or by the quality of sleep one gets on a regular basis (assessed within the past month).  It is not clear why the ESS had such comparatively robust effects as a predictor of both mind wandering propensity and non-executive cognitive performance. However, it should first be noted that this apparent robustness is only applicable as it relates to the comparison of the ESS to the other sleep measures used; the overall effect of the ESS was 43  limited, accounting for only a small amount of the variance of any dependant variable it significantly predicted. Still, its solitary impact is interesting. With respect to its relationship with cognitive performance, it could be that the ESS, which assesses daytime sleepiness, is the questionnaire out of the three employed which most closely measures the results of sleep deprivation. Because sleep deprivation was a significant predictor of non-executive function in the Tucker et al. (2010) study, and a night of sleep deprivation is likely to cause considerable daytime sleepiness, it follows that a questionnaire measure of daytime sleepiness would itself be a significant predictor of that measure of cognitive function, albeit on a smaller scale (which may account for the relatively small effect found). Conversely, the quality of sleep one gets or whether one is a morning or evening person (measured by the PSQI and MSQ, respectively) may not matter as much when compared to a night of complete sleep deprivation, and this might explain why neither of these questionnaires significantly predicted cognitive function.  However, this explanation doesn’t hold for the prediction of mind wandering propensity, which would be expected to be reasonably affected by all three sleep measures according to the findings of Carciofo et al. (2014). It is not surprising that daytime sleepiness (ESS) was itself a predictor of mind wandering propensity given that tiredness has been associated with increased mind-wandering episodes (McVay, Kane & Kwapil, 2009; Ottaviani, Shapiro & Couyoumdjian, 2013). It may also not be surprising that morningness and eveningness (MEQ) did not significantly predict propensity to mind wander, as this trait may not matter as long as one gets sufficient sleep to function well during the day. The discrepancy between the present findings and those of Carciofo et al. (2014) in this regard may be ultimately due to the method in which mind wandering was measured. This may also 44  be true with regards to the discrepant findings concerning sleep quality (measured by the PSQI). Given that Carciofo et al. measured mind wandering using a self-report questionnaire, it is possible that participants which had poorer sleep consistently over-estimated their level of mind wandering when asked directly, in the context of questions regarding their sleep. Conversely, it may be that participants are more accurately able to report their tendency to mind wander when they are asked about mind wandering episodes in a context in which they have just had the experience, as is the case when participating in the SART. Ultimately, it may also be that a calculation of mind wandering frequency derived from a task lasting only minutes may not be an accurate representation of a participant’s propensity to mind wander in an entire day, or in general. This possibility is further discussed in a later section. The principal hypothesis of the present study predicting that sleep and mind wandering propensity would have similar effects on dissociated components of cognitive function was not supported. When entered into a multiple linear regression model, neither sleepiness (ESS) nor propensity to mind wander (SART), nor a centered interaction term combining the two variables, significantly predicted non-executive cognitive function (MST intercept) or executive cognitive function (MST slope). While it was expected that executive function would not be significantly predicted, it was expected that non-executive function would be, in line with the results of Tucker et al (2010). Although the non-significant results (p = 0.06) of the present study have been interpreted conservatively, the statistics summarized in Table 3.10 imply that even if we were to ascertain an effect (either by increasing power or by speculating a trend towards significance), this effect would likely have been carried by the ESS. That is, even if the model as a whole had been significant, it would not have reflected the MST intercept being significantly predicted by the SART.  45  These findings do not support the idea that a similarity between the underlying mechanisms or functional processes of sleep and mind wandering can be ascertained, as there was no similarity in the results found between the effect of sleep and mind wandering on cognitive performance. Alternatively, the results suggest that cognitive performance, either executive or non-executive, is in fact not affected by one’s propensity to mind wander. That is, whether someone mind wanders more or less during a period of time in which they are performing a (cognitive performance) task, their overall performance on that task is not affected, suggesting that mind wandering does not impair performance on a universal level.  4.2 Integration Within Current Literature  In addition to producing a pattern of results which does not support the primary hypothesis that sleep and mind wandering affect cognitive performance similarly, the results of the present study appear to contradict a variety of findings showing detrimental effects of mind wandering on cognitive tasks very similar to those employed here. Propensity to mind wander while performing the SART did not predict performance on any aspect of cognitive performance. Given the large literature showing impairments of mind wandering on abilities such as cognitive processing and working memory, it was expected that higher rates of mind wandering would be associated with poorer general performance on the cognitive tasks, either on the non-executive or the executive components, even if the pattern of results still didn’t support the main hypothesis.   With respect to studies which have examined the immediate effects of mind wandering and have shown that cognitive performance is impaired in the specific instances in which the mind wanders (Stawarczyk et al., 2011; Smallwood et al., 2013), as is often the case when mind wandering is studied in a laboratory setting, the present findings cannot 46  directly be compared as they involve the effect of a prolonged period of time assessing the propensity to mind wander rather than the immediate effect of a mind wandering episode. However, studies which have shown mind wandering is detrimental to prolonged activities in daily life, such as driving (Galera et al., 2012; Yanko & Spalek, 2014) and attending lectures (Risko et al., 2012; Szpunar, Moulton & Schacter, 2013) appear contradictory. The large literature regarding the detrimental effects of mind wandering on reading also appears to stand in contrast to the present findings.  Aside from potential confounds concerning methodology and operationalization which may underlie these discrepant findings, one possible explanation for the inconsistency may come from the context regulation hypothesis (Smallwood & Schooler, 2015), which recognizes that the pattern of mind wandering may differ depending on the context of the external environment. Thus, it is possible that the tasks used to measure cognitive performance in the present study provided a context in which attentional control was necessary, suppressing the effects of mind wandering. Conversely, it may be that the SART provided an opposing context which lead to more mind wandering when performed, in contrast to the cognitive performance tasks. Thus, it may be that the propensity to mind wander titrated itself to the current contextual demands of the environment. If this is the case, it may explain the discrepant findings in the literature which produce seemingly contradictory results but in fact represent the effects of different contexts on mind wandering.  4.3 Implications  While the present study found no effect of mind wandering propensity on cognitive performance, this was not true for sleep. The results show that sleep, especially daytime sleepiness, is still likely to affect our cognitive performance. However, this was only true for 47  non-executive cognitive functioning, implying that sleepiness specifically affected the ability to pay attention to the task, ultimately resulting in poorer performance. Given that both types of cognitive functioning have been associated with effects of sleep and mind wandering, it may be necessary to continue differentiating between executive and non-executive cognitive components in future studies examining both constructs in order to avoid potentially confounding results.  Given the innate and seemingly effortless nature of external attention and engagement with the external environment, we are wont to be unaware of just how cognitively taxing it can be. However, it shouldn’t be surprising that cognitive function, even that thought to be “lower-level” such as non-executive function (i.e. attentional orientation), cannot remain consistently engaged throughout the day (especially following, and given the effects of, lack of sleep) and is likely to need the frequent perpetual cognitive breaks that mind wandering appears to provide. The present study implies that, given the right context, mind wandering can occur without cognitive impairment to provide these perceptual cognitive breaks.  4.4 Limitations  Perhaps the most evident limitation of the present study, and the one most likely to be problematic if its underlying assumption doesn’t hold, is the interpretation of the results of the SART. The SART was administered over a 10 to 15-minute period, during which participants were occasionally asked to report whether or not they were mind wandering while they were performing the task. These answers were scored and used to provide a measure of propensity to mind wander which took into account all the reports of mind wandering during that 10 to 15-minute period that participants were engaged in the SART. The assumption was subsequently made that participants’ propensity to mind wander during 48  this task was indicative of their overall propensity to mind wander. That is, the scores on the SART were used as a measure of a participant’s overall tendency to mind wander, with the assumption that this would be their propensity to mind wander during any given 10 to 15-minute span, and specifically during the time in which they completed the cognitive performance (MST, PRT & PVFT) tasks.  While the SART was developed to provide a reliable and valid measure of failures of sustained attention over a brief period (Robertson et al., 1997), it has not as of yet been validated as a measure of general propensity to succumb to sustained attentional failures (mind wander) and may not be an accurate measure of a person’s propensity to do so throughout the day, especially as it is not yet known if the propensity to mind wander remains stable over days, hours, or even minutes. However, given that SART scores have been shown to correlate with people’s self-reports of their tendencies to mind wander throughout the day (Van der Linden et al., 2005; Smilek, Carriere & Cheyne, 2010), it is unlikely that the SART provides a completely skewed representation of overall propensity to mind wander, even if it is not completely accurate.  Another limitation relating to the SART is the preclusion of the ability to make inferences about the differences in target and non-target accuracy when participants indicated they were on task or mind wandering. Alongside mind wandering propensity, these are measures which are often used when assessing SART scores and have been shown to correlate with each other and, importantly, with the self-reported measures of mind wandering (Allan Cheyne et al., 2009; Mooneyham & Schooler, 2013). However, studies using the SART report accuracy and omissions during the final few moments before the participant is asked whether or not they were mind wandering (Smallwood et al., 2008; Kam 49  et al., 2011; Kam, Nagamatsu & Handy, 2014), to ensure that they were categorically in the mind wandering or on-task state. In the present study, because the SART was used as a measure of global propensity to mind wander, these variables were recorded during the entire duration of the block of trials in which the participant indicated they were on-task or mind wandering. That is, after the participant indicated they were on task or mind wandering at the end of the block, every instance of these measures in that block were categorized as resulting from being on task or mind wandering, rather than the few at the end of the block. Thus, it may be possible that, like the SART propensity scores may not be indicative of mind wandering throughout the entire study session, the accuracy scores may not be indicative of entirely mind wandering or on-task episodes through a block of trials. For this reason, SART accuracy scores for mind wandering and on-task blocks were reported for transparency but were not used in the analyses. However, it should be noted that these scores, summarized on Table 3.2, do appear to follow the pattern of results that have been found in previous studies (Mooneyham & Schooler, 2013; Smallwood, 2013).   4.5 Future Directions  Two related avenues of potential future direction, arising from the limitations of the present study, have to do with the way in which mind wandering is measured. Firstly, it was discussed in the section above that an un-validated assumption which had to be made regarding the way in which the SART was utilized was that it was indicative of a participant’s general propensity to mind wander, above and beyond the small window of time in which the SART was administered. Given that this is a novel approach with regards to the conclusions drawn from the results of the SART, if it proves to be a useful framework it will have to be validated in the future, as this is an important part of the study of cognition and 50  attention (Kingstone, Smilek, Ristic, Friesen & Eastwood, 2003; Kingstone, Smilek & Eastwood, 2008). A simple possible approach to this would be to have participants perform the SART at various points throughout the day, providing a measure of their rate of mind wandering that could be plotted and examined continuously over a 12 or 16-hour period. In this way it would also be possible to determine the way that rates of mind wandering fluctuate throughout the day (if they do), also in response to key variables of interest that participants might be tested on or asked to self-report. Advances in psychological methods using mobile smartphones might make this an especially lucrative way to collect data. Given a large enough sample size it might also be possible to map general trends in mind wandering in different homogeneous populations. This could lead to interesting avenues of applied research, especially in elderly and at-risk populations (Einstein & McDaniel, 1997; Jackson & Balota, 2012; Hofmann, Schmeichel & Baddeley, 2012; Marchetti, Koster, Sonuga-Barke & De Raedt, 2012; Nagamatsu, Kam, Liu-Ambrose, Chan & Handy, 2013).  Secondly, the necessarily correlational nature of the present study with regards to mind wandering alludes to the necessity for future research to design methods which can manipulate mind wandering experimentally. Currently, we not only rely on the accuracy of participants’ self-reported states to measure mind wandering (Schooler & Schreiber, 2004) but we can only passively measure the occurrence of spontaneous episodes of mind wandering, if they happen to occur when we happen to be measuring. Not only does this leave us with relatively unreliable methods of measuring mind wandering, but it precludes the ability to determine causation, making us unable to conclude whether mind wandering caused an observed effect on some other variable, or whether that variable led to more or less mind wandering (Smallwood & Schooler, 2015). Thus, developing methods to either train 51  participants to control their own mind wandering, or to cause an increase or decrease in mind wandering directly, would be invaluable to future mind wandering research. With regards to the former, studies have already shown that mindfulness training, a form of meditation, can be used to lower participants’ rates of mind wandering by training their ability to stay on task (Baer, 2003; Jha, Krompinger & Baime, 2007; Zeidan, Johnson, Diamond, David & Goolkasian, 2012; Mrazek et al., 2013). Recently, Axelrod, Rees, Lavidor and Bar (2015) have made promising headway in the latter endeavor by showing for the first time that the propensity to mind wander can be increased externally during the SART by using transcranial direct current stimulation over the dorsolateral prefrontal cortex. These methods provide exciting potential avenues for future mind wandering research.  In conclusion, mind wandering and sleep, as they have been assessed here, do not have similar effects on cognitive performance. In the present study and in previous research, it has been shown that sleepiness affects non-executive, but not executive, aspects of cognitive performance. The present study attempted to extend this finding to mind wandering in the effort of illuminating some of mind wandering’s potential underlying mechanisms and showing it is not a negative phenomenon by broadening the comparisons that can be made between it and sleep, which is a thoroughly studied and necessary phenomenon that has been shown to be similar to mind wandering in its attentional characteristics. Despite the main hypothesis not being supported, the present study did find similarities in the way sleep affected mind wandering and cognitive performance, and found that cognitive performance was not affected negatively by a higher propensity to mind wander. 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