Anticipated, experienced, and remembered subjective effort and discomfort on sustained attention versus working memory tasks

Consciousness and Cognition 75 (2019) 102812

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Anticipated, experienced, and remembered subjective effort and discomfort on sustained attention versus working memory tasks☆

T

Veerpal Bambraha, , Chia-Fen Hsub,c, Maggie E. Toplaka, John D. Eastwooda ⁎

a

Department of Psychology, Faculty of Health, York University, 4700 Keele Street, Toronto, Ontario M3J 1P3, Canada Division of Clinical Psychology, Graduate Institute of Behavioural Sciences, Chang Gung University, No. 259, Wenhua 1st Road, Taoyuan 33302, Taiwan c Department of Child Psychiatry, Chang Gung Memorial Hospital at Linkou, No. 5, Fuxing Street, Taoyuan 33305, Taiwan b

ARTICLE INFO

ABSTRACT

Keywords: Anticipation Subjective experience Cognitive effort Discomfort Sustained attention Working memory

This study examined individuals’ ability to accurately anticipate how cognitively effortful and uncomfortable a task will feel based on a short sample of the task. Participants completed a sustained attention or working memory task. Post-practice, participants rated the effort and discomfort that they anticipated their task would require and engender, respectively. Participants also rated their effort and discomfort during task-administration and the effort and discomfort they recalled feeling after task-administration. Sustained attention task participants anticipated significantly less effort than working memory task participants. Sustained attention task participants felt significantly more effort during the task and remembered feeling more effort than they had anticipated. Working memory task participants felt significantly less effort during the task than they had anticipated. Sustained attention task participants anticipated, experienced, and recalled feeling more discomfort than working memory task participants. Individuals’ anticipation of effort required depends on the task and is different from the effort they actually feel during the task and later recall feeling.

1. Introduction Cognitive processes that require extensive use and control of sustained attention or working memory are often characterized as computationally expensive, costly in decision-making, and aversive (Dreisbach & Fischer, 2012; Kool, McGuire, Rosen, & Botvinick, 2010; McGuire & Botvinick, 2010; Redish, 2015; Shenhav et al., 2017; Stanovich, 2009). As a result, individuals tend to be cognitive misers in their thinking, constantly managing a trade-off between available computational capabilities and the demands of a given situation (Stanovich, 2009), as well as experiencing the impulse to disengage even when such actions may be adaptive (Kurzban, 2016). The purpose of the current investigation was to explore the conscious experience of cognitively demanding tasks, specifically how task type may impact anticipated versus actual and remembered subjective experiences of cognitive effort and discomfort associated with these tasks. Specifically, we examined the anticipated, real-time, and retrospective experiences of cognitive effort and discomfort on sustained attention and working memory tasks. Individuals’ anticipation of the effort required for a cognitive task is

☆ This research was supported in part by grants from the Social Sciences and Humanities Research Council to Dr. Maggie E. Toplak and the Natural Sciences and Engineering Research Council to Dr. John D. Eastwood. ⁎ Corresponding author at: Department of Psychology, Faculty of Health, York University, Behavioural Sciences Building, 4700 Keele Street, Toronto, Ontario M3J 1P3, Canada. E-mail address: [email protected] (V. Bambrah).

https://doi.org/10.1016/j.concog.2019.102812 Received 23 October 2018; Received in revised form 21 August 2019; Accepted 27 August 2019 Available online 12 September 2019 1053-8100/ © 2019 Elsevier Inc. All rights reserved.

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significantly different from the effort that they actually experience during the task and later recall experiencing. Moreover, the differences between anticipated, real-time, and retrospective cognitive effort depends on the type of cognitive task that the individual undergoes, presumably because of the distinct cognitive processes that the task requires. 1.1. Defining cognitive effort Cognitive effort is a broad concept with different operationalizations; thus, what specifically constitutes effort as a construct to be empirically tested is still an open question. The most predominant definition of cognitive effort focuses on objective indices and views effort as being invested or exerted when situated within a demanding task (Ackerman & Thompson, 2017; Navon & Gopher, 1979, Kahneman, 1973; Verguts, Vassena, & Silvetti, 2015; Wickens, 2002). In other words, cognitive effort is a type of capacity or resource in-and-of itself that is limited and it is deployed in response to task demand requirements, such the rate at which mental activity is to be performed, the relative task difficulty, task switching (i.e., completing two tasks simultaneously), etc. (Kahneman, 1973; Paas, Tuovinen, Tabbers, & Van Gerven, 2003; Sergeant, 2000). Recently, Shenhav et al. (2017) proposed a conceptualization of cognitive effort as a type of capacity that can be exerted to mediate between the characteristics of a task, the individual’s available informationprocessing capacity, and the fidelity of the information-processing operations actually performed (reflected in task performance). Taken as a whole, this account suggests that the mobilization of effort is determined by the demands of the task rather than by the performer’s intentions. However, the focus on task demands as the main index of cognitive effort remains inadequate. For example, the increases in effort required to stay on task, to slow the decline in task performance, or to regain optimal task performance after an event (e.g., distractor stimuli) appear to be controlled by the performer’s motivation to maintain performance, as opposed to the demands of the task (Sarter, Gehring, & Kozak, 2006). Indeed, allowing performance to worsen or to even terminate represent plausible outcomes, especially in laboratory settings where the costs for such outcomes have no measurable impact. This is not to say that task performance and task demands are insignificant, as increases in the exertion of effort typically are triggered by the individual’s detection of a declining reward rate or performance errors. Accordingly, an alternative to the above-noted conceptualization of cognitive effort is that effort is a subjective motivational phenomenon (Kool et al., 2010; Kurzban, Duckworth, Kable, & Myers, 2013; Westbrook & Braver, 2015). Importantly, cognitive effort is not simply tied to the cognitive demands of the task. Rather, these contemporary models have centered on motivational accounts of effort, arguing that an individual’s decision to exert cognitive effort is contingent upon more than its limited availability and capacity, such as individual differences in personal willingness or “wanting to” (as opposed to “having to”) exert effort and engage in a task (Hockey, 2011; Hockey, 2013; Kool & Botvinick, 2013). Furthermore, these accounts suggest that exerting cognitive effort is often accompanied by a negative hedonic experience (i.e., aversion; Allport, 2000; Botvinick, 2007; Garbarino & Edell, 1997; Kool et al., 2010; Kurzban et al., 2013). Taken together, tasks are experienced as more effortful and, by extension, aversive, when one has no intrinsic desire to complete them. 1.2. The function of subjective cognitive effort Although the impact of objective task indices (i.e., task demands, task performance, etc.) on individuals’ decision-making and control is well-established, there is an emerging focus on the role of the aversive feeling of effort in individuals’ decisions and behaviours related to exerting effort. For example, the most recent version of Diagnostic and Statistical Manual of Mental Disorders (DSM-5; American Psychiatric Association, 2013) considers a frequent avoidance or dislike of cognitive effort as a diagnostic criterion of Attention-Deficit/Hyperactivity Disorder (ADHD), an area that has received little empirical attention. More broadly, the feeling of cognitive effort in a given moment is a signal that causes in-the-moment goal switching, such that effort is withdrawn and attention is diverted to other goal pursuits that are associated with less negative affect (Garbarino & Edell, 1997; Hockey, 2011, 2013; Kahneman, 2011; Kurzban, 2016; Stanovich, 2009; Stanovich, 2011). Ackerman and Thompson (2017) posit that, much like a thermometer, an individual will meta-cognitively monitor his/her subjective assessment of a cognitive task and control the initiation of, termination of, or allocation of effort to the task on a continuous basis. Dunn, Lutes, and Risko (2016) explicitly compare cognitive effort to a subjective and largely inferential metacognitive evaluation of demand (i.e., perceived effort) that is used to control an individual’s action selection whether the individual is situated within a task or not. Using the demand selection task, where individuals were faced with a repeated free-choice between two alternative courses of action, Dunn et al. (2016) demonstrated that individuals not only disengaged from an action through a retrospective evaluation of felt effort, but that they avoided an action outright through a prospective evaluation of effort (Dunn et al., 2016), suggesting that both effort judgments play a causal role in the control of effort avoidance behaviours. 1.3. Anticipating, experiencing, and remembering effort on cognitively demanding tasks The above-reviewed theoretical and empirical literature suggests that cognitive effort is an aversive subjective experience. Moreover, the effort that an individual experiences in a given moment, remembers experiencing, or anticipates experiencing will impact his/her decision to exert effort in the moment, exert effort in the future, or go forth and exert effort, respectively—which provides further impetus to examine cognitive effort as a subjective phenomenon. Yet, while past research has explored the link between individuals’ retrospective reports of cognitive effort and discomfort and task characteristics (e.g., when the task ends with easier or harder items despite the task’s length; Finn, 2010; Hoogerheide & Paas, 2012; Hsu et al., 2018), precisely how individuals anticipate cognitive effort—specifically the factors that shape anticipated 2

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effort—remains under-investigated. Only one study compared the role of error-likelihood (i.e., how error-prone the task is) versus time demands (i.e., how much time a task takes) on participants’ anticipation of effort (Dunn, Inzlicht, & Risko, 2017). Participants viewed two side-by-side stimuli (i.e., a single word presented at ± 110° and two words presented upright at 0°) and were asked to imagine having to name the word or words presented to them aloud, at which point they chose which of the two different tasks would be more effortful, more time demanding, and more error-prone. Individuals anticipated tasks that are high in perceived errorlikelihood (yet less time consuming; i.e., the single word presented at ± 110°) as more effortful than tasks that are perceived to be more time consuming (yet low in error-likelihood; i.e., the two words presented upright at 0°; Dunn et al., 2017), which suggests that error-likelihood is a pertinent factor in individuals’ anticipation of cognitive effort. Anticipated effort is crucial in decision-making processes, specifically whether or not to take on a task at all (Dunn et al., 2016; Payne, Bettman, & Johnson, 1993). One cannot experience the true effortfulness of a task if he or she makes the choice to avoid the task because of a prospective evaluation of effort. When individuals make decisions about behaviours, they often engage in affective forecasting, the process of predicting one’s emotional state in the future. Yet, affective forecasting research has demonstrated that people are often poor predictors of their future emotional states (for a review, see Kurtz, 2016), such that they mispredict the intensity of their emotional reactions to future events (Wilson & Gilbert, 2003; Wilson & Gilbert, 2008; Wilson, Wheatley, Meyers, Gilbert, & Axsom, 2000). While the above-reviewed study by Dunn et al. (2017) provides some specification of what leads individuals to anticipate tasks to feel effortful, no study to our knowledge has explored individuals’ capacity to accurately anticipate how cognitively effortful and uncomfortable a task will feel based on a short sample of the task. Thus, we sought to extend the previous work by examining anticipated evaluations of cognitive effort and discomfort for different cognitive tasks, and comparing these anticipated evaluations to the effort and discomfort that participants feel during a task and remember feeling after completing a task. No prior investigation has systematically explored subjective cognitive effort before, during, and after participants undergo a cognitively demanding task. 1.4. The current study There are at least three subjective aspects of cognitive effort that need to be differentiated. As outlined by Hsu, Eastwood, and Toplak (2017), individuals can consciously report ‘how difficult the task is’ (i.e., the level of effort potentially demanded by a task), ‘how taxed I felt’ (i.e., the level of effort extracted for a given level of achievement), and ‘how hard I tried’ (i.e., the level of effort an individual exerted at his/her own will). The key distinction between ‘how difficult the task is’ and ‘how taxed I felt’ is that the former emphasizes the characteristics of the external task (i.e., the relative difficulty of the task to others), while the latter emphasizes the impact of the task on the individual (i.e., how much a task required of the individual). The key distinction between ‘how hard I tried’ and ‘how taxed I felt’ is that the former emphasizes an individual’s motivation to perform his/her best. Indeed, these conceptual differences are also observed empirically. Otto, Zijlstra, and Goebel (2014) found that, compared to ratings of task difficulty (how difficult the task is), ratings of mental effort (how taxed I felt) during a working memory task were related to increased activation in the left anterior insular cortex, an area that plays a prominent role in emotional self-awareness. Mulert et al. (2007) found that the amount of “active volitional mental effort” (how hard I tried) remained constant across increases in task difficulty, whereas “passive mental effort demands” (i.e., the effort required from an individual, how taxed I felt) increased as task difficulty increased. In the current study, we specifically explored ‘how taxed I felt’ among participants who took part in one of two randomly assigned cognitive tasks, the Paced Auditory Serial Addition Test (PASAT; Gronwall, 1977) or the Sustained Attention to Response Task (Robertson, Manly, Andrade, Baddeley, & Yiend, 1997). Given that the objective was to examine the extent to which participants accurately anticipate cognitive effort and discomfort (i.e., how anticipated effort and discomfort differ from real-time experiences and retrospective reports) when engaged in distinct cognitive processes, we chose two tasks to activate different underlying mental processes (i.e., working memory versus sustained attention). After completing brief practice blocks of their assigned task, participants provided ratings of the level of cognitive effort that they anticipated the task would require. Given that effort is often aversive, we also asked participants to anticipate the amount of discomfort that they expected the task would engender. Anticipation in this study was based on a combination of one’s heuristics of the task and experience with a short practice. After each block of their assigned task, participants provided ratings of their current level of cognitive effort and discomfort. Finally, we asked participants to provide retrospective ratings of the cognitive effort and discomfort they remembered feeling during the task. Past work suggests that performance on “vigilant attention” tasks (Robertson & O’Connell, 2010), which require monitoring visual displays or auditory streams for infrequent signals, reliably decreases over time with concomitant increases in perceived mental effort (Mackworth, 1948; Scerbo, 2001; Smit, Eling, & Coenen, 2004a; 2004b; Warm, Matthews, & Finomore, 2008; Warm, Parasuraman, & Matthews, 2008). In contrast, working memory tasks feel easier and more automatized with continuous practice (Jolles, Grol, Buchem, Rombouts, & Crone, 2010; Klingberg, 2010; Olesen, Westerberg, & Klingberg, 2003; Shipstead, Redick, & Engle, 2012). Thus, we predicted that individuals’ anticipation of cognitive effort and discomfort would differ between the PASAT and SART, such that SART participants would anticipate less cognitive effort and discomfort than PASAT participants. Further, we expected that differences between anticipated and real-time ratings of effort (and of discomfort), and differences between anticipated and retrospective ratings of effort (and of discomfort) would be moderated by the cognitive task that individuals completed: SART participants would actually feel and remember feeling more cognitive effort and discomfort than they anticipated feeling and PASAT participants would actually feel and remember feeling less cognitive effort and discomfort than they anticipated feeling.

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2. Material and methods 2.1. Ethics statement The research ethics board at York University approved the current study. All participants provided written informed consent before any data was collected and were informed of their right to withdraw consent at any time, with no consequences to receiving academic credit. 2.2. Participants A final sample of 551 university participants (Mage = 20.50, SDage = 4.55, 359 females) was included in the current investigation. All participants took part in one of four consecutive studies for academic credit: Students were recruited for three consecutive studies using the PASAT (n = 401) and one study using the SART (n = 150). Data from a total of 39 participants were excluded from the statistical analyses. Among participants who completed the PASAT, 27 were excluded: Five had incomplete or missing data; 10 failed the practice trials of the task; and 12 were deemed outliers due to extreme performance on the task (i.e., accuracy at any block was more than three standard deviations away from the mean of the blocks with the same difficulty). Data from participants who completed the PASAT are also reported in other studies (Hsu et al., 2017; Hsu et al., 2018). Among participants who completed the SART, 12 were excluded: Two failed to complete the task; and 10 were deemed outliers (i.e., they made Go-errors on more than 10 percent of trials). 2.3. Measures 2.3.1. Paced auditory serial addition test (PASAT) An adapted version of the Paced Auditory Serial Addition Test (Gronwall, 1977) is the working memory task that was used to elicit cognitive effort in participants. In this task, each digit was presented on the screen for 1000 ms; with each digit, participants simultaneously heard an auditory beep. The delay between the offset of one digit and the onset of the following digit was 4000 ms for all trials.1 All participants were instructed to sum the last two digits that they had seen and to enter this response using the keyboard; responses could be entered as soon as the latest digit appeared and during the 4000 ms delay (i.e., within a response window of 5000 ms). Digits were presented on the centre of the screen in black font against a white background (Fig. 1). Participants were randomly assigned to complete between five to nine “blocks” of the PASAT. Each block of the PASAT required participants to provide a total of 15 sums (i.e., 15 trials with a total of 16 digits presented) and had one of three difficulty levels: hard (i.e., adding one single and one double digit), medium (i.e., adding two single digits that summed to more than nine), or easy (i.e., adding two single digits that summed to nine or less). Every participant received one hard block, one easy block, and three to seven medium difficulty blocks depending on the total number of blocks that the participant was randomly assigned to.2 The blocks were presented in a pseudo-randomized sequence: The first and last blocks were always medium difficulty and the intervening blocks were composed of one easy, one hard, and one to five medium blocks (Fig. 2). Upon receiving the standard task instructions of the PASAT (Gronwall, 1977), all participants completed two practice blocks to ensure that they understood the task and to familiarize themselves with the task demands. Both practice blocks were of medium difficulty for all participants. Upon completing the practice blocks, participants were asked to anticipate how much cognitive effort the task would require (“In light of your experience so far, how much mental effort do you expect will be required to complete this task?”) and how much discomfort3 the task would engender (“In light of your experience so far, how much discomfort or distress do you expect to experience during this task?”). After each block during the task (i.e., after every 15 trials), participants were prompted to answer two questions about their current subjective experience of cognitive effort (“Rate your current level of mental effort”) and discomfort (“Rate your current level of discomfort or distress”); these scores are referred to as ‘real-time’ cognitive effort and discomfort ratings. Upon completion of the task, participants completed a demographic and self-report questionnaire, which were approximately six minutes in duration, before providing ‘retrospective’ evaluations of cognitive effort (“On this working memory task, what was your total amount of mental effort?”) and discomfort (“On this working memory task, what was your total amount of discomfort and distress?”). All ‘anticipated’, ‘real-time’, and ‘retrospective’ ratings were provided on a scale ranging from 1 (None) to 7 (A Lot).

1 The inter-stimulus timing of the PASAT remained 4000 ms (i.e., it did not vary within the task or between participants). This inter-stimulus timing was chosen in order to ensure that the time that had elapsed between real-time ratings of cognitive effort and discomfort was consistent between the PASAT and SART participants (particularly those SART participants who provided real-time ratings every 75 trials). See Section 2.3.2 for more details. 2 One easy and one hard block were included in the PASAT in order to vary task difficulty. This methodological design allowed us to examine the specific aspects of real-time effort and discomfort that predict participants’ retrospective effort and discomfort, and their willingness to complete the PASAT again. This research question and data are reported in Hsu et al., 2018. 3 PASAT participants who were recruited in the first semester of data collection did not provide anticipated ratings of discomfort (n = 149).

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7 1000 ms

No r e s pons e e nt e r e d 4000 ms

2 1000 ms

Cor r e c t r e s pons e :

9

4000 ms

1 1000 ms

Cor r e c t r e s pons e :

3

4000 ms

And s o on f or 1 3 t r i al s

Fig. 1. Visual representation of the PASAT stimulus presentation parameters. Note 1. This is an example of a single block procedure of the PASAT with 15 trials in each block. Note 2. Participants were asked to sum the last two digits they had seen and to enter this response using the keyboard. Responses could be entered as soon as the latest digit appeared and during the 4000 ms delay (i.e., within a response window of 5000 ms).

2.3.2. Sustained attention to response task (SART) The Sustained Attention to Response Task (Robertson et al., 1997) was also used to elicit cognitive effort in participants. The SART is used to measure sustained attention while minimizing other aspects of cognitive load. The task presented a number of single digits (i.e., 1 to 9) in a quasi-random sequence and participants were required to make a button press to the majority of trials except when the target digit “3” appeared, when they were to withhold a response. Each trial was presented for 250 ms, followed by a 900millisecond mask presented on the screen; thus, the period from digit onset to digit onset was 1150 ms. The mask following each digit consisted of a ring with a diagonal cross in the middle and the total diameter of the circular mask was 29 mm. Both digit and mask were presented on the centre of the screen in white font against a black background (Fig. 3). The digits were presented in one of five randomly allocated font sizes to enhance the demands for processing the numerical value, rather than simply setting a search template for some peripheral feature of the no-response target. The font sizes were 48, 72, 94, 100, and 120 point, respectively, corresponding to a height varying between 12 mm and 20 mm. We manipulated the length of the SART in order to approximate the duration of the PASAT. Thus, participants were randomly assigned to one of the three conditions differing in total trial numbers: 450, 675, or 900 trials. As with participants who completed the PASAT, participants assigned to the SART received the standard instructions of the task (Robertson et al., 1997) and then completed two practice blocks to familiarize themselves with the task requirements. Each SART participant was randomly assigned to one of two practice block conditions.4 One practice condition of the SART mirrored the duration (in seconds) of the practice blocks of PASAT: Here, the first practice block had 29 trials and the second practice block had 72 trials (and for each trial, there was a 1/9 chance of receiving the target ‘3’, with the exception that the target was not presented twice in a row). The other practice condition of the SART mirrored the trial numbers of the practice blocks of PASAT: Here, the first practice block had five trials (with one target trial where participants were to withhold responding) and the second practice block had 15 trials (with two target trials where participants were to withhold responding). 4 Independent t-tests revealed that anticipated cognitive effort and discomfort did not significantly differ between the SART participants who completed the practice that mirrored the duration of the PASAT practice (n = 75) and the SART participants who completed the practice that mirrored the trial numbers of the PASAT practice (n = 75; both p’s > 0.05).

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5-block Condition Practice Block 1

Practice Block 2

Medium Block

3 blocks in random order: 1 easy, 1 medium, 1 hard

Medium Block

5 Trials

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6-block Condition Practice Block 1

Practice Block 2

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4 blocks in random order: 1 easy, 2 medium, 1 hard

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4 x 15 Trials

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7-block Condition Practice Block 1

Practice Block 2

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5 blocks in random order: 1 easy, 3 medium, 1 hard

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15 Trials

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8-block Condition Practice Block 1

Practice Block 2

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6 blocks in random order: 1 easy, 4 medium, 1 hard

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9-block Condition Practice Block 1

Practice Block 2

Medium Block

7 blocks in random order: 1 easy, 5 medium, 1 hard

Medium Block

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7 x 15 Trials

15 Trials

Fig. 2. PASAT conditions (random assignment to five, six, seven, eight, or nine blocks). Note. The PASAT comprised five conditions, ranging from five to nine blocks, which were randomized between participants. Each block had 15 trials, and was one of three levels of difficulty: hard, medium, or easy. The task blocks were presented in pseudo-randomized sequence, as the first and last blocks were of medium difficulty and the middle blocks were composed of one easy, one hard, and one to five medium blocks.

With the same rating scales and instructions as the PASAT, SART participants provided anticipated ratings of cognitive effort and discomfort after completing practice blocks of the SART, as well as real-time cognitive effort and discomfort ratings after each block of trials. We manipulated two levels of frequency to collect real-time ratings of cognitive effort and discomfort. In particular, some participants (n = 78) provided real-time ratings every 225 trials (the task length ranged from two to four blocks) in order to match the original methodology of the SART. Other participants (n = 72) provided real-time ratings every 75 trials (the task length was six, nine, or 12 blocks) in order to match the frequency of the real-time ratings sampled from participants who completed the PASAT (Fig. 4) and to approximate the time-based frequency of real-time responses (i.e., 86.25 s versus 80 s) between task participants. After completing a demographic and self-report questionnaire (approximately six minutes in length), participants gave retrospective evaluations of cognitive effort and discomfort. We used independent t-tests to examine differences between the SART participants who completed 225 trials in each block and the SART participants who completed 75 trials in each block. The two groups did not differ in their real-time ratings and retrospective ratings of cognitive effort and discomfort (all four p’s > 0.05). For each version of the SART, we also investigated the linear trend of real-time effort and discomfort over time by calculating the linear trend of each participant (calculated based on the particular number of blocks that participant had completed) and then averaging these slopes to create a group mean. The two groups of SART participants did not differ in their slopes of real-time cognitive effort and real-time discomfort (both p’s > 0.05). In terms of performance, the two groups of SART participants did not differ in errors of omission (Go errors) and errors of commission (No-go errors) (both p’s > 0.05). As a result, within all subsequent analyses that compared the PASAT and the SART, all participants who underwent the SART were examined together, not separately by the number of trials in each block of the task. 2.4. Procedure All materials and stimuli were presented to participants individually in a lab room and were recorded using a desktop computer within a single session. The total duration of the experiment session ranged from 40 to 60 min and all participants received academic

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7 250 ms (Button press)

X 900 ms mask

4 250 ms (Button press )

X 900 ms mask

3 250 ms (Withhold res ponse)

X 900 ms mask

And so on for 75 trials

Fig. 3. Visual representation of the SART stimulus presentation parameters. Note 1. This is an example of a single block procedure of the SART with 75 trials in each block. Note 2. Participants were asked to make a button press after every digit except when the target digit “3” appeared. Note 3. On each trial, the digits were presented in one of five different randomly determined font sizes (48, 72, 94, 100, and 120 point) to enhance the processing demands.

credit upon completion of the session. Data for the PASAT experiment was collected in three consecutive studies5 over different experimental presentation software. In the first two studies, the PASAT was programmed with E-prime (Schneider, Eschman, & Zuccolotto, 2012) and the demographic questions and retrospective evaluations of cognitive effort and discomfort were presented separately on Qualtrics survey software (Qualtrics, Provo, UT). In the third study, jsPsych (de Leeuw, 2015) was used to program the PASAT (jsPASAT), demographic questions, and retrospective ratings so that all of the PASAT data could be collected in a single program.6 In the SART study, the entire study was programmed with jsPsych. Across both tasks, the first practice block provided feedback to the participant and the second block simulated the actual task and did not provide feedback. 2.5. Statistical analyses One-sample t-tests assessed the linear trends of real-time cognitive effort and real-time discomfort for the two tasks separately, and independent t-tests analyzed differences between the PASAT and SART in slopes of effort and discomfort. Then, a series of twoway mixed repeated measures ANOVAs examined differences in anticipated versus real-time and retrospective cognitive effort and discomfort on the PASAT and SART. More specifically, these analyses assessed if changes in cognitive effort and discomfort ratings over the course of the entire experiment (i.e., the differences between anticipated and real-time ratings, and between anticipated and retrospective ratings) were moderated by the task completed (i.e., PASAT or SART). In this analysis, we computed the average realtime effort and average real-time discomfort scores from all real-time ratings that participants provided for each task. Also, task duration and task performance were statistically controlled. Boxplots and histograms revealed that there were no remaining outliers and that the distributions of the data did not significantly deviate from normality. The assumption for homogeneity of variances was violated only for the anticipated effort and average realtime discomfort variables, and the assumption for homogeneity of co-variances was not met. Given that the assumption for homogeneity of variance was unmet for only two variables within different models and given our a-prior hypotheses, specifically that differences in cognitive effort and discomfort ratings would be moderated by task, mixed ANOVAs were conducted in order to allow us to interpret any significant interaction terms (Field, 2013). 5 One-way between-subjects ANOVAs revealed that anticipated, real-time, and retrospective cognitive effort and discomfort did not significantly differ between the three data collection cohorts (all p’s > 0.05). 6 Independent t-tests revealed that anticipated, real-time, and retrospective cognitive effort and discomfort did not differ between participants who completed the PASAT programmed with E-prime (n = 254) and participants who completed the jsPASAT (n = 147; all p’s > 0.05). Real-time discomfort was significantly higher among participants who completed the PASAT with E-prime (p = .028).

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Fig. 4. SART conditions (random assignment to two, three, four, six, nine, or 12-blocks). Note 1. Block length of the SART was randomized between participants. Participants were first randomly assigned to complete real-time effort and discomfort ratings every 225 trials or every 75 trials. Participants assigned to the former condition were randomly assigned to complete two, three, or four blocks of the SART (with 225 trials/block). Participants assigned to the latter condition were randomly assigned to complete six, nine, or 12 blocks of the SART (with 75 trials/block). Note 2. The practice blocks of the SART had two conditions, which were randomized between participants. One condition mirrored the duration of the practice blocks of the PASAT (in seconds) and the other condition mirrored the trial numbers of the practice blocks of the PASAT. For the condition that mirrored the PASAT in duration, the first practice block had 29 trials and the second practice block had 72 trials. For the condition that mirrored the PASAT in trial numbers, the first practice block had five trials and the second practice block had 15 trials.

3. Results Table 1 presents descriptive statistics for the variables under investigation for the overall sample and for PASAT and SART participants. 3.1. Linear trend of real-time cognitive effort and discomfort We explored the linear trend of real-time cognitive effort and real-time discomfort by computing the linear trend for each participant on the PASAT and SART and subsequently averaging these slopes to create a group mean. Separate one-sample t-tests (two for each task) found that the changes in cognitive effort and discomfort during the task varied between PASAT and SART participants. Participants who completed the PASAT found it less effortful (Mslope = −0.03; differed significantly from zero, t(400) = −2.92, p = .004, d = −0.13) and more uncomfortable (Mslope = 0.04; differed significantly from zero, t(400) = 3.11, p = .002, d = 0.15) as the task progressed. In contrast, participants who completed the SART found it more effortful (Mslope = 0.04; t(149) = 2.69, p = .008, d = 0.21) and more uncomfortable (Mslope = 0.10; t(149) = 6.19, p < .001, d = 0.50) as the task progressed. Independent samples ttests confirmed that the linear trends of real-time effort, t(324.24) = 3.90, p < .001, d = 0.32, and of real-time discomfort, t 8

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Table 1 Mean (SD) ratings and trends of subjective experience on the PASAT and SART. Overall sample (n = 551) M (SD)

PASAT (n = 401) M (SD)

SART (n = 150) M (SD)

Anticipated ratings: Cognitive Effort Discomforta

5.00 (1.45) 4.07 (1.63)

5.26 (1.24) 3.88 (1.58)

4.31 (1.72) 4.39 (1.68)

Average real-time ratings: Cognitive Effort Discomfort

4.86 (1.33) 3.95 (1.61)

4.68 (1.27) 3.85 (1.56)

5.36 (1.38) 4.22 (1.70)

Retrospective ratings: Cognitive Effort Discomfort

5.13 (1.37) 4.24 (1.72)

5.09 (1.37) 4.17 (1.70)

5.22 (1.39) 4.41 (1.76)

Linear trend in Effort Linear trend in Discomfort

−0.01 (0.23) 0.06 (0.25)

−0.03 (0.24) 0.04 (0.26)

0.04 (0.19) 0.10 (0.20)

Task Duration (seconds) Task Performance (accuracy)

612.23 (178.44) 0.79 (0.15)

554.41 (111.86) 0.76 (0.15)

766.80 (225.59) 0.89 (0.09)

Note. a The average rating for this variable was based on a total of 402 participants (252 PASAT participants and 150 SART participants), as PASAT participants recruited in the first semester of data collection (n = 149) did not provide anticipated ratings of discomfort.

(549) = 2.68, p = .008, d = 0.26, were significantly greater among SART participants than PASAT participants. Thus, although both the PASAT and SART became more uncomfortable for participants over the course of the tasks, the SART became more cognitively effortful but the PASAT became less cognitively effortful. 3.2. Experiences of cognitive effort and discomfort over time: Moderating role of task Four two-way mixed ANOVAs were conducted to examine if differences between participants’ anticipated and average real-time cognitive effort (and discomfort), and between participants’ anticipated and retrospective cognitive effort (and discomfort) were moderated by the task completed (i.e., PASAT or SART). Significant differences between anticipated and average real-time ratings of effort and discomfort, and between anticipated and retrospective ratings, would suggest that participants do not accurately predict their experiences of effort and discomfort. Significant interactions between rating-time and task would suggest that differences between anticipated and real-time effort and discomfort, as well as between anticipated and retrospective effort and discomfort, depend on the task participants are assigned to. Prior to the ANOVAs, t-tests and Pearson’s correlation analyses were used to examine task duration (in seconds) and task performance (i.e., accuracy) as potential covariates (Table 2). For both tasks, accuracy was operationalized as the proportion of trials completed correctly (i.e., PASAT – providing the correct sum; SART – not making an omission error or a commission error). The duration of the SART was significantly greater than the duration of the PASAT, t(177.11) = 11.03, p < .001. Accuracy was significantly higher among SART participants than PASAT participants, t(451.00) = 13.10, p < .001. Among the whole sample, task duration was positively associated with average real-time discomfort, and task performance was inversely associated with average real-time cognitive effort, average real-time discomfort, retrospective cognitive effort, and retrospective discomfort. Thus, all ANOVAs included task duration and task performance as statistical covariates. 3.2.1. Cognitive effort While statistically controlling for task duration and task performance, there was a significant interaction between rating-time and task (SART versus PASAT) on differences between anticipated and average real-time ratings of cognitive effort, F(1, 547) = 114.52, p < .001, partial ηp2 = 0.17 (Fig. 5). Tests of simple main effects for task (a Bonferonni-corrected p level of < 0.025, 0.05/2 comparisons, was used to maintain family-wise alpha) revealed that PASAT participants had significantly higher anticipated ratings of cognitive effort than SART participants, F(1, 547) = 27.84, p < .001, partial ηp2 = 0.05 (this was also supported by an independent samples t-test that accounted for the unequal variances between task groups, t(209.71) = 6.22, p < .001). In contrast, SART participants had significantly higher average real-time ratings of cognitive effort than PASAT participants, F(1, 547) = 43.67, Table 2 Correlations of task duration and task performance with ratings of cognitive effort and discomfort.

Average real-time cognitive effort Average real-time discomfort Retrospective cognitive effort Retrospective discomfort

Task duration (Seconds)

Task performance (Accuracy)

0.08 0.09* 0.01 0.08

−0.09* −0.20*** −0.19*** −0.21***

*p < .05. **p < .01. ***p < .001. 9

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Cognitive Effort

5.5 5 4.5 4 3.5 3

ANTICIPATED

REAL-TIME

PASAT

SART

Fig. 5. Comparisons between anticipated and average real-time cognitive effort by task, with standard error bars.

p < .001, partial ηp2 = 0.07. Tests of simple main effects for rating-time (a Bonferonni-corrected p level of < 0.025, 0.05/2 comparisons, was used to maintain family-wise alpha) found that, among PASAT participants, anticipated effort was significantly higher than average real-time effort, F(1, 549) = 61.92, p < .001, partial ηp2 = 0.10. In contrast, among SART participants, anticipated effort was significantly lower than average real-time effort. F(1, 549) = 74.81, p < .001, partial ηp2 = 0.12. A significant main effect of rating-time was found, F(1, 547) = 13.29, p < .001, partial ηp2 = 0.02, between participants’ anticipated and average real-time cognitive effort ratings. There was no main effect of task, F(1, 547) = 0.23, p = .634, partial ηp2 = 0.00. Regarding differences between anticipated and retrospective cognitive effort, there was a significant interaction between ratingtime and task while statistically controlling for task duration and task performance, F(1, 547) = 55.47, p < .001, partial ηp2 = 0.09 (Fig. 6). As with prior simple main effects tests of task (a Bonferonni-corrected p level of < 0.025 was used), PASAT participants had significantly higher anticipated ratings of cognitive effort than SART participants, F(1, 547) = 27.84, p < .001, partial ηp2 = 0.05. Similar to average real-time ratings, SART participants had significantly higher retrospective ratings of cognitive effort than PASAT participants, F(1, 547) = 7.67, p = .006, partial ηp2 = 0.01. Tests of simple main effects of rating-time (a Bonferonni-corrected p level of < 0.025 was used) revealed that, among PASAT participants, there were marginal differences between anticipated and retrospective effort, F(1, 549) = 5.08, p = .025, partial ηp2 = 0.01. Among SART participants, anticipated effort was significantly lower than retrospective effort, F(1, 549) = 55.12, p < .001, partial ηp2 = 0.09. A significant main effect for rating-time, F(1, 547) = 16.91, p < .001, partial ηp2 = 0.03, but a non-significant main effect for task, F(1, 547) = 2.49, p = .115, partial ηp2 = 0.01, were found. 3.2.2. Discomfort Consistent with ANOVA analyses for cognitive effort, task duration and task performance were entered into each model for discomfort as statistical covariates. There was a non-significant main effect of rating-time, F(1, 397) = 0.69, p = .408, partial ηp2 = 0.00, and a non-significant interaction between rating-time and task, F(1, 397) = 0.02, p = .897, partial ηp2 = 0.00, on differences between anticipated and average real-time ratings of discomfort (Fig. 7). The significant main effect for task found that SART participants anticipated feeling and actually felt more discomfort than PASAT participants, F(1, 397) = 21.33, p < .001, partial ηp2 = 0.05. Similar patterns emerged when examining differences between anticipated and retrospective discomfort. There was a no main effect of rating-time, F(1, 398) = 1.09, p = .297, partial ηp2 = 0.00, and a non-significant interaction between rating-time and task, F (1, 398) = 1.06, p = .304, partial ηp2 = 0.00, on differences between anticipated to retrospective ratings of discomfort (Fig. 8). There was a significant main effect of task, such that SART participants anticipated feeling and remembered feeling more discomfort than 6

Cognitive Effort

5.5 5 4.5 4 3.5 3

ANTICIPATED

RETROSPECTIVE

PASAT

SART

Fig. 6. Comparison between anticipated and retrospective cognitive effort by task, with standard error bars. 10

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V. Bambrah, et al. 6 5.5

Discomfort

5 4.5 4 3.5 3

ANTICIPATED

REAL-TIME

PASAT

SART

Fig. 7. Comparisons between anticipated and average real-time discomfort by task, with standard error bars. 6 5.5

Discomfort

5 4.5

4 3.5 3

ANTICIPATED

RETROSPECTIVE

PASAT

SART

Fig. 8. Comparisons between anticipated and retrospective discomfort by task, with standard error bars.

PASAT participants, F(1, 398) = 15.91, p < .001, partial ηp2 = 0.04. In these analyses, anticipated cognitive effort was higher among PASAT participants than SART participants, but average realtime cognitive effort and remembered cognitive effort were lower among PASAT participants than SART participants. The SART felt consistently more uncomfortable than the PASAT, across anticipated, real-time, and retrospective ratings. 4. Discussion 4.1. Summary of findings Our analyses of the slopes of real-time effort and discomfort revealed that the feeling of effort decreased and the feeling of discomfort increased over the course of the PASAT. In contrast, both effort and discomfort increased over the course of the SART. The mixed ANOVAs suggest that PASAT participants anticipated significantly more cognitive effort than SART participants. In contrast, SART participants felt significantly more effort during the task and recalled feeling significantly more cognitive effort than PASAT participants. Similar to the patterns observed with the slope analyses, the mixed ANOVAs found that cognitive effort significantly increased from anticipated ratings to average real-time ratings (and to retrospective ratings) for SART participants. However, cognitive effort significantly decreased from anticipated ratings to real-time ratings and the decrease in effort from anticipated to retrospective evaluations was marginally significant for PASAT participants. As with the slope analyses, the experience of discomfort differed from that of effort: Our mixed ANOVAs suggest that SART participants not only anticipated, but also felt and later recalled feeling significantly more discomfort than PASAT participants. Overall, the findings of this study provide a converging set of analyses that demonstrate how anticipated, experienced (real-time), and remembered (retrospective) subjective effort and discomfort differ from each other depending on a given task, specifically on a sustained attention versus working memory task. These differences are not attributable to task duration or task performance. These findings have important implications for understanding how subjective experiences of effort and discomfort systematically vary on cognitive performance-based measures, highlighting individuals’ limited capacity to accurately anticipate these experiences. 4.2. Real-time cognitive effort and discomfort are distinct subjective experiences The varying trajectories of real-time cognitive effort and discomfort between the PASAT and SART found with the slope analyses 11

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underscore prior work in our lab that has examined the relationship between cognitive effort and discomfort. For example, one study by Hsu et al. (2018) found that changes in real-time ratings of cognitive effort required on the PASAT were positively and significantly correlated with changes in real-time ratings of discomfort at 0.46. Although this suggests important overlap between these constructs, a considerable amount of variance between these two constructs remains unexplained (Hsu et al., 2018). There is room for some dissociation, such that some individuals who experience high cognitive effort do not necessarily experience discomfort. Moreover, the same study found that real time ratings of discomfort predicted participants’ willingness to repeat the PASAT, but realtime ratings of effort did not (Hsu et al., 2018). Accordingly, these findings suggest that the experience of cognitive effort and discomfort are not the same, but may frequently co-occur. In the current study, as the PASAT progressed, it felt less effortful but more uncomfortable for participants; yet, as the SART progressed, it felt more effortful and more uncomfortable for participants. These results specifically point to the moderating role of the type of task in changes in cognitive effort and discomfort over the course of the task. If subjective effort and discomfort are indeed distinct and if these differences vary depending on the cognitive task at hand, then the results of the current study and the above-reviewed findings may qualify existing theories and empirical work that posit that effort and discomfort are equivalent experiences—that the subjective experience of effort is unpleasant and aversive (Allport, 2000; Botvinick, 2007; Kahneman, 2011; Kurzban et al., 2013; Stanovich, 2009; Stanovich, 2011). Thus, future work should further validate the possible conceptual and psychometric distinctions between the experience of cognitive effort and discomfort; doing so might, in turn, facilitate the ability to precisely capture any moderating or causal mechanisms that give rise to these potentially diverging experiences. Futhermore, given that there are three proposed aspects of subjective cognitive effort (Hsu et al., 2017), future work should explore if and how the trajectory of ‘how difficult the task is’ or ‘how hard I tried’ during a task (which we did not examine in the current study) are distinct from the experience of discomfort. Doing so provides the opportunity to further validate the utility of these distinct aspects of cognitive effort and to enhance the precision with which they are measured. 4.3. Why are individuals inaccurate predictors of cognitive effort? The ANOVAs revealed that the experience of cognitive effort differed in the two groups of task participants. Cognitive effort increased for SART participants and decreased for PASAT participants. Yet, SART participants anticipated, experienced, and later recalled feeling more discomfort than PASAT participants. In other words, although the trajectory of effort differed within the two groups of task participants, the trajectory of discomfort did not. Taken together, these results suggest that participants were better at predicting how uncomfortable their task would feel, as opposed to the level of effort that would be required for the task. Moreover, it is possible that, while participants for each task were able to accurately predict the discomfort that they would feel, anticipated discomfort does not index the level of cognitive effort that will be required for the task—further substantiating the distinct nature of effort and discomfort and the importance of exploring the conditions and circumstances under which these two experiences overlap and separate (Hsu et al., 2018). Compared to anticipation in the affective forecasting literature, the state of anticipation has been less central in the study of experience-based measures using cognitive paradigms. The current study’s findings are consistent with the rapidly growing psychological literature indicating that, while individuals are generally accurate when making crude predictions of emotional valence (e.g., a vacation will be good, but surgery will be bad), they make errors when predicting just how intense their emotional experiences will be (Loewenstein & Schkade, 1999; Wilson & Gilbert, 2003; Wilson & Gilbert, 2008; Wilson et al., 2000). As with joy, misery, regret, and other positive and negative emotional valences examined within the affective forecasting literature (Ayton, Pott, & Elwakili, 2007; Hsee & Hastie, 2006; Gilbert, Morewedge, Risen, & Wilson, 2004; Gilbert, Pinel, Wilson, Blumberg, & Wheatley, 1998; Wilson et al., 2000; Wilson & Gilbert, 2003), there are significant discrepancies in the intensities of individuals’ anticipated and actual experiences of cognitive effort. Behavioural-decision researchers have identified key systematic sources of biases that lead to inaccurate predictions of affect and cognition (Hsee & Hastie, 2006; Wasko & Pury, 2009). For example, forecasters often exhibit focalism, whereby they focus on only certain details of a present event and ignore other factors when making predictions about the outcomes of that event. Forecasters’ often experience immune neglect, which is the meta-cognitive unawareness of, or lack of acknowledgement in, one’s ability to adapt and cope with a negative event over time. We discuss these biases in the context of interpreting the findings of the current study. Participants provided anticipated ratings of cognitive effort and discomfort after completing practice blocks of the PASAT or SART, which gave participants with some experience of the task upon which to base their ratings. The purpose and selection of these particular tasks were based on the fact that both tasks elicit considerable effort, but are demanding in different ways and activate different underlying mental processes. Thus, each task has distinct characteristics that may impact projected effort. For the PASAT, participants were informed that they would undergo a working memory task and that they would be required to add the next number to each preceding digit. When a respondent performs the PASAT as intended (i.e., provide a response after each digit), the cognitive demands include an active maintenance and control of task-relevant information (Miyake & Shah, 1999; Gonzalez et al., 2006), but also arithmetic retrieval. Alternatively, on the SART, the cognitive demand includes sustained attention. Taken as a whole, though both tasks require the processing of information, brief practice with each task signals different expectations about cognitive effort due to its characteristics presented during the practice. The PASAT required participants to carefully track each single or double digit and correctly add it to a previous single or double digit, which may seem effortful at the outset (i.e., during practice). In contrast, the SART required participants to monitor and press a button on the keyboard in response to single digits and to withhold a response in the presence of an infrequent target, which may seem effortless at the outset. Thus, the finding that SART participants anticipated significantly less cognitive effort than PASAT participants could be attributed to SART participants’ focus—at the time of making predictions—on the seemingly straightforward characteristics of the SART, as opposed to PASAT participants’ focus on having to 12

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correctly complete mathematical problems, which often feels aversive (Ashcraft, 2002; Stodolsky, 1985). The observation that cognitive effort ratings decreased among PASAT participants but increased among SART participants could reflect participants’ lack of meta-cognitive monitoring of their ability to adapt to the cognitive demands of their respective tasks over time. For example, the assumption underlying the PASAT is that most individuals who learned their number bonds well in school (i.e., simple arithmetic facts) can retrieve such information in a fairly effortless and automatic way with practice. Thus, performance on the PASAT reflects an ability to become accustomed to incoming information, not the ability to carry out the simple arithmetic involved. In contrast, the central idea behind the SART is that the continuous performance over 75 or 225 trials, with the long and unpredictable intervals between infrequent targets (i.e., the digit 3), encourages the development of an automatic response to nontarget ‘go’ trials (i.e., all other digits from 1 to 9) and that vigilant monitoring is required to withhold this response on the infrequent ‘no-go’ target trials. This contrasts with previous sustained attention paradigms that have typically required participants to respond to the infrequent target (Loken, Thornton, Otto, & Long, 1995; Parasuraman, Mutter, & Molloy, 1991; Whyte, Polansky, Fleming, Coslett, & Calvallucci, 1995). Robertson et al. (1997) argue that in these paradigms, it is responses to the target stimuli that can become automatized, which tends to increase performance and makes the detection of attention lapses harder. Anecdotal and experimental evidence suggests that it is usually harder to maintain continuous attention in intellectually unchallenging, monotonous tasks than cognitively demanding but interesting ones (Kahneman, 1973; Manly et al., 2003; for a review, see Robertson & O’Connell, 2010). Simple, repetitive tasks requiring continuous attention are often found to be associated with increased stress responses and higher subjective effort expenditure, as compared with more complex, variable tasks (Warm et al., 2008). In the context of the current study, it is likely that PASAT participants became better adjusted to the pace of incoming information and arithmetic computations and thus, felt less cognitively taxed during the task. On the other hand, given the unpredictability and infrequency of the target digit, it is likely that SART participants actually felt (and remembered feeling) that the continuous need for sustained attention was more cognitively effortful than they had anticipated. 4.4. Implications of task characteristics and anticipated cognitive effort The current findings suggest that we may not recognize at the outset of a task how much cognitive effort will be required, but as the task progresses, our level of effort changes. The expectations may initially be inaccurate, likely informed by certain forecasting biases, but then change with continued exposure to the task. Failures to accurately predict the amount of cognitive effort an activity will require is significant because subjective cognitive effort acts as a self-regulatory ‘signal’ that subsequently motivates changes in behaviour (Hagger, Wood, Stiff, & Chatzisarantis, 2010; Karoly, 1993; Paas, Tuovinen, Merriënboer, & Darabi, 2005; Silvestrini & Gendolla, 2013) or is taken in as ‘input’ in decision-making processes (Botvinick, Huffstetler, & McGuire, 2009; Kool et al., 2010; Kurzban et al., 2013; Westbrook & Braver, 2015). Thus, the implication of this work is that people may make choices to start or continue with particular activities on the bases of inaccurate predictions about how cognitively taxing the activity will be, as has been shown with other positive or negative affect (Baumeister, Vohs, Dewall, & Zhang, 2007; Mellers & Mcgraw, 2001). However, the differences observed between anticipated and real-time effort among participants for both tasks suggest that, while individuals do not correctly anticipate how cognitively effortful a task will feel, participants are not solely relying on these predictions when making real-time judgments about how effortful and uncomfortable a task currently feels. Instead, individuals are sensitive to the realities of “in-the-moment” experiences of effort and discomfort, and such discrepancies between anticipated and real-time effort may also influence decisions about the appropriate re-allocation of cognitive resources. Future research should continue to explore the capacity to anticipate cognitive effort and discomfort, but also assess individual differences in sensitivities to real-time changes in effort and how such changes influence decisions about the exertion of effort. 4.5. Future directions The current study included two cognitive tasks to illustrate how the distinct characteristics of these tasks—working memory versus sustained attention—influence anticipations of cognitive effort. However, there are numerous parameters of each task that can be varied, such as the inter-stimulus timing of a task (Gronwall, 1977) or whether participants are responding to or withholding a response to the infrequent target (Loken et al., 1995; Parasuraman et al., 1991; Whyte et al., 1995). Thus, we acknowledge that our findings do not generalize to all instantiations of the PASAT and SART. In addition to exploring anticipated effort and changes in this subjective experience among different variations of the PASAT and SART, as well as among other cognitively-demanding tasks, future research should seek to determine exactly how task characteristics give rise to varying predictions of effort; that is, identifying and cataloguing specific cognitive effort forecasting biases that arise from certain tasks and, in turn, lead to forecasting errors. Moreover, future research should consider other variables that might impact individuals’ capacity to accurately anticipate cognitive effort, such as state factors (e.g., valence or arousal at the time of making predictions; Gasper & Bramesfeld, 2006; Gasper & Clore, 2000; Gohm, 2003; Loewenstein, 1996), dispositional factors (e.g., conscientiousness, the need for cognition, flow, absorption, boredom propensity, mind-wandering etc; Cacioppo, Petty, Feinstein, & Jarvis, 1996; Carriere, Seli, & Smilek, 2013; Csikszentmihályi, 2008; Farmer & Sundberg, 1986; Harris, Vine, & Wilson, 2017; John & Srivastava, 1999; Zuckerman, 1979) or under-studied individual differences in attributions or attitudes about exerting cognitive effort (e.g., the moderating role of ADHD diagnosis; Koriat, Nussinson, & Ackerman, 2014). Finally, when participants provided their ratings of anticipated, real-time, and retrospective discomfort, no specification as to what discomfort could be was provided. We acknowledge that the feeling of discomfort may be characterized by more discrete secondary or tertiary negative emotions (e.g., boredom, worry, frustration; Shaver, Schwartz, Kirson, & O’Connor, 1987; Parrott, 2001), which may vastly differ between tasks and change over the course of each task. Thus, future research should 13

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consider measuring more distinct negative affect in order to obtain more precision in participants’ subjective experiences during cognitively demanding tasks. 5. Conclusions Although cognitive effort may covary with objective dimensions of task demands, it cannot be described purely in these objective terms. The present study examined cognitive effort from the perspective of subjective self-report, specifically individuals’ ability to accurately anticipate how effortful a working memory task or sustained attention task will be based on a short sample of one of the two tasks. Rather than relying on “objective” task performance (Haji, Rojas, Childs, Ribaupierre, & Dubrowski, 2015; Yeh & Wickens, 1988) or difficulty (Jansma, Ramsey, Zwart, Gelderen, & Duyn, 2007) as indices for cognitive effort, we directly asked participants how effortful they anticipated their assigned task to be, how effortful the task actually feels, and how effortful they remember the task to have felt. Furthermore, rather than theoretically proposing (Kool & Botvinick, 2013; Kurzban et al., 2013) or inferring the negative affect associated with cognitive effort from task preference or avoidance (Kool et al., 2010), we also collected ratings of discomfort. Our findings suggest that, much like discrete positive and negative valences, individuals inaccurately anticipate the amount of cognitive effort a task will extract. Furthermore, these forecasting errors and the trajectory of subjective effort are contingent upon the task completed, specifically the mental processes and forecasting biases that are elicited by the task’s characteristics. Participants assigned to the working memory task anticipated significantly more cognitive effort than they actually felt during the task. Participants assigned to the sustained attention task anticipated significantly less cognitive effort than they actually felt during the task and later recalled feeling. Between both task groups, SART participants anticipated, felt, and recalled feeling significantly more discomfort. Given the self-regulatory nature of subjective cognitive effort, it would be useful to continue to examine the impact of anticipated cognitive effort, as well as the impact of the degree of anticipation inaccuracy, on subsequent effort avoidance or approach behaviours. Declaration of Competing Interest None. References Ackerman, R., & Thompson, V. A. (2017). Meta-reasoning: Monitoring and control of thinking and reasoning. Trends in Cognitive Sciences, 21(8), 607–617. https://doi. org/10.1016/j.tics.2017.05.004. Allport, G. W. (2000). Stereotypes and prejudice: Essential readings. New York, N.Y.: Psychology Press. American Psychiatric Association (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Washington, D.C.: Author. Ashcraft, M. H. (2002). Math anxiety: Personal, educational, and cognitive consequences. 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