Exp Brain Res (2014) 232:565–573 DOI 10.1007/s00221-013-3765-0
Research Article
Practice does not make perfect in a modified sustained attention to response task James Head · William S. Helton
Received: 3 August 2013 / Accepted: 1 November 2013 / Published online: 19 November 2013 © Springer-Verlag Berlin Heidelberg 2013
Abstract In the current investigation, we examined the changes in performance, task-related thoughts (TRT), and task-unrelated thoughts (TUT) over four sessions of a modified sustained attention to response task (SART). Eighteen participants completed a clockwise manual selection SART (Head and Helton in Conscious Cogn 22:913–919, 2013) and a conscious thought questionnaire once a week for four weeks. Response times and errors of commission oscillated over sessions in line with a motor strategy interpretation of the SART. As participants became faster in the task, they made more commission errors. The conscious thought questionnaire failed to show a relationship between errors of commission and TRT and TUT on the SART at either a between-subject or within-subject level of analysis. Commission errors in the SART may be better measures of executive motor control and response strategy than perceptual decoupling. Keywords Attention · Response inhibition · Speed-accuracy trade-offs · Sustained attention · Vigilance Introduction The sustained attention to response task (SART) is widely used to measure lapses of sustained attention in a variety of experimental and clinical contexts (Docktree et al. 2004, 2006; Johnson et al. 2007; Manly et al. 1999; Shaw et al. 2013; Smallwood et al. 2003, 2004). The SART differs from traditionally formatted vigilance or sustained J. Head · W. S. Helton (*) Department of Psychology, University of Canterbury, Private Bag 4800, Christchurch, New Zealand e-mail: [email protected]
attention tasks by inverting the relative ratio of Go and No-Go responses. In most traditional vigilance tasks, participants respond to infrequent Go stimuli and ignore the more frequent and common No-Go stimuli (Hancock 2013). Unlike traditional vigilance tasks, the SART is a high Go and low No-Go target detection task whereby participants respond frequently to neutral stimuli and withhold responses to rare target stimuli (Robertson et al. 1997). The original SART (Robertson et al. 1997) involves participants withholding responses to a predefined numeric target (e.g., 3) and overtly responding to a larger digit set (e.g., 1, 2, 5, 7, 6, 7, 8, and 9) using a simple button response (see Head et al. 2011; Head and Helton 2012; Smallwood and Schooler 2006, for use of word and pictorial targets and distracters used in the SART). The use of the SART has generated an ongoing debate regarding the underlying mechanism of commission errors in the task (failures to withhold a response to the rare Go targets). This debate is not merely a methodological concern as the SART is widely used as a measure of sustained attention, and its use has implications for theories of sustained attention and vigilance itself (see Head et al. 2011; Grier et al. 2003; Helton 2009; Helton et al. 2005, 2009; Helton and Warm 2008; Manly et al. 1999, 2004; Robertson et al. 1997). Some researchers suggest the errors of commission in the SART are relatively reliable behavioral indicators of the participant not attending to the task stimuli (perceptual decoupling; see Smallwood et al. 2007). From this perceptual decoupling perspective, the repetitive and relatively monotonous nature of the task stimuli themselves results in participant disengagement and this in turn results in inappropriate responses. Alternatively, other researchers suggest that SART errors of commission are primarily a measure of speed-accuracy trade-off (SATO), failures of motor control, and/or strategy, not perceptual decoupling
13
566
per se (Head and Helton 2012, 2013; Peebles and Bothell 2004; Stevenson et al. 2011). From this, motor decoupling, perspective, the repetitive nature of SART responding (not the stimuli themselves) causes the development of a selforganizing prepotent ballistic motor response (Head and Helton 2013; Helton et al. 2011; Robertson et al. 1997). This prepotent ballistic motor routine requires active inhibition or a strategic decision to slow responding (Peebles and Bothell 2004). Evidence for this later motor perspective is accumulating. Generally, when the SART is administered, participants are instructed to respond as fast and accurately as possible (Head and Helton 2012, 2013; Helton and Russell 2011a; Manly et al. 1999; Robertson et al. 1997). However, when the participants are instructed to adopt a different response strategy, performance on the SART is subsequently influenced. For example, Seli et al. (2012) provided task instructions to participants to either adopt a faster or slower response strategy. Participants instructed to adopt a slower response strategy had fewer errors of commission. Conversely, those instructed to respond faster had increased errors of commission. Performance on the SART is considerably influenced by either instructions or task alterations which slow response rates. In addition, Head and Helton (2012) constructed a SART in which the Go and No-Go stimuli consisted of forest and city picture scenes instead of repetitive numeric stimuli. The use of pictorial stimuli allowed stimuli to never be repeated in this modified SART. Despite completely eliminating stimulus repetition and monotony, commission errors were actually elevated in this SART compared to when using the simpler repetitive and monotonous numeric stimuli. If SART commission errors result from stimuli monotony, this is hard to explain. Instead, SART commission errors may result from response monotony (pressing over and over again), and the stimuli may themselves prove relatively unimportant, unless the stimuli characteristics influence response rates or facilitate the speed of detection, hence, improving the participants’ response inhibition. Advocates of both perspectives allow for the possibility that some errors of commission in the SART may be due to either mechanism, perceptual, or motor decoupling, and the debate should not be framed in terms of artificial straw men. Nevertheless, like many debates in the behavioral neurosciences (nature-nurture, etc.), the relative proportion of the phenomenon explained is of actual importance. As pointed out by Head and Helton (2013), there is a real difference between whether a hunter (or policeman or soldier) accidentally shoots someone because the hunter did not see the person or failing to stop a quick, but inappropriate motor response, despite full realization at the moment that the hunter is making a mistake. Wearing bright orange gear, for example, might help prevent recognition errors,
13
Exp Brain Res (2014) 232:565–573
but may not eliminate entirely feed-forward motor control errors. While distinctive coloration or anything enabling quicker recognition of the No-Go stimuli will help in the race against the prepotent ballistic motor response (Helton et al. 2011; Smallwood 2013), it will not entirely eliminate errors due to speeded motor response. The SART has been recently modified into a firearms simulation task, and the same commission errors occur even when the response is firing on a person, not pushing a button (see Wilson et al. 2013). Although, for example, there is epidemiological evidence to suggest that wearing orange clothing has reduced hunting accidents, this has not eliminated those accidents and hunters wearing bright orange gear are shot accidentally even at short range in clear, non-cluttered environments (Cole and Patetta 1988). In a recent study, Head and Helton (2013) investigated the motor and perceptual characteristics of the SART by manipulating stimuli location uncertainty and stimuli acquisition using a point and click mouse task. Stimuli location uncertainty was manipulated by presenting a single numeric stimulus in one of the four box locations presented in a cross or plus sign configuration (see Fig. 1 for similar experimental setup). In the clockwise or predictable location task, stimuli would first occur in the top box with subsequent number stimuli occurring in the adjacent box in a clockwise direction. Though the numeric stimulus could be a No-Go signal (the number 8) or a Go signal (numbers 1–9, except 8), the location of the occurrence was entirely predictable. In the random location condition, number stimuli could appear in any of the four boxes at random. In regard to target acquisition, participants either manually selected stimuli or the stimuli were automatically selected for them. In the manual condition, the participants were required to physically move a cursor with a mouse to the box containing either a Go or No-Go number and then press a button to register a Go response. In the automatic acquisition task, the box in which the number stimuli appeared was automatically selected and participants responded to stimuli with a simple button response. The manual selection SART externally slowed down the motor response by requiring a longer time to complete motor action, increasing the Go reaction time by 125 ms. This task modification dramatically decreased the percentages of errors of commission in the SART condition (from 42 to 12.5 %). In other words, the more time that the participant took to make the final button response, the more likely they had time to inhibit the feed-forward motor response (Seli et al. 2013). Head and Helton (2013) also examined the correlations between Go reaction times and errors of commission in their four task conditions. There was a significant negative correlation between these two performance metrics in every condition except for when stimuli were presented in random locations and participants
Exp Brain Res (2014) 232:565–573
567
Fig. 1 Clockwise SART presentation example
had to manually select them (although even in this condition, the relationship was negative, though not statistically significant). Nevertheless, even in the clockwise-predictable location, for manual-select SART, participants appeared to be susceptible to commission errors due to simply responding too fast. One implication of this result is that as participants become more fluent (quicker) in responding in SART-like tasks requiring more complex motor responses, they will likely make more speeded motor control errors. This would especially be the case if the impending location of the stimulus is predictable a priori. The participants are likely to develop a feed-forward motor program when the locations of the stimuli are predictable, similar to the motor programs developed by typists or pianists whose key locations are fixed (spatially predictable). In the current investigation, we examined this developmental process by examining performance of the clockwise-predictable manual selection SART over repeated practice sessions. From the SATO and response strategy perspective of SART performance,
as motor responses become quicker, the participant will make more errors of commission. Interestingly, if the participant is aware of this increase in errors of commission (as they are not perceptually decoupled from the task from this perspective), they may react by strategically slowing down responses in a subsequent session. Therefore, you might expect some oscillation in errors and response time over repeated sessions, but with a general trend of quicker speeds and more elevated commission errors. In a previous study employing the SART in which participants indicated their awareness of commission errors being errors, non-brain injured controls indicated awareness of the error 99.1 % of the time (McAvinue et al. 2005). In this experiment, we also included self-report measures of task-related and task-unrelated thoughts. Generally, each of these measures is calculated by averaging individual items in each measure prior to analysis (Head and Helton 2012; Matthews et al. 2002). However, this analytical approach fails to distinguish participant’s trait or inter-individual differences versus state or intra-individual
13
568
differences (Helton et al. 2013). We suspect that an individual’s trait, or general tendency relative to others to report task-unrelated or task-related thoughts, will relate to performance; however, we also believe that a participant’s state is likely to change with time on task and also relates to performance (see Helton et al. 2013). Many claims about the relationship between thought content and performance are made at the intra-individual level (occurring within a person over time), yet the analytical techniques commonly employed are made at the interindividual level. To address this analytical issue, we will examine within-subject (state) and between-subject (trait) correlations (Zelenski and Larsen 2000). We expect the speed-accuracy trade-off between commission errors and response time will be large at both the state and trait level of analysis. Recent investigations have shown that when participants are more familiar with a task (e.g., driving a known route repeatedly), this may induce mind wandering (e.g., daydreaming) (Yanko and Spalek 2013). The SART is often used as an indirect measure of thought occurrence or daydreaming (Smallwood et al. 2007). Whether this is appropriate or not is an open question (McVay and Kane 2012). Nevertheless, do self-report measures of thoughts change with repeated practice on a SART? If errors of commission in the SART are indicators of episodes of perceptual or motor decoupling due to the participant being engrossed in thought (Smallwood et al. 2007), then we would expect the relationship between TUTs and errors of commission should be positive (more TUTs, more errors of commission) both between-subject and within-subject.
Methods Participants Eighteen students (13 females, 5 males) from a psychology course at the University of Canterbury served as participants for course credit. All the participants had normal or corrected-to-normal vision. Participants ranged in age between 20 and 27 (M = 22 years; SD = 1.85). Materials and procedure Participants were tested individually within the same cubic station at the same time over the span of four weeks (four sessions, one session each week). Participants were seated 50 cm in front of video display terminals (377 mm × 303 mm, 75 Hz refresh rate) that were mounted at eye level. Participant’s head movements were not restrained. Wristwatches and cell phones were surrendered at the start of the experiment each time.
13
Exp Brain Res (2014) 232:565–573
Participants completed a clockwise manual selection SART (Head and Helton 2013). Participants were presented with four 35 mm × 35 mm boxes arranged in a cross pattern (see Fig. 1). During the trial procedure, participants were first presented with a fixation period, which consisted of 4 empty boxes and a 6 mm × 6 mm fixation marker placed in the center of the cross for 200 ms. Participants were instructed to focus on the fixation cross at the onset of each trial to refocus their attention to the center of the screen. The fixation periods also allowed us to reset the mouse cursor to the center for each subsequent trial. After the fixation period, participants gained control of a 10 mm × 10 mm crosshair icon that appeared in the same location as the fixation marker. A single neutral or target stimulus first appeared in the top box for approximately 250 ms followed by 1,000 ms period in which a response could be recorded. During the 1,000 ms, the four boxes remained on the screen. Each subsequent stimulus was presented in the adjacent box in a clockwise direction (see Fig. 1). The total target onset interval was 1,450 ms. During the stimulus presentation, participants were presented with a single black 20-pt Courier New font number stimulus within a box. Participants were instructed to respond to numbers 1–9 except for the target number 8. Target and neutral number sampling were random; however, neutral and target stimuli had equal probability to occur in each of the four boxes. Targets occurred with the probability of 1 out of 9 (p = .11) and neutral numbers with p = .89. Participants made their responses using a Saitek joystick. Participants were instructed to use their index finger of their dominant hand to press a trigger button on the Saitek joystick once the cursor was registered inside of the box containing a neutral number. Once the joystick cursor was registered in the box, it would then become bolded to give the participant visual feedback that the cursor was in the box and ready for a response (for similar procedure see Head and Helton 2013). Participants completed a 16-item version of the Dundee Stress State Questionnaire (DSSQ; Matthews et al. 2002) each week after completing the SART. The DSSQ is a subjective-state questionnaire that assesses affect, motivation, and cognition. The questionnaire consists of 10 scales in total; however, only task-related thoughts (TRT) and task-unrelated thoughts (TUT) were used in this study. Each subscale consisted of 8 items in which a five-point Likert scale was used for each subscale, 5 = “very often,” 4 = “often,” 3 = “a few times,” 2 = “once,” 1 = “never.” Participants completed a 2-min practice trial prior to the experimental block each week. The task was 288 trials with the task lasting approximately 7 min. The total duration of the experiment was 10 min. Presentation of stimuli and response accuracy and timing were achieved using E-prime 2.0 software (Psychology Software Tools, Pittsburgh, PA).
Exp Brain Res (2014) 232:565–573
569
Table 1 Descriptive statistics and trend analyses over sessions, and parenthesis indicates standard deviation Variable
Commissions Omissions Response time TUT TRT
Session
Linear
Quadratic
Cubic
1
2
3
4
F
P
ηp2
0.07 (0.06) 0.16 (0.16) 579 (106) 1.4 (0.40)
0.17 (0.18) 0.06 (0.06) 514 (110) 1.5 (0.54)
0.10 (0.11) 0.05 (0.03) 547 (93) 1.6 (0.48)
0.14 (0.16) 0.04 (0.02) 524 (93) 1.8 (0.67)
1.47 29.81 8.02 5.90
.24
Research Article
Practice does not make perfect in a modified sustained attention to response task James Head · William S. Helton
Received: 3 August 2013 / Accepted: 1 November 2013 / Published online: 19 November 2013 © Springer-Verlag Berlin Heidelberg 2013
Abstract In the current investigation, we examined the changes in performance, task-related thoughts (TRT), and task-unrelated thoughts (TUT) over four sessions of a modified sustained attention to response task (SART). Eighteen participants completed a clockwise manual selection SART (Head and Helton in Conscious Cogn 22:913–919, 2013) and a conscious thought questionnaire once a week for four weeks. Response times and errors of commission oscillated over sessions in line with a motor strategy interpretation of the SART. As participants became faster in the task, they made more commission errors. The conscious thought questionnaire failed to show a relationship between errors of commission and TRT and TUT on the SART at either a between-subject or within-subject level of analysis. Commission errors in the SART may be better measures of executive motor control and response strategy than perceptual decoupling. Keywords Attention · Response inhibition · Speed-accuracy trade-offs · Sustained attention · Vigilance Introduction The sustained attention to response task (SART) is widely used to measure lapses of sustained attention in a variety of experimental and clinical contexts (Docktree et al. 2004, 2006; Johnson et al. 2007; Manly et al. 1999; Shaw et al. 2013; Smallwood et al. 2003, 2004). The SART differs from traditionally formatted vigilance or sustained J. Head · W. S. Helton (*) Department of Psychology, University of Canterbury, Private Bag 4800, Christchurch, New Zealand e-mail: [email protected]
attention tasks by inverting the relative ratio of Go and No-Go responses. In most traditional vigilance tasks, participants respond to infrequent Go stimuli and ignore the more frequent and common No-Go stimuli (Hancock 2013). Unlike traditional vigilance tasks, the SART is a high Go and low No-Go target detection task whereby participants respond frequently to neutral stimuli and withhold responses to rare target stimuli (Robertson et al. 1997). The original SART (Robertson et al. 1997) involves participants withholding responses to a predefined numeric target (e.g., 3) and overtly responding to a larger digit set (e.g., 1, 2, 5, 7, 6, 7, 8, and 9) using a simple button response (see Head et al. 2011; Head and Helton 2012; Smallwood and Schooler 2006, for use of word and pictorial targets and distracters used in the SART). The use of the SART has generated an ongoing debate regarding the underlying mechanism of commission errors in the task (failures to withhold a response to the rare Go targets). This debate is not merely a methodological concern as the SART is widely used as a measure of sustained attention, and its use has implications for theories of sustained attention and vigilance itself (see Head et al. 2011; Grier et al. 2003; Helton 2009; Helton et al. 2005, 2009; Helton and Warm 2008; Manly et al. 1999, 2004; Robertson et al. 1997). Some researchers suggest the errors of commission in the SART are relatively reliable behavioral indicators of the participant not attending to the task stimuli (perceptual decoupling; see Smallwood et al. 2007). From this perceptual decoupling perspective, the repetitive and relatively monotonous nature of the task stimuli themselves results in participant disengagement and this in turn results in inappropriate responses. Alternatively, other researchers suggest that SART errors of commission are primarily a measure of speed-accuracy trade-off (SATO), failures of motor control, and/or strategy, not perceptual decoupling
13
566
per se (Head and Helton 2012, 2013; Peebles and Bothell 2004; Stevenson et al. 2011). From this, motor decoupling, perspective, the repetitive nature of SART responding (not the stimuli themselves) causes the development of a selforganizing prepotent ballistic motor response (Head and Helton 2013; Helton et al. 2011; Robertson et al. 1997). This prepotent ballistic motor routine requires active inhibition or a strategic decision to slow responding (Peebles and Bothell 2004). Evidence for this later motor perspective is accumulating. Generally, when the SART is administered, participants are instructed to respond as fast and accurately as possible (Head and Helton 2012, 2013; Helton and Russell 2011a; Manly et al. 1999; Robertson et al. 1997). However, when the participants are instructed to adopt a different response strategy, performance on the SART is subsequently influenced. For example, Seli et al. (2012) provided task instructions to participants to either adopt a faster or slower response strategy. Participants instructed to adopt a slower response strategy had fewer errors of commission. Conversely, those instructed to respond faster had increased errors of commission. Performance on the SART is considerably influenced by either instructions or task alterations which slow response rates. In addition, Head and Helton (2012) constructed a SART in which the Go and No-Go stimuli consisted of forest and city picture scenes instead of repetitive numeric stimuli. The use of pictorial stimuli allowed stimuli to never be repeated in this modified SART. Despite completely eliminating stimulus repetition and monotony, commission errors were actually elevated in this SART compared to when using the simpler repetitive and monotonous numeric stimuli. If SART commission errors result from stimuli monotony, this is hard to explain. Instead, SART commission errors may result from response monotony (pressing over and over again), and the stimuli may themselves prove relatively unimportant, unless the stimuli characteristics influence response rates or facilitate the speed of detection, hence, improving the participants’ response inhibition. Advocates of both perspectives allow for the possibility that some errors of commission in the SART may be due to either mechanism, perceptual, or motor decoupling, and the debate should not be framed in terms of artificial straw men. Nevertheless, like many debates in the behavioral neurosciences (nature-nurture, etc.), the relative proportion of the phenomenon explained is of actual importance. As pointed out by Head and Helton (2013), there is a real difference between whether a hunter (or policeman or soldier) accidentally shoots someone because the hunter did not see the person or failing to stop a quick, but inappropriate motor response, despite full realization at the moment that the hunter is making a mistake. Wearing bright orange gear, for example, might help prevent recognition errors,
13
Exp Brain Res (2014) 232:565–573
but may not eliminate entirely feed-forward motor control errors. While distinctive coloration or anything enabling quicker recognition of the No-Go stimuli will help in the race against the prepotent ballistic motor response (Helton et al. 2011; Smallwood 2013), it will not entirely eliminate errors due to speeded motor response. The SART has been recently modified into a firearms simulation task, and the same commission errors occur even when the response is firing on a person, not pushing a button (see Wilson et al. 2013). Although, for example, there is epidemiological evidence to suggest that wearing orange clothing has reduced hunting accidents, this has not eliminated those accidents and hunters wearing bright orange gear are shot accidentally even at short range in clear, non-cluttered environments (Cole and Patetta 1988). In a recent study, Head and Helton (2013) investigated the motor and perceptual characteristics of the SART by manipulating stimuli location uncertainty and stimuli acquisition using a point and click mouse task. Stimuli location uncertainty was manipulated by presenting a single numeric stimulus in one of the four box locations presented in a cross or plus sign configuration (see Fig. 1 for similar experimental setup). In the clockwise or predictable location task, stimuli would first occur in the top box with subsequent number stimuli occurring in the adjacent box in a clockwise direction. Though the numeric stimulus could be a No-Go signal (the number 8) or a Go signal (numbers 1–9, except 8), the location of the occurrence was entirely predictable. In the random location condition, number stimuli could appear in any of the four boxes at random. In regard to target acquisition, participants either manually selected stimuli or the stimuli were automatically selected for them. In the manual condition, the participants were required to physically move a cursor with a mouse to the box containing either a Go or No-Go number and then press a button to register a Go response. In the automatic acquisition task, the box in which the number stimuli appeared was automatically selected and participants responded to stimuli with a simple button response. The manual selection SART externally slowed down the motor response by requiring a longer time to complete motor action, increasing the Go reaction time by 125 ms. This task modification dramatically decreased the percentages of errors of commission in the SART condition (from 42 to 12.5 %). In other words, the more time that the participant took to make the final button response, the more likely they had time to inhibit the feed-forward motor response (Seli et al. 2013). Head and Helton (2013) also examined the correlations between Go reaction times and errors of commission in their four task conditions. There was a significant negative correlation between these two performance metrics in every condition except for when stimuli were presented in random locations and participants
Exp Brain Res (2014) 232:565–573
567
Fig. 1 Clockwise SART presentation example
had to manually select them (although even in this condition, the relationship was negative, though not statistically significant). Nevertheless, even in the clockwise-predictable location, for manual-select SART, participants appeared to be susceptible to commission errors due to simply responding too fast. One implication of this result is that as participants become more fluent (quicker) in responding in SART-like tasks requiring more complex motor responses, they will likely make more speeded motor control errors. This would especially be the case if the impending location of the stimulus is predictable a priori. The participants are likely to develop a feed-forward motor program when the locations of the stimuli are predictable, similar to the motor programs developed by typists or pianists whose key locations are fixed (spatially predictable). In the current investigation, we examined this developmental process by examining performance of the clockwise-predictable manual selection SART over repeated practice sessions. From the SATO and response strategy perspective of SART performance,
as motor responses become quicker, the participant will make more errors of commission. Interestingly, if the participant is aware of this increase in errors of commission (as they are not perceptually decoupled from the task from this perspective), they may react by strategically slowing down responses in a subsequent session. Therefore, you might expect some oscillation in errors and response time over repeated sessions, but with a general trend of quicker speeds and more elevated commission errors. In a previous study employing the SART in which participants indicated their awareness of commission errors being errors, non-brain injured controls indicated awareness of the error 99.1 % of the time (McAvinue et al. 2005). In this experiment, we also included self-report measures of task-related and task-unrelated thoughts. Generally, each of these measures is calculated by averaging individual items in each measure prior to analysis (Head and Helton 2012; Matthews et al. 2002). However, this analytical approach fails to distinguish participant’s trait or inter-individual differences versus state or intra-individual
13
568
differences (Helton et al. 2013). We suspect that an individual’s trait, or general tendency relative to others to report task-unrelated or task-related thoughts, will relate to performance; however, we also believe that a participant’s state is likely to change with time on task and also relates to performance (see Helton et al. 2013). Many claims about the relationship between thought content and performance are made at the intra-individual level (occurring within a person over time), yet the analytical techniques commonly employed are made at the interindividual level. To address this analytical issue, we will examine within-subject (state) and between-subject (trait) correlations (Zelenski and Larsen 2000). We expect the speed-accuracy trade-off between commission errors and response time will be large at both the state and trait level of analysis. Recent investigations have shown that when participants are more familiar with a task (e.g., driving a known route repeatedly), this may induce mind wandering (e.g., daydreaming) (Yanko and Spalek 2013). The SART is often used as an indirect measure of thought occurrence or daydreaming (Smallwood et al. 2007). Whether this is appropriate or not is an open question (McVay and Kane 2012). Nevertheless, do self-report measures of thoughts change with repeated practice on a SART? If errors of commission in the SART are indicators of episodes of perceptual or motor decoupling due to the participant being engrossed in thought (Smallwood et al. 2007), then we would expect the relationship between TUTs and errors of commission should be positive (more TUTs, more errors of commission) both between-subject and within-subject.
Methods Participants Eighteen students (13 females, 5 males) from a psychology course at the University of Canterbury served as participants for course credit. All the participants had normal or corrected-to-normal vision. Participants ranged in age between 20 and 27 (M = 22 years; SD = 1.85). Materials and procedure Participants were tested individually within the same cubic station at the same time over the span of four weeks (four sessions, one session each week). Participants were seated 50 cm in front of video display terminals (377 mm × 303 mm, 75 Hz refresh rate) that were mounted at eye level. Participant’s head movements were not restrained. Wristwatches and cell phones were surrendered at the start of the experiment each time.
13
Exp Brain Res (2014) 232:565–573
Participants completed a clockwise manual selection SART (Head and Helton 2013). Participants were presented with four 35 mm × 35 mm boxes arranged in a cross pattern (see Fig. 1). During the trial procedure, participants were first presented with a fixation period, which consisted of 4 empty boxes and a 6 mm × 6 mm fixation marker placed in the center of the cross for 200 ms. Participants were instructed to focus on the fixation cross at the onset of each trial to refocus their attention to the center of the screen. The fixation periods also allowed us to reset the mouse cursor to the center for each subsequent trial. After the fixation period, participants gained control of a 10 mm × 10 mm crosshair icon that appeared in the same location as the fixation marker. A single neutral or target stimulus first appeared in the top box for approximately 250 ms followed by 1,000 ms period in which a response could be recorded. During the 1,000 ms, the four boxes remained on the screen. Each subsequent stimulus was presented in the adjacent box in a clockwise direction (see Fig. 1). The total target onset interval was 1,450 ms. During the stimulus presentation, participants were presented with a single black 20-pt Courier New font number stimulus within a box. Participants were instructed to respond to numbers 1–9 except for the target number 8. Target and neutral number sampling were random; however, neutral and target stimuli had equal probability to occur in each of the four boxes. Targets occurred with the probability of 1 out of 9 (p = .11) and neutral numbers with p = .89. Participants made their responses using a Saitek joystick. Participants were instructed to use their index finger of their dominant hand to press a trigger button on the Saitek joystick once the cursor was registered inside of the box containing a neutral number. Once the joystick cursor was registered in the box, it would then become bolded to give the participant visual feedback that the cursor was in the box and ready for a response (for similar procedure see Head and Helton 2013). Participants completed a 16-item version of the Dundee Stress State Questionnaire (DSSQ; Matthews et al. 2002) each week after completing the SART. The DSSQ is a subjective-state questionnaire that assesses affect, motivation, and cognition. The questionnaire consists of 10 scales in total; however, only task-related thoughts (TRT) and task-unrelated thoughts (TUT) were used in this study. Each subscale consisted of 8 items in which a five-point Likert scale was used for each subscale, 5 = “very often,” 4 = “often,” 3 = “a few times,” 2 = “once,” 1 = “never.” Participants completed a 2-min practice trial prior to the experimental block each week. The task was 288 trials with the task lasting approximately 7 min. The total duration of the experiment was 10 min. Presentation of stimuli and response accuracy and timing were achieved using E-prime 2.0 software (Psychology Software Tools, Pittsburgh, PA).
Exp Brain Res (2014) 232:565–573
569
Table 1 Descriptive statistics and trend analyses over sessions, and parenthesis indicates standard deviation Variable
Commissions Omissions Response time TUT TRT
Session
Linear
Quadratic
Cubic
1
2
3
4
F
P
ηp2
0.07 (0.06) 0.16 (0.16) 579 (106) 1.4 (0.40)
0.17 (0.18) 0.06 (0.06) 514 (110) 1.5 (0.54)
0.10 (0.11) 0.05 (0.03) 547 (93) 1.6 (0.48)
0.14 (0.16) 0.04 (0.02) 524 (93) 1.8 (0.67)
1.47 29.81 8.02 5.90
.24
