Exp Brain Res DOI 10.1007/s00221-015-4291-z

RESEARCH ARTICLE

The effects of self‑control on cognitive resource allocation during sustained attention: a transcranial Doppler investigation Alexandra Becker1 · Arielle R. Mandell1   · June P. Tangney1 · Linda D. Chrosniak1 · Tyler H. Shaw1 

Received: 18 October 2014 / Accepted: 16 April 2015 © Springer-Verlag Berlin Heidelberg 2015

Abstract  Vigilance, or sustained attention, is a required ability in many operational professions. While past research has consistently indicated that vigilance performance declines over time, referred to as the vigilance decrement, the theoretical mechanisms underlying the decrement continue to be explored. In the current study, trait self-control was examined to determine how this individual differences measure may contribute to the theoretical explanation of vigilance decrement. Transcranial Doppler sonography (TCD) was used as a measure of cerebral blood flow velocity (CBFV), as previous research has indicated that CBFV may index attentional resource allocation during vigilance (e.g., Shaw et al. in Hum Factors Ergon Soc 50:1619–1623, 2009). Participants performed a demanding 12-min computer-based vigilance task. Prior to the task, a validated self-report measure was used to determine trait-level selfcontrol, and subjective workload was measured after the task was completed. Participants were divided, based upon survey responses, as either low- or high-trait self-control. Performance results showed a significant decrement across participants, but no significant main effect or interaction relating to the self-control measure was observed. Results relating to the TCD measure showed a significant decline in CBFV in the low self-control group, but no CBFV decrement was observed in the high self-control group. The subjective workload results revealed a nonsignificant trend of the low self-control group becoming more frustrated after Alexandra Becker and Arielle R. Mandell have contributed equally to this manuscript. * Arielle R. Mandell [email protected] 1



Department of Psychology, George Mason University, MS 3f5, Fairfax, VA, USA

the task. These results suggest that there are differences in the resource allocation strategies between low and high self-control participants. How trait self-control can add to an understanding of the theoretical underpinnings of sustained attention performance is discussed. Keywords  Self-control · Vigilance · Cognitive resource theory · Transcranial Doppler sonography · Cerebral blood flow velocity

Introduction Vigilance or sustained attention is the ability to maintain attention and respond to subtle environmental changes (Warm et al. 2008). This ability is required in professions that rely on display monitoring, such as air traffic control, airport baggage inspection, and the maintenance of automated systems. Although the study of vigilance has a very long and extensive history (e.g., Mackworth 1948), interest in this construct has not waned due to the importance of vigilance in operational contexts. Moreover, limited vigilance ability is associated with the symptomology of multiple mental health diagnoses, such as schizophrenia (Levin et al. 1996), attention deficit hyperactive disorder (Seidel and Joschko 1990), Alzheimer’s disease (Berardi et al. 2005), and bipolar disorder (Clark et al. 2002). Research investigating the mechanisms of vigilance thus has high relevance to human factors research as well as psychology at large. During vigilance tasks, participants respond to infrequent and unpredictable occurrences of pre-designated critical signals that are embedded within a stream of neutral, non-critical signals. While a seemingly simple task, previous research has shown that the efficiency of performance

13



declines as the task is continuously performed (Helton and Russell 2011; Helton et al. 2008; See et al. 1995; Ossowski et al. 2011; Shaw et al. 2009, 2012). This finding has been termed the vigilance decrement. While there have been many explanations for the vigilance decrement over the course of its almost 70 year history, the theory accepted by most researchers to explain vigilance is cognitive resource theory (Kahneman 1973; Parasuraman and Davies 1984). Resource theory provides a workload account for vigilance performance. Specifically, the theory suggests that the decrement occurs because of a depletion of information-processing resources that are not replenished over time when the task is performed continuously (Parasuraman et al. 1987; Warm and Dember 1998). The purpose of the current paper is to explore how an individual difference measure, self-control, can affect the way in which resources are allocated to vigilance task performance. While resource theory provides an overarching conceptual framework for understanding the decrement, especially at the group level, research exploring vigilance has revealed that individuals vary greatly in their proficiency to perform vigilance tasks (Parasuraman 2009). The first section of this introduction will provide evidence for resource theory and discuss how an understanding of individual differences can aid in our understanding of the resource/performance relation. The second section will focus on a specific individual difference measure, self-control, which may be linked to resource allocation strategies during vigilance. The introduction will conclude with a statement about the specific objectives of the current study. The resource theory of vigilance: utilization versus allocation Support for the resource theory of vigilance comes from several lines of evidence in the vigilance literature. Studies using the NASA Task Load Index (NASA-TLX; Hart and Staveland 1988), a self-report rating scale that provides a measure of perceived mental workload, have indicated that the performance of vigilance tasks requires high mental effort (Warm et al. 2008). Moreover, a series of studies has shown that vigilance tasks impose a considerable degree of stress upon operators (Helton et al. 1999; Szalma et al. 2004). More recent evidence offers neurophysiological support for resource theory, particularly evidence coming from research using transcranial Doppler sonography (TCD). TCD is an ultrasound procedure that allows for continuous monitoring of cerebral blood flow velocity (CBFV) in the main-stem intracranial arteries. This neurophysiological technique has been shown to have the ability to measure changes in cognitive activity during a wide range of tasks (see Duschek and Schandry 2003, for a review). The logic

13

Exp Brain Res

underlying TCD is that when a particular area of the brain becomes metabolically active, such as in the performance of mental tasks, by-products of this activity, such as carbon dioxide (CO2), increase. This increase in CO2 leads to a frequency shift in the amount of oxygenated hemoglobin delivered to the underlying cortical regions (Aaslid 1986). Studies examining the relation between CBFV and performance on sustained attention tasks have shown that (1) the absolute level of blood flow velocity is directly related to increases in task difficulty (Shaw et al. 2010, 2012, 2013a, b), (2) the vigilance decrement is paralleled by a temporal decline in cerebral hemovelocity (Shaw et al. 2009; Warm et al. 2008), (3) the CBFV effects are lateralized to the right cerebral hemisphere (Parasuraman et al. 1998), and (4) the temporal decline in CBFV only occurs when observers are actively engaged in the task. Control observers asked to merely look at a display for an equivalent amount of time as their experimental group counterparts do not display a temporal decline in CBFV (Hitchcock et al. 2003; Shaw et al. 2009). The current interpretation that is drawn from the TCD and vigilance performance relation is that the CBFV measure reflects the loss or drainage of attentional resources. However, there is recent evidence that has suggested that the CBFV measure may be indicative of the operator’s perception of the amount of effort that is required to perform the task. In other words, CBFV levels may be indicative of how resources are being allocated to task performance and not merely the extent to which resources are being depleted. As an example of this, a recent study examined the effects that extensive experience may have on resource allocation (Shaw et al. 2013b). In that study, operators were either given 20 min or 14 h of practice with a vigilance task. While there was no performance difference between the two groups (both groups showed a vigilance decrement over time), the data relating to the CBFV measure showed that CBFV was initially elevated and declined over time for the novice group, but that the more experienced operators did not show any increase in CBFV or temporal decline from a resting baseline. A result of this sort indicates that the experienced group did not require as many resources for task performance and that group (as indicated by the CBFV measure) allocated less effort to the task than novice operators, a result that is consistent with fMRI studies showing reductions in brain activation with extensive practice (Hill and Schneider 2006). These findings indicate that the CBFV measure may be a measure of task-directed effort and not just resource utilization. Disentangling whether the decrement is due to resource drainage or resource allocation may be achieved if we consider trait characteristics about the individual. To that end, individual differences associated with vigilance have stimulated a great deal of research. There have been attempts to

Exp Brain Res

examine individual differences in vigilance from the perspective of molecular genetics (Parasuraman 2009) and personality influences on vigilance performance (see Finomore et al. 2009 for a review). With regard to personality, even though a great deal of research has been conducted to examine the effects of trait characteristics on vigilance, and some personality characteristics have indeed been shown to be predicative of vigilance, results have nevertheless been inconsistent. There are two potential explanations for this lack of predictive power. First, the inability of any single individual difference measures to accurately predict vigilance performance is a reflection of the fact that the personality dimension is so complex that any single measure may be ineffective in picking up the variance necessary to account for vigilance ability. This would mean that multivariate approaches to predicting vigilance would be more effective, and indeed, this notion has been supported by previous research adopting a multivariate strategy (Shaw et al. 2010). A second explanation is that the trait characteristics that have been used in previous research focus solely on energetic effects, which reflect higher levels of overall arousal. For example, extraverts typically perform more poorly on vigilance tasks as compared to introverts (Koelega 1992), and accounts drawn from the resource theory perspective (e.g., Humphreys and Revelle 1984; Shaw et al. 2010) suggest that arousal mediates the effects of personality on performance by influencing the amount of attentional resources that are available for processing. More specifically, the inferior performance of extraverts is attributable to fewer resources being available for performance because of a lowered state of arousal. Self‑control and vigilance performance An approach that deserves more attention in vigilance research is one that seeks to find traits related to the regulation of effort as opposed to traits that modulate overall arousal. Vigilance tasks demand sustained task performance and attentional engagement. If vigilant behavior is viewed as an act of self-control, then the decrement can be viewed as a failure to self-regulate (Muraven and Baumeister 2000). A more general theory of self-regula‑ tory strength is thus suggested (Baumeister et al. 2007), so it is appropriate to examine how individual variation in self-control can impact performance on tasks requiring vigilance. Trait self-control refers to the personality trait ability to self-override responses and alter personal states or behaviors that are automatic (Baumeister and Alquist 2009). Self-control is shown in many areas of daily life, such as concentration, emotional regulation, fighting boredom, and impulse control (Tangney et al. 2004; de Ridder et al. 2012). The benefit of high self-control is that one can maximize long-term positive outcomes (DeWall et al. 2011; Tangney et al. 2004).

Underlying the current conceptualization of self-control are a few key assumptions. First, acts of self-control and volition require strength or energy. Second, all selfcontrol, no matter the domain, operates on the same general resource reservoir. This means that if self-control is expended to enforce a diet, for example, then self-control in other aspects of life, such as the ability to manage interruptive behavior, will be diminished. Finally success or failure of self-control depends upon individual level of strength. More specifically, individuals naturally vary in baseline self-control resources (Muraven and Baumeister 2000; Vohs et al. 2008). While there have been very few research studies linking self-control to vigilance, it should be noted that there has been some conjecture in the literature as to the nature of the relation (e.g., Langner et al. 2010; Shaw et al. 2013a). The few studies that have looked at self-control in relation to vigilance generally support the idea that depleting the self-control resource can affect vigilance. For example, Green and Rogers (1995) found that individuals who reported being on a weight-reducing diet had reduced accuracy and increased reaction times on a vigilance task. Also, theories accounting for the finding that individuals with ADHD have reduced vigilance suggest that self-control is the mechanism driving the ADHD/performance relation (Barkley 1997). The current study The purpose of the current experiment is to further examine both the theoretical and neurocognitive mechanisms for the decrement function. Specifically, this investigation aims to determine the degree to which self-control is related to vigilance. Since self-regulation is proposed as a cognitive process requiring executive control, it is logical that it will also deplete general cognitive resources when it is expended. Within the current study, individuals with higher levels of trait self-control are expected to show less cognitive resource decline because they will have superior resource allocation strategies due to the ability to delay the onset of task-unrelated thoughts and loss of task-directed attention. Furthermore, TCD will be used to measure the way in which cognitive resources are differentially allocated to performance between high and low self-control observers. Given the aforementioned framework about the relation between self-control and vigilance performance, several specific predictions can be drawn. With regard to performance, it is predicted that individuals with higher levels of trait self-control will exhibit superior vigilance performance and less of a vigilance decrement than their lowertrait-level counterparts. With regard to the CBFV measure, it is predicted that those individuals with higher levels of trait self-control will exhibit less of a decline in CBFV due

13



Exp Brain Res

to their superior resource allocation strategies. Finally, the subjective workload measure, the NASA-TLX, will reveal different subjective workload profiles for low and high selfcontrol observers. More specifically, individuals with high self-control will rate the task as being less demanding and less frustrating.

reliable measure for overall workload, scaled from 0 to 100. The measure examines six different subscales of workload: mental demand, temporal demand, physical demand, performance, effort, and frustration. The first three facets measure the demands that the task imposes on the operator and the last three facets characterize the interaction between the observer and the task.

Methods

Vigilance tasks and CBFV measurement

Participants and design

The vigilance task used in this investigation was originally developed by Temple and associates (Temple et al. 2000). This abbreviated, 12-min vigilance task exhibits many of the same effects as the longer-duration vigilance tasks, including the vigilance decrement and right cerebral dominance (Helton et al. 2009). The levels of stress and workload associated with the abbreviated task have been high, as they are in the case of long-duration tasks (Helton et al. 2008). Moreover, the effects of psychophysical manipulations of task difficulty, such as signal salience, and the role of pharmacological stimulants (caffeine) on performance are similar to those on longer-duration vigilance tasks (Temple et al. 2000). The vigilance task required the repetitive watch keeping of a 8 × 6 mm light gray capital letter (‘O’, ‘D,’ or backwards ‘D’) in a 24-point Avant Garde font exposed for 40 ms each against a visual mask, made up of unfilled circles on a white background. The circular elements of the visual mask measure 1 mm in diameter and are outlined by 0.25-mm-thick black lines. The letter stimulus was randomly presented within each period of watch. The target stimuli (letter ‘O’) occurred with a probability of 20 %, and the non-target stimuli (letters ‘D’ and backward ‘D’) each occurred with a probability of 40 % in each period of watch. Participants were asked to respond to only the infrequent, critical targets (“O”) by hitting the spacebar of the keyboard in front of them. A response within 1000 ms after the onset of a critical stimulus was recorded as correct detection (hit). All other responses were to be registered as errors of commission (false alarms). Hemovelocity measurements in cm/s were taken bilaterally from the right and the left middle cerebral arteries (MCAs) by means of a Nicolet Pioneer TC8080 TCD unit equipped with two 2-MHz ultrasound transducers. The transducers, embedded in a plastic bracket, were secured to the observer’s head by an adjustable plastic strap and located dorsal and immediately proximal to the zygomatic arch along the temporal bone. A small amount of Aquasonic-100 brand ultrasound transmission gel (Parker Laboratories, NJ, USA) was placed on the transducers to facilitate transmission of the ultrasound signal. The distance between the transducer face and the sample volume

Twenty-seven right-handed participants (23 female) aged 18–38 years (M = 20.6 years) served in the study as vigilance observers. All participants were required to refrain from the use of drugs or alcohol for at least 12 h before participation. Handedness was used as a selection criterion because of evidence that right-handers typically have left hemispheric dominance for language and several types of cognitive tasks but there exists a more variable pattern of hemispheric dominance among left-handers (Stroobant and Vingerhoets 2000). Handedness was assessed via self-report and the hand used to sign the informed consent form. Participants were given research participation credit for completion of the study. The participant pool was divided into high self-control (n = 13) and low self-control (n = 14) using a median split based on scores on the selfcontrol scale (discussed below). Self‑report measures Self‑control scale This 36-item self-control scale examines trait self-control in relation to habit breaking, resisting temptation, and selfdiscipline (Tangney et al. 2004). Participants are asked to respond to 36 statements, assigning a number rating for how much the statement applies to them (Scale ranging from 1 to 5, 1 = “not at all” and 5 = “very much”). The scale was developed to address a lack of trait-level measurement of self-control ability. Questions were compiled from a broad spectrum of behaviors (impulse control, achievement and task performance, adjustment, interpersonal relationships, moral emotions, and related personality features). Tangney et al. report that the scale has a test– retest reliability of 87 % over a period of 3 weeks. Sample statements from the scale include: “I am good at resisting temptation” or “I say inappropriate things.” NASA Task Load Index (NASA‑TLX) The NASA-TLX (Hart and Staveland 1988) is a respected measure of perceived mental workload that provides a

13

Exp Brain Res

can be adjusted in 2-mm increments in order to isolate the MCA, which was monitored at depths of 45–55 mm (Aaslid 1986). CBFV was averaged and displayed by the TCD unit approximately once every second for recording. These values were channeled into a personal computer for subsequent data analysis.

the NASA-TLX. The entire testing session lasted approximately 60 min.

Results Data manipulation

Procedure Observers were tested individually in a windowless laboratory. Upon arrival, participants were given a copy of the consent form to review and allowed to ask any questions they may have. After reviewing informed consent, participants completed the self-control scale. Stimulus presentation and response recording were orchestrated by a Dell personal computer. Upon identifying a critical signal, participants were told to execute a response as accurately and quickly as possible by pressing the “spacebar” key on a computer keyboard. The TCD equipment was placed behind the observer during the test condition in order to minimize distraction from the equipment. All operators in all conditions took part in computerbased training which explained the task that they would perform prior to the final 12-min testing session. Participants then received a 2-min practice session which duplicated the task they would perform during the experimental session. In order to be retained in the study, participants had to obtain a minimum of 80 % correct detections with no more than 10 % false alarms during practice. All participants met this criterion. Prior to the start of training and the 12-min watch-keeping session, the MCAs for each participant were isolated bilaterally and transducers were secured onto the head for CBFV recording. After practice but prior to the start of the experimental session, a 5-min baseline was recorded, in which observers were instructed to stare at a blank screen. Only the last 60 s of this baseline was used as the comparison for the task-related CBFV data, as recommended by previous investigations (Aaslid 1986). Immediately following each session, observers completed Table 1  Mean reaction times, hit rates, and false alarm rates for low and high self-control groups

DV Hits (%) Low SC High SC RT Low SC High SC FA (%) Low SC High SC

Period 1

To examine vigilance decrement, the 12-min vigilance task was broken up into six blocks of 2 min and treated as a repeated measures variable. While these blocks hold statistical relevance, the participant experiences a continuous 12-min vigilance task. A median split was utilized to divide self-control scores into low and high groups. The primary reason for this is that of key interest here is the overall difference in allocation strategies between low and high self-control individuals. Thus, manipulating the data in this manner makes the data amenable to a test of effect (ANOVA). Effects of self‑control on vigilance performance Self-control scores were divided into low- and highlevel groups by a median split [total self-control (n = 27, M  = 117.14, SD = 16.18); low self-control (n  = 14, M  = 103.0, SD = 5.24); high self-control (n  = 13, M = 129.5, SD = 11.59)]. Reaction time, hit rate, and false alarm rate were examined using a 2 (self-control) × 6 (periods of watch) mixed analyses of variance (ANOVA). ANOVAs on hit rate and false alarm rate were corrected using the Greenhouse-Geisser correction to account for violations of the sphericity assumption. All means and standard deviations can be found in Table 1. For reaction time, the ANOVA showed no significant effect of high or low self-control grouping, F(1, 25) = 0.55, p > 0.05, ηp2 = 0.02. In addition, there was no significant interaction between periods and self-control, F(1, 125) = 0.58, p > 0.05, ηp2 = 0.02. The main effect of periods was significant, such that reaction time slowed for

Period 2

Period 3

Period 4

Period 5

Period 6

98.1 (3.7) 95.8 (8.8)

95.8 (9.1) 89.9 (16.8)

93.9 (11.1) 89.3 (17.1)

87.8 (19) 86.3 (22.4)

89.1 (16.6) 86 (15)

85.9 (16.1) 84.8 (18)

382.1 (45.6) 378.6 (46.8)

412.4 (40.3) 388.9 (48.9)

434.7 (69.8) 414.8 (62.8)

429.3 (52.4) 420.9 (66.1)

447.4 (61.5) 430.3 (55)

441.7 (60.4) 426.6 (56.2)

0.32 (0.5)

0.8 (1.1)

1.9 (3.8)

1.9 (3.5)

0.22 (0.4)

0.15 (0.4)

0.6 (0.7)

0.37 (0.6)

1.1 (1.9) 0.74 (1)

1.3 (2.6) 0.67 (2.2)

Standard deviation is in parentheses

13



Effects of self‑control on CBFV A 2 (hemisphere) × 2 (self-control) × 6 (periods of watch) ANOVA on the CBFV data revealed that there was a significant decrement in CBFV over time [F(3.23, 80.78)  = 14.21, p  0.05, ηp2  = 0.029]. However, the effect of this variable emerged in a significant CBFV  × self-control interaction [F(3.23, 8.78) = 2.88, p  0.05). Effects of self‑control on perceived mental workload A 2 (self-control) × 6 (TLX subscale) mixed ANOVA showed no significant effects of self-control level, but its subdivisions did show a noteworthy (albeit non-significant) trend. Individuals within the lower self-control group displayed higher levels of frustration from the task (p = 0.12), while showing more comparable levels of mental demand and performance demand. This finding, while not significant, suggests that low self-control participants were more frustrated by the task than those with high self-control. Table 2 shows the weighted TLX scores for high and low self-control levels.

13

1.04 CBFV Relative to Baseline

both groups at a similar rate, F(5, 125) = 21.22, p  0.05). The ANOVA on hit rate displayed similar results to reaction time. The high and low self-control groups did not significantly differ in accuracy [F(1, 25) = 0.33, p > 0.05, ηp2  = 0.01], and the interaction between self-control and period was not significant, F(2.54, 63.47) = 0.44, p > 0.05, ηp2  = 0.02. However, there was a significant decrease in hit rate over time on task, F(2.54, 63.47) = 9.36, p  0.05). The ANOVA on false alarm rate indicated no significant main effect of self-control group [F(1, 25) = 2.38, p > 0.05, ηp2  = 0.09], or period [F(2.63, 65.85) = 1.70, p > 0.05, ηp2 = 0.06], and no significant interaction between self-control and period [F(2.63, 65.85) = 0.88, p > 0.05, ηp2 = 0.03].

Exp Brain Res

LowSC

1.03

HighSC

1.02 1.01 1 0.99 0.98 0.97 0.96

1

2

3

4

5

6

Periods of Watch (2-minutes)

Fig. 1  Cerebral blood flow velocity plotted as a function of six periods of watch (2 min) for low and high self-control groups. Error bars represent standard error

Discussion The purpose of the current investigation was to explore how self-control can influence performance and CBFV during vigilance. It was predicted that individuals with higher levels of trait self-control will exhibit superior vigilance performance and less of a vigilance decrement than observers with lower self-control. It was also predicted that observers with higher levels of trait self-control would exhibit less of a decline in CBFV due to superior resource allocation strategies. Finally, it was predicted that an analysis of the subjective workload profiles would be different for low and high self-control observers. Self‑control: performance effects An analysis of reaction times to critical signals, hit rates, and false alarm rates between low and high self-control observers did not provide robust support for the hypothesis that high self-control observers would outperform their low-self-control counterparts. While there was no performance difference between the groups, it can be seen in Table 1 that there was a trend for the high self-control group to respond to critical stimuli faster. One possibility is that the task was not sufficient in producing a divergence in performance because of its short nature. More specifically, while the abbreviated vigilance task is considered an adequate surrogate for long-duration vigilance tasks, which are typically 40–60 min in length, some investigations using the abbreviated task have indeed shown that it is difficult to elicit robust temporal effects often seen in longer-duration tasks (e.g., Helton et al. 2007). Perhaps a longer duration would have elicited the performance differences relating to self-control.

Exp Brain Res Table 2  NASA-TLX mean weighted subscale scores for both low and high self-control Mental demand

Physical demand

Temporal demand

Performance

Effort

Frustration

Low SC

277.7 (45.8)

70.5 (28.5)

191.8 (39.1)

88.6 (15.6)

215.0 (35.1)

69.1 (36.3)

High SC

331.4 (22.1)

11.8 (4.3)

205.9 (37.7)

128.2 (29.6)

242.7 (36.4)

11.4 (4.3)

Standard error is in parentheses

Self‑control: CBFV and workload effects While there was not a group difference in performance, low self-control participants showed a significantly greater CBFV decline than their high-self-control counterparts over the course of the task. CBFV has been used in prior research as a measure of cognitive resource depletion, specifically during vigilance tasks (e.g., Shaw et al. 2012; Warm et al. 2009). This finding suggests that as CBFV declines, cognitive resources are also declining, such that those with low self-control expended more resources during the task. However, declines in CBFV are generally associated with declines in performance, but a recent body of evidence, as mentioned in the introduction to this report, shows that there may be a dissociation between the neurophysiological and performance measures (cf. Shaw et al. 2013b). It could be the case that despite the lack of performance difference, observers with high self-control did not have to exert as much effort as the low self-control group to achieve the same level of performance. This finding is consistent with the prediction that individuals with high self-control are better able to modulate resources and do not exhaust resources that they have available for processing (Shaw et al. 2013a). Data from the analysis of the NASA-TLX suggest that the low self-control group was more frustrated by the task than those with high control, albeit nonsignificantly. Clearly, the low self-control participants were more frustrated and more exhausted from the task than high selfcontrol participants, potentially due to greater resource expenditure. Follow-up research could examine low and high self-control on a longer or more difficult task, with the expectation that greater performance decline, and increased self-report frustration, would be observed with the low self-control group on a longer-duration task due to greater expenditure and misallocation of cognitive resources. Theoretical and practical implications While the resource theory of vigilance performance argues that cognitive functioning runs on a depletable resource that is expended during the course of vigilance tasks, which causes performance to decline (Kahneman 1973), there is a competing theoretical account for performance. Mindlessness theory states that the vigilance decrement is due to

the loss of attention due to boredom and task routinization (Manly et al. 1999; Robertson et al. 1997). Recently, there has been some suggestion that the two theoretical positions can be reconciled by considering a wandering mind as evidence of a loss of attentional resources (Langner and Eickhoff 2013; Langner et al. 2010; Risko et al. 2012; Shaw et al. 2013a). It could be the case that a more general theory of self-control can integrate the two theoretical positions. More specifically, if vigilant behavior is viewed as a failure of a self-regulatory process, then performance decrements attributed to either loss of attentional capacity (resource theory) or unconscious responses (mindlessness theory) can be unified under a more general theory (Baumeister et al. 2007) that suggests that self-regulatory process can delay the onset of mindlessness. An examination of resource allocation strategies over long periods at the individual level could serve to validate this notion. Since blood flow changes in the current study revealed that CBFV levels were reduced in our low self-control, it could be argued that those with less self-control were less able to direct their attention to the task. Further research would benefit from a measure that examines task-related and taskunrelated thoughts in tandem with the current experiment to see whether low self-control groups truly lack task focus compared to high self-control subjects. On a practical level, the performance decrement has always been of chief concern in vigilance research. This study showed no performance difference between high and low self-control groups. However, it should be noted that the lack of a performance difference when considering individual-level characteristics has been seen in previous research, particularly as it pertains to the effect of extensive practice (Shaw et al. 2013b) and extraversion (Brocke et al. 1996; Nguyen et al. 2013). Indeed, studies that examined differential performance effects of extraversion have shown that divergences in performance may only appear at very late stages of the task (Smulders and Meijer 2008), but that physiological differences (event-related potentials in this case) can appear quite early regardless of whether a performance difference exists (Brocke et al. 1996). Why differences in physiology seem to consistently reveal differences but performance differences do not always emerge needs to be examined further, but the current thinking is that there is a difference in effort reactivity (cf. Brocke et al. 1996) and effort allocation (cf. Shaw et al. 2013a). Specifically,

13



any “disadvantaged” group (e.g., extraverts) can compensate for lower arousal by expending more effort toward the task. With regard to the current study, it could be the case that the low self-control group’s poorer resource allocation strategy required them to expend more effort over time. In other words, the data from the current study implies that the effects of self-control in this case are limited to performance efficiency (how much effort is necessary to attain high quality performance), over performance effectiveness (quality of task performance) (Eysenck and Calvo 1992). One way to examine whether this conclusion is correct would be to employ a secondary task. If indeed the high self-control group has better strategies for allocating resources, there should be additional processing resources available for task allocation, and the expectation would be that the high self-control group would show superior performance on a secondary task than the low self-control group. This could be a subject of a future examination. Conflict of interest  The authors declare that they have no conflict of interest.

References Aaslid R (1986) Transcranial Doppler examination techniques. In: Aaslid R (ed) Transcranial Doppler sonography. Springer-Verlon, Vienna, pp 39–59 Barkley RA (1997) ADHD and the nature of self-control. Guilford Press, New York Baumeister RF, Alquist JL (2009) Is there a downside to good selfcontrol? Self Identity 8:115–130 Baumeister RF, Vohs KD, Tice DM (2007) The strength model of self-control. Curr Dir Psychol Sci 16:351–355 Berardi AM, Parasuraman R, Haxby JV (2005) Sustained attention in mild Alzheimer’s disease. Developmental Neuropsychol 28(1):507–537 Brocke B, Tasche KG, Beauducel A (1996) Biopsychological foundations of extraversion: differential effort reactivity and the differential P300-effect. Pers Indiv Differ 21:727–738 Clark L, Iverson SD, Goodwin GM (2002) Sustained attention deficit in bipolar disorder. Br J Psychiatry 180:313–319 de Ridder DT, Lensvelt-Mulders G, Finkenauer C, Stok FM, Baumeister RF (2012) Taking stock of self-control: a meta-analysis of how trait self-control relates to a wide range of behaviors. Pers Soc Psychol Rev 16:76–99 DeWall NC, Baumeister RF, Mead NL, Vohs KD (2011) How leaders self-regulate their task performance: evidence that power promotes diligence, depletion, and disdain. J Pers Soc Psychol 100:47–65 Duschek S, Schandry R (2003) Functional transcranial Doppler sonography as a tool in psychophysiological research. Psychophysiology 40:436–454 Eysenck MW, Calvo MG (1992) Anxiety and performance: the processing efficiency theory. Cogn Emot 6:409–434 Finomore VS, Matthews G, Shaw TH, Warm JS (2009) Predicting vigilance: a fresh look at an old problem. Ergonomics 52:791–808 Green MW, Rogers PJ (1995) Impaired cognitive functioning during spontaneous dieting. Psychol Med 25:1003–1010

13

Exp Brain Res Hart SG, Staveland LE (1988) Development of NASA-TLX (Task Load Index): results of empirical and theoretical research in human mental workload. Adv Psychol 52:139–183 Helton WS, Russell PN (2011) Feature absence–presence and two theories of lapses of sustained attention. Psychol Res 75:384–391 Helton WS, Dember WM, Warm JS, Matthews G (1999) Optimism, pessimism, and false failure feedback: effects on vigilance performance. Curr Psychol 18:311–325 Helton WS et al (2007) The abbreviated vigilance task and cerebral hemodynamics. J Clin Exp Neuropsychol 29:545–552 Helton WS, Shaw T, Warm JS, Matthews G, Hancock PA (2008) Effects of warned and unwarned demand transitions on vigilance performance and stress. Anxiety Stress Copin 21:173–184 Helton WS, Matthews G, Warm JS (2009) Stress state mediation between environmental variables and performance: the case of noise and vigilance. Acta Psychol 130:204–213 Hill NM, Schneider W (2006) Brain changes in the development of expertise: neuroanatomical and neurophysiological evidence about skill-based adaptations. In: Ericsson KA, Charness N, Feltovich P, Hoffman RR (eds) Cambridge handbook of expertise and expert performance. Cambridge University Press, Cambridge, pp 223–242 Hitchcock et al (2003) Automation cueing modulates cerebral blood flow and vigilance in a simulated air traffic control task. Theor Issues Ergon Sci 4:89–112 Humphreys MS, Revelle W (1984) Personality, motivation and performance. A theory of the relationship between individual differences and information processing. Psychol Rev 91:153–184 Kahneman D (1973) Attention and effort. Prentice-Hall, Englewood Cliffs Koelega HS (1992) Extraversion and vigilance performance: 30 years of inconsistencies. Psychol Bull 112:239–258 Langner R, Eickhoff SB (2013) Sustaining attention to simple tasks: A meta-analytic review of the neural mechanisms of vigilant attention. Psychol Bull 139(4):870 Langner R, Willmes K, Chatterjee A, Eickhoff SB, Sturm W (2010) Energetic effects of stimulus intensity on prolonged simple reaction-time performance. Psycholl Res 74:99–512 Levin ED, Wilson W, Rose JE, McEvoy J (1996) Nicotine–haloperidol interactions and cognitive performance in schizophrenics. Neuropsychopharmacol 15:429–436 Mackworth NH (1948) The breakdown of vigilance during prolonged visual search. Quart J Exp Psychol 1:6–21 Manly T, Robertson IH, Galloway M, Hawkins K (1999) The absent mind: further investigations of sustained attention to response. Neuropsychologia 37:661–670 Muraven M, Baumeister RF (2000) Self-regulation and depletion of limited resources: does self-control resemble a muscle? Psychol Bull 126:247–259 Nguyen C, Satterfield K, Bellows B, McKnight P, Shaw TH (2013) Assessing resource allocation during vigilance using transcranial Doppler: the effects of extraversion. Hum Factors Ergon Soc 57:818–822 Ossowski U, Malinen S, Helton WS (2011) The effects of emotional stimuli on target detection: indirect and direct resource costs. Conscious Cog 20:1649–1658 Parasuraman R (2009) Assaying individual differences in cognition with molecular genetics: theory and application. Theor Issues Ergon Sci 10:399–416 Parasuraman R, Davies DR (1984) Varieties of attention. Academic Press, San Diego Parasuraman R, Warm JS, Dember WN (1987) Vigilance: taxonomy and utility. In: Mark LS, Warm JS, Huston RL (eds) Ergonomics and human factors: recent research. Springer, New York, pp 11–32

Exp Brain Res Parasuraman R, Warm JS, See JE (1998) Brain systems of vigilance. In: Parasuraman R (ed) The attentive brain. MIT Press, Cambridge, MA, pp 221–256 Risko EF, Anderson N, Sarwal A, Engelhardt M, Kingstone A (2012) Everyday attention: variation in mind wandering and memory in lecture. Appl Cogn Psychol 26:234–242 Robertson IH, Manly T, Andrade I, Baddeley BT, Yiend J (1997) “Oops”: performance correlates of everyday attentional failures in traumatic brain injured and normal subjects. Neuropsychologica 35:747–748 See JE, Howe SR, Warm JS, Dember WN (1995) Meta-analysis of the sensitivity decrement in vigilance. Psychol Bull 117(2):230 Seidel WT, Joschko M (1990) Evidence of difficulties in sustained attention in children with ADDH. J Abnorm Child Psychol 18:217–229 Shaw TH, Warm JS, Matthews G, Riley M, Weiler EM, Dember WN, Tripp L, Finomore V, Hollander TD (2009) Effects of sensory modality on vigilance performance and cerebral hemovelocity. Hum Factors Ergon Soc 50:1619–1623 Shaw TH, Matthews G, Warm JS, Finomore VS, Silverman L, Costa PT Jr (2010) Individual differences in vigilance: personality, ability and states of stress. J Res Pers 44:297–308 Shaw T, Finomore V, Warm J, Matthews G (2012) Effects of regular or irregular event schedules on cerebral hemovelocity during a sustained attention task. J Clin Exp Neuropsychol 34:57–66 Shaw TH, Funke ME, Dillard M, Funke GJ, Warm JS, Parasuraman R (2013a) Event-related cerebral hemodynamics reveal targetspecific resource allocation for both “go” and “no-go” responsebased vigilance tasks. Brain Cogn 82:265–273 Shaw TH, Satterfield K, Ramirez R, Finomore V (2013b) Using cerebral hemovelocity to measure workload during a spatialized auditory vigilance task for novice and experienced observers. Ergonomics 56:1251–1263

Smulders FTY, Meijer EH (2008) Extraversion and performance: a cognitive energetical approach. Pers Indiv Differ 44:475–486 Stroobant N, Vingerhoets G (2000) Transcranial Doppler ultrasonography monitoring of cerebral hemodynamics during performance of cognitive tasks: a review. Neuropsychol Rev 10:213–231 Szalma JL, Warm JS, Matthews G, Dember WN, Weiler EM, Meier A, Eggemeier FT (2004) Effects of sensory modality and task duration on performance, workload, and stress in sustained attention. Hum Factors 46:219–233 Tangney JP, Baumeister RF, Boone AL (2004) High self-control predicts good adjustment, less pathology, better grades, and interpersonal success. J Pers 72:271–322 Temple JG, Warm JS, Dember WN, Jones KS, LaGrange CM, Matthews G (2000) The effects of signal salience and caffeine on performance, workload and stress in an abbreviated vigilance task. Hum Factors 42:183–194 Vohs K, Baumeister RF, Schmeichel BJ, Twenge JM, Nelson NM, Tice DM (2008) Making choices impairs subsequent self-control: a limited-resource account of decision making, self-regulation, and active initiative. J Pers Soc Psychol 94:883 Warm JS, Dember WN (1998) Tests of a vigilance taxonomy. In: Hoffman RR, Sherrick MF, Warm JS (eds) Viewing psychology as a whole: the integrative science of William N. Dember. American Psychological Association, Washington, pp 87–112 Warm JS, Parasuraman R, Matthews G (2008) Vigilance requires hard mental work and is stressful. Hum Factors 50:433–441 Warm JS, Matthews G, Parasuraman R (2009) Cerebral hemodynamics and vigilance performance. Mil Psychol 21(suppl 1):S75–S100

13