Do Attentional Lapses Account for the Worst Performance Rule?

The worst performance rule (WPR) describes the phenomenon that individuals’ slowest responses in a task are often more predictive of their intelligence than their fastest or average responses. To explain this phenomenon, it was previously suggested that occasional lapses of attention during task completion might be associated with particularly slow reaction times. Because less intelligent individuals should experience lapses of attention more frequently, reaction time distribution should be more heavily skewed for them than for more intelligent people. Consequently, the correlation between intelligence and reaction times should increase from the lowest to the highest quantile of the response time distribution. This attentional lapses account has some intuitive appeal, but has not yet been tested empirically. Using a hierarchical modeling approach, we investigated whether the WPR pattern would disappear when including different behavioral, self-report, and neural measurements of attentional lapses as predictors. In a sample of N = 85, we found that attentional lapses accounted for the WPR, but effect sizes of single covariates were mostly small to very small. We replicated these results in a reanalysis of a much larger previously published data set. Our findings render empirical support to the attentional lapses account of the WPR.

Optimizing Performance and Mental Skills With Mindfulness-Based Attention Training: Two Field Studies With Operational Units

ABSTRACT Introduction Mental skills such as focusing attention and managing emotions are essential for optimal performance in high-stress occupations. Studies with military samples have demonstrated that mindfulness training (MT) led to improved computer-based cognitive performance. Materials and Methods To examine the impact of MT on operational performance, mental skills, and psychological health, a short-form program, Mindfulness-Based Attention Training (MBAT), was delivered to active duty soldiers as part of two randomized trials. Participants in study 1 (n = 121) and study 2 (n = 77) were randomized to one of three conditions: MT with proctored practice, MT with unproctored practice, or a waitlist control. Weekly 2-hour MBAT sessions were offered to participants in both MT conditions for 4 weeks. Beyond these sessions, participants also engaged in mindfulness practice that was proctored (within the occupational context) or unproctored (left up to the individual) for four subsequent weeks. Results Overall, the frequency of mindfulness practice was generally associated with better performance and improvements in mental skills. In study 1, those who practiced 3 or more days per week performed better on marksmanship under physical stress and reported fewer attentional lapses, less emotion regulation difficulties, greater mental toughness, and higher self-reported mindfulness compared to those who did not practice. In study 2, the frequency of mindfulness practice was associated with fewer attentional lapses and emotion regulation difficulties. Conclusions Consistent with prior findings, results suggest that regular engagement in MT practice may help to optimize operational performance and improve mental skills in military cohorts.

Psychometric Curves Reveal Three Mechanisms of Vigilance Decrement

The vigilance decrement is a decline in signal detection rate that occurs over time on a sustained-attention task. The effect has typically been ascribed to conservative shifts of response bias and losses of perceptual sensitivity. Recent work, though, has suggested that sensitivity losses in vigilance tasks are spurious, and other findings have implied that attentional lapses contribute to vigilance failures. To test these possibilities, we used Bayesian hierarchical modeling to compare psychometric curves for the first and last blocks of a visual vigilance task. Participants were a convenience sample of 99 young adults. Data showed evidence for all three postulated mechanisms of vigilance loss: a conservative shift of response bias, a decrease in perceptual sensitivity, and a tendency toward more frequent attentional lapses. Results confirm that sensitivity losses are possible in a sustained-attention task but indicate that mental lapses can also contribute to the vigilance decrement.

Do attentional lapses account for the worst performance rule?

The worst performance rule (WPR) describes the phenomenon that individuals’ slowest responses in a task are often more predictive of their intelligence than their fastest or average responses. To explain this phenomenon, Larson and Alderton (1990) suggested that occasional lapses of attention during task completion might be associated with particularly slow reaction times. Because less intelligent individuals should experience lapses of attention more frequently, reaction time distribution should be more heavily skewed for them than for more intelligent people. Consequently, the correlation between intelligence and reaction times should increase from the lowest to the highest quantile of the response time distribution. This attentional lapses account has some intuitive appeal, but has not yet been tested empirically. Using a hierarchical modeling, we investigated whether the WPR pattern would disappear when including different behavioral, self-report, and neural measurements of attentional lapses as predictors. In a sample of N = 85, we found that attentional lapses accounted for the WPR, but effect sizes of single covariates were mostly small to very small. We replicated these results in a reanalysis of a larger previously published data set (N = 352). Our findings render empirical support to the attentional lapses account of the WPR.

Predicting lapses of attention with sleep-like slow waves

Attentional lapses occur commonly and are associated with mind wandering, where focus is turned to thoughts unrelated to ongoing tasks and environmental demands, or mind blanking, where the stream of consciousness itself comes to a halt. To understand the neural mechanisms underlying attentional lapses, we studied the behaviour, subjective experience and neural activity of healthy participants performing a task. Random interruptions prompted participants to indicate their mental states as task-focused, mind-wandering or mind-blanking. Using high-density electroencephalography, we report here that spatially and temporally localized slow waves, a pattern of neural activity characteristic of the transition toward sleep, accompany behavioural markers of lapses and preceded reports of mind wandering and mind blanking. The location of slow waves could distinguish between sluggish and impulsive behaviours, and between mind wandering and mind blanking. Our results suggest attentional lapses share a common physiological origin: the emergence of local sleep-like activity within the awake brain.

Linking Phubbing Behavior to Self-Reported Attentional Failures and Media Multitasking

Phubbing, or using a phone to snub another person, has been investigated through social and personality frameworks. Phubbing involves attending to and performing competing tasks, implying the involvement of attentional abilities. Yet, past research has not yet used a cognitive framework to establish a link between phubbing and attention. Using self-report data from a large online sample, we explored the associations between phubbing and everyday attentional failures. Phubbing was associated with difficulties in attentional shifting and distractibility, frequent attentional lapses, spontaneous and deliberate mind wandering, and attention-related cognitive errors. When examining these attention variables alongside several psychosocial and personality variables, attention-related cognitive errors acted as the biggest predictor of phubbing behavior. Phubbing was also positively correlated with media multitasking, which is a conceptually similar yet distinct technology use behavior. The results suggest that perceived everyday attentional failures are strongly associated with, and to an extent can predict, phubbing behavior, even more so than some social and personality variables. Technology has incorporated itself as a necessity, or at the very least a favored convenience, in most people’s lives. Characterizing technology multitasking behaviors from a variety of frameworks can help us better understand who is engaging in these behaviors and why.

Increased cortical excitability but stable effective connectivity index during attentional lapses

Modern lifestyle curtails sleep and increases night-time work and leisure activities. This has a deleterious impact on vigilance and attention, exacerbating chances of committing attentional lapses, with potential dramatic outcomes. Here, we investigated the brain signature of attentional lapses and assessed whether cortical excitability and brain response propagation were modified during lapses and whether these modifications changed with aging. We compared electroencephalogram (EEG) responses to transcranial magnetic stimulation (TMS) during lapse and no-lapse periods while performing a continuous attentional/vigilance task at night, after usual bedtime. Data were collected in healthy younger (N=12; 18-30 y) and older individuals (N=12; 50-70 y) of both sexes. The amplitude and slope of the first component of the TMS-Evoked Potential (TEP) were larger during lapses. In contrast, TMS response scattering over the cortical surface, as well as EEG response complexity, did not significantly vary between lapse and no-lapse periods. Importantly, despite qualitative differences, age did not significantly affect any of the TMS-EEG measures. These results demonstrate that attentional lapses are associated with a transient increase of cortical excitability. This initial change is not associated with detectable changes in subsequent effective connectivity - as indexed by response propagation - and are not markedly different between younger and older adults. These findings could contribute to develop models aimed to predicting and preventing lapses in real life situations.

Attentional lapses cannot explain the worst performance rule

The worst performance rule describes the often-observed phenomenon that individuals' slowest responses in a task are more predictive of their intelligence than their fastest or average responses. To explain this phenomenon, Larson and Alderton (1990) suggested that occasional lapses of attention might result in slower reaction times. Because less intelligent individuals are more likely to experience lapses of attention, they should show a more heavily skewed reaction time distribution, causing an increase in correlations between reaction times and intelligence across the percentiles of the distribution. The attentional lapses account has been well-received, not least because of to its intuitive appeal, but has never been subjected to a direct empirical test. Using state-of-the-art hierarchical modeling approaches to quantify and test the worst performance rule, we investigated in a sample of 98 participants if different behavioral, self-report, and neural measures of attentional control accounted for the phenomenon. Notably, no measure of attentional lapses accounted for increasing covariances between intelligence and reaction time from best to worst performance. Hence, our results challenge the attentional lapses account of the worst performance rule.