Pupillometry and the vigilance decrement: Task-evoked but not baseline pupil measures reflect declining performance in visual vigilance tasks

Baseline and task-evoked pupil measures are known to reflect the activity of the nervous system's central arousal mechanisms. With the increasing availability, affordability and flexibility of video-based eye tracking hardware, these measures may one day find practical application in real-time biobehavioral monitoring systems to assess performance or fitness for duty in tasks requiring vigilant attention. But real-world vigilance tasks are predominantly visual in their nature and most research in this area has taken place in the auditory domain. Here we explore the relationship between pupil size—both baseline and task-evoked—and behavioral performance measures in two novel vigilance tasks requiring visual target detection: 1) a traditional vigilance task involving prolonged, continuous, and uninterrupted performance (n = 28), and 2) a psychomotor vigilance task (n = 25). In both tasks, behavioral performance and task-evoked pupil responses declined as time spent on task increased, corroborating previous reports in the literature of a vigilance decrement with a corresponding reduction in task-evoked pupil measures. Also in line with previous findings, baseline pupil size did not show a consistent relationship with performance measures. We discuss our findings considering the adaptive gain theory of locus coeruleus function and question the validity of the assumption that baseline (prestimulus) pupil size and task-evoked (poststimulus) pupil measures correspond to the tonic and phasic firing modes of the LC.

Propensity to intentional and unintentional mind-wandering differs in arousal and executive vigilance tasks

We typically observe a decrement in vigilance with time-on-task, which favors the propensity for mind-wandering, i.e., the shifting of attention from the task at hand to task-unrelated thoughts. Here, we examined participants’ mind-wandering, either intentional or unintentional, while performing vigilance tasks that tap different components of vigilance. Intentional mind-wandering is expected mainly when the arousal component is involved, whereas unintentional mind-wandering is expected mainly in tasks involving the executive component. The Psychomotor Vigilance Task (PVT) assessed the arousal component, whereas the Sustained Attention to Response task (SART) assessed the executive component of vigilance. The two types of mind-wandering were probed throughout task execution. The results showed that the overall rate of mind-wandering was higher in the PVT than in the SART. Intentional mind-wandering was higher with the PVT than with the SART, whereas unintentional mind-wandering was higher with the SART than with the PVT. Regarding mind-wandering as a function of vigilance decrement with time-on-task, unintentional mind-wandering in the PVT increased between blocks 1 and 2 and then stabilized, whereas a progressive increase was observed in the SART. Regarding intentional mind-wandering, a progressive increase was only observed in the SART. The differential patterns of intentional and unintentional mind-wandering in both tasks suggest that, intentional mind wandering occurs mainly in arousal tasks in which propensity to mind-wander has little impact on task performance. However, unintentional mind-wandering occurs mainly in executive tasks as a result of a failure of cognitive control, which promotes attentional resources to be diverted toward mind-wandering. These results are discussed in the context of the resource-control model of mind-wandering.

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.

Comparing measures of sensitivity for vigilance tasks: Bayesian-estimated and log-linear corrected measures outperform A!

Signal detection analyses often attribute the vigilance decrement to a combination of bias shifts and sensitivity losses. In many vigilance experiments, however, false alarm rates are at or near zero, complicating the analysis of sensitivity. Here, we report Monte Carlo simulations comparing three measures of sensitivity that can be calculated even with extreme hit and false alarm rates: A’, an estimate of the area under the curve that is commonly but mistakenly described as nonparametric; Az calculated using the log-linear correction, a statistic that adjusts individual observers’ data to protect against low false alarm rates; and, 4z estimated using a Bayesian hierarchical procedure, a measure that protects against extreme false alarm rates by sharing information between observers. Results confirm that bias shifts produce spurious changes in A’, and demonstrate that, 4z estimated with either a log-linear correction or through hierarchical Bayesian modeling is more robust against low false alarm rates.

Generalized Deep Learning EEG Models for Cross-Participant and Cross-Task Detection of the Vigilance Decrement in Sustained Attention Tasks

Tasks which require sustained attention over a lengthy period of time have been a focal point of cognitive fatigue research for decades, with these tasks including air traffic control, watchkeeping, baggage inspection, and many others. Recent research into physiological markers of mental fatigue indicate that markers exist which extend across all individuals and all types of vigilance tasks. This suggests that it would be possible to build an EEG model which detects these markers and the subsequent vigilance decrement in any task (i.e., a task-generic model) and in any person (i.e., a cross-participant model). However, thus far, no task-generic EEG cross-participant model has been built or tested. In this research, we explored creation and application of a task-generic EEG cross-participant model for detection of the vigilance decrement in an unseen task and unseen individuals. We utilized three different models to investigate this capability: a multi-layer perceptron neural network (MLPNN) which employed spectral features extracted from the five traditional EEG frequency bands, a temporal convolutional network (TCN), and a TCN autoencoder (TCN-AE), with these two TCN models being time-domain based, i.e., using raw EEG time-series voltage values. The MLPNN and TCN models both achieved accuracy greater than random chance (50%), with the MLPNN performing best with a 7-fold CV balanced accuracy of 64% (95% CI: 0.59, 0.69) and validation accuracies greater than random chance for 9 of the 14 participants. This finding demonstrates that it is possible to classify a vigilance decrement using EEG, even with EEG from an unseen individual and unseen task.

Vigilance in Cardiac Telemetry Monitoring: Performance Outcomes and Effects on Operators’ Cognitive and Affective States

Healthcare represents an industry where any errors have the potential to lead to significant consequences such as unintended patient injury or death. Teams of healthcare professionals work together to complete patient care activities across several hospital environments including operating rooms, trauma centers, and care floors. While most healthcare tasks are patient-facing, some tasks such as cardiac telemetry monitoring occur without much patient interaction. Even so, cardiac telemetry monitoring represents a task where errors result in patient harm or death. The purpose of this study was to investigate the vigilance decrement using a healthcare visual search task based on cardiac telemetry monitoring. Results of this study were viewed through the lens of resource theory of vigilance. Findings from this study include a decrease in correct detections and false alarms with increased workload which supports previous vigilance decrement research as well providing directions for future research.

Examining Methods for Combining Speed and Accuracy in a Go/No-Go Vigilance Task

In many human performance tasks, researchers assess performance by measuring both accuracy and response time. A number of theoretical and practical approaches have been proposed to obtain a single performance value that combines these measures, with varying degrees of success. In this report, we examine data from a common paradigm used in applied human factors assessment: a go/no-go vigilance task (Smith et al., 2019). We examined whether 12 different measures of performance were sensitive to the vigilance decrement induced by the design, and also examined how the different measures were correlated. Results suggest that most combined measures were slight improvements over accuracy or response time alone, with the most sensitive and representative result coming from the Linear Ballistic Accumulator model. Practical lessons for applying these measures are discussed.