Although the phenomenon of cognitive fatigue during sustained task performance is well documented, surprisingly little is known about its temporal dynamics. This chapter focuses on the interaction of cognitive fatigue across time on task with the influence of sleep loss (time awake) and circadian rhythm (time of day). In the section that follows, we discuss what is known about this issue from the published literature. In the next section, we present new data showing that the impact of sleep loss on the time-on-task effect generalizes from acute total sleep deprivation to the more commonly experienced conditions of chronic sleep restriction.In the section thereafter, we present additional new data from studies of repeated sleep deprivation, which reveal poor replicability of the slope of performance degradation across time on task. This finding points to considerable influence of an as yet unknown stochastic process. We conclude the chapter by introducing a theoretical account for this stochastic process, which we hypothesize involves the presence of a use-dependent sleep state in local neuronal assemblies involved in task performance. We also propose a model of the underlying mechanisms, which suggests that cognitive fatigue from sustained task performance and fatigue from sleep loss and circadian rhythm may share neurobiological pathways. Time Awake, Time of Day, and Time on TaskFrom a perspective of sleep-wake regulation, at least three key neurobiological processes cause systematic changes in cognitive function over time: a homeostatic process producing a progressive sleep drive over time awake, a circadian process We thank David Dinges and David Rector for contributing to the concepts presented in this chapter.

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Time on task and time awake are two important influences on human cognitive performance. Both extended periods of wakefulness and extended periods of effort on a single task lead to performance declines (e.g.

Study Objectives A cognitive throughput task known as the Digit Symbol Substitution Test (DSST) (or Symbol Digit Modalities Test) has been used as an assay of general cognitive slowing during sleep deprivation. Here, the effects of total sleep deprivation (TSD) on specific cognitive processes involved in DSST performance, including visual search, spatial memory, paired-associate learning, and motor response, were investigated through targeted task manipulations. Methods A total of 12 DSST variants, designed to manipulate the use of specific cognitive processes, were implemented in two laboratory-based TSD studies with N = 59 and N = 26 subjects, respectively. In each study, the Psychomotor Vigilance Test (PVT) was administered alongside the DSST variants. Results TSD reduced cognitive throughput on all DSST variants, with response time distributions exhibiting rightward skewing. All DSST variants showed practice effects, which were however minimized by inclusion of a pause between trials. Importantly, TSD-induced impairment on the DSST variants was not uniform, with a principal component analysis revealing three factors. Diffusion model decomposition of cognitive processes revealed that inter-individual differences during TSD on a two-alternative forced choice DSST variant were different from those on the PVT. Conclusions While reduced cognitive throughput has been interpreted to reflect general cognitive slowing, such TSD-induced impairment appears to reflect cognitive instability, like on the PVT, rather than general slowing. Further, comparisons between task variants revealed not one, but three distinct underlying processes impacted by sleep deprivation. Moreover, the practice effect on the task was found to be independent of the TSD effect and minimized by a task pacing manipulation.

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Workplace safety and productivity will be enhanced considerably with the development and application of individualized sleep and performance prediction models. These are models that predict an individual's operational performance based on his/her unique sleep schedule, individual sleep requirements, and individual pattern of responses to sleep loss across a variety of cognitive performance domains. Progress in the individualization of such models will occur as the result of integrated efforts based on (a) an expanding understanding of the relevant physiological processes underlying the sleep/circadian/performance interactions as well as (b) novel empirical and statistical approaches. In the present paper, an overview is presented of the state of the art of the individualization of sleep and performance models with sections on current efforts in model integration, the application of Bayesian forecasting techniques to the problem of model individualization, construction of Bayesian confidence intervals for predicted performance, and the problem of generalizability of individualized model predictions -i.e., the problem of using models constructed with performance data from one cognitive domain to predict performance in another cognitive domain. Success in model individualization will ultimately be facilitated by concerted, coordinated efforts involving multiple scientific entities and stakeholders.

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