Investigating the temporal dynamics and underlying mechanisms of cognitive fatigue.

Abstract

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.

3-minute smartphone-based and tablet-based psychomotor vigilance tests for the assessment of reduced alertness due to sleep deprivation

“…Data are plotted in 10-ms bins. The dashed vertical lines indicate the lapse thresholds used in the primary analyses for each of the PVT versions amplification of the effect as a function of time awake and time of day (Van Dongen et al, 2011a;Wesensten et al, 2004) and returning to baseline patterns after recovery sleep. Individual differences in performance were comparable across the three devices, such that trait vulnerability to reduced alertness due to sleep deprivation (Van Dongen, Baynard, et al, 2004) was captured by each of the three PVT versions.…”

“…For sleep deprivation, this effect is well captured by counting the number of lapses of attention, conventionally defined as RTs greater than 500 ms (Doran et al, 2001;Dorrian et al, 2005). The number of lapses on the PVT per test session is a sensitive measure of instability in sustained attention due to sleep deprivation Lim & Dinges, 2008;Van Dongen, Belenky, & Krueger, 2011a;Van Dongen, Maislin, Mullington, & Dinges, 2003). Also, the impact of sleep deprivation on PVT lapses shows large, trait-like individual differences (Van Dongen, Baynard, Maislin, & Dinges, 2004).…”

“…All three PVT versions showed a general increase of mean RTs with progressing time on task, which was reset by the rest breaks between the tests and test blocks. For each PVT version, the time-on-task effect was amplified as a function of time awake and time of day (Van Dongen et al, 2011a;Wesensten, Belenky, Thorne, Kautz, & Balkin, 2004). For the 3-min table PVT, the interaction of the test blocks with Means ± SEs for RTs in 1-min bins on the 10-min laptop PVT (top), the 3-min smartphone PVT (middle), and the 3-min tablet PVT (bottom).…”

Computational cognitive modeling of the temporal dynamics of fatigue from sleep loss

“…Based on the concept of local, use-dependent sleep (Krueger et al, 2008), this paradigm postulates that as a consequence of prior use, cortical columns may temporarily fail to process information, effectively reducing functional connectivity and thereby degrading the quality of cognitive processing (Krueger, Huang, Rector, & Buysse, 2013; Van Dongen, Belenky, & Krueger, 2011a). Prior use is a function of time awake and is further modulated by task load (Van Dongen, Belenky, & Krueger, 2011b), which is determined by stimulus density and time on task (i.e., cumulative cognitive processing requirement) and is particularly high in repetitive, attention-demanding tasks such as the PVT. The effects of local sleep on performance depend on the number of functional neuronal circuits available to process information for a given task – that is, level of redundancy, or cognitive capacity – which may vary across tasks and among individuals (Chee & Van Dongen, 2013).…”

Operationalising cognitive fatigability in multiple sclerosis: A Gordian knot that can be cut?

“…Indeed, only a handful Some of the studies in Table 1 refer to an a priori guiding theory, or pre-specified underlying mechanism(s), to understand the construct of cognitive fatigability construct 11,13,15,17 . For instance A good example is, Sandry et al 15 where the authors set out to test cognitive load 16 , cognitive domain 27 , and temporal fatigue hypotheses 28 . More theoretically guided mechanistic work is needed to understand fatigability.…”