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

Abstract

Computational models have become common tools in psychology. They provide quantitative instantiations of theories that seek to explain the functioning of the human mind. In this paper, we focus on identifying deep theoretical similarities between two very different models. Both models are concerned with how fatigue from sleep loss impacts cognitive processing. The first is based on the diffusion model and posits that fatigue decreases the drift rate of the diffusion process. The second is based on the ACT-R cognitive architecture and posits that fatigue decreases the utility of candidate actions leading to microlapses in cognitive processing. A biomathematical model of fatigue is used to control drift rate in the first account and utility in the second. We investigated the predicted response time distributions of these two integrated computational cognitive models for performance on a psychomotor vigilance test under conditions of total sleep deprivation, simulated shift work, and sustained sleep restriction. The models generated equivalent predictions of response time distributions with excellent goodness-of-fit to the human data. More importantly, although the accounts involve different modeling approaches and levels of abstraction, they represent the effects of fatigue in a functionally equivalent way: in both, fatigue decreases the signal-to-noise ratio in decision processes and decreases response inhibition. This convergence suggests that sleep loss impairs psychomotor vigilance performance through degradation of the quality of cognitive processing, which provides a foundation for systematic investigation of the effects of sleep loss on other aspects of cognition. Our findings illustrate the value of treating different modeling formalisms as vehicles for discovery.

Further Evidence That Sleep Deprivation Effects and the Vigilance Decrement Are Functionally Equivalent: Comment on Altmann (2018)

“…Clock Task, whereas other research has provided evidence for the theory in sleep loss and circadian rhythms (Walsh et al, 2017). We would find it unsurprising if similar dynamics were at play in human performance in Altmann and Gray (2008), where participants were asked to complete nearly 3,000 trials of an attention-demanding laboratory task in an experiment lasting 45 minutes.…”

“…This is based on evidence that exponential functions capture the general shape of the decline observed in vigilance tasks (Giambra & Quilter, 1987). Analogously, we use biomathematical models of alertness (e.g., McCauley et al, 2013) to set utility and threshold values for each session in the sleep model (Walsh, Gunzelmann, & Van Dongen, 2017). They capture the shape of the decrements observed in tasks like the PVT resulting from the interacting effects of sleep loss and circadian rhythms across sessions (e.g., Van Dongen, 2004).…”

“…They capture the shape of the decrements observed in tasks like the PVT resulting from the interacting effects of sleep loss and circadian rhythms across sessions (e.g., Van Dongen, 2004). Walsh et al (2017) did not model intrasession changes in PVT performance, in part because PVT sessions tend to be shorter in sleep research (10 min) than what we used (35 min; Veksler & Gunzelmann, 2018). However, we have explored the conjunction of sleep loss and time on task in the PVT in the past (Gunzelmann, Moore, Gluck, Van Dongen, & Dinges, 2011).…”

“…When a microlapse occurs, the probability of a subsequent microlapse increases at a rate governed by a parameter we refer to as FP dec . Critically, the value for FP dec is identical in both the sleep and vigilance models (0.98; Walsh et al, 2017;Veksler & Gunzelmann, 2018), which is held constant across levels of sleep loss in Walsh et al (2017) and across time on task in Veksler and Gunzelmann (2018). In addition, in both models the probability of a microlapse reverts to the baseline value at the start of each trial.…”

“…1). Far from being vestigial, this component of the model is critical in accounting for the time on task effect in the PVT (Veksler & Gunzelmann, 2018), just as it is necessary to capture the deleterious effects of sleep loss (Walsh et al, 2017).…”