Using Computational Cognitive Modeling to Predict Dual-Task Performance With Sleep Deprivation

Gunzelmann, Glenn; Byrne, Michael D.; Gluck, Kevin A.; Moore, L. Richard

doi:10.1177/0018720809334592

Cited by 23 publications

(27 citation statements)

References 25 publications

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“…A comprehensive listing of ACT-R models of behavior would be prohibitive, but would include empirically grounded accounts of visual attention (Reifers, Schenck, & Ritter, 2005), time perception (Taatgen et al, 2007), working and long term memory (Anderson, Reder, & Lebiere, 1996;Altmann, 2000), skill acquisition and selection (Taatgen & Lee, 2003;Fu & Anderson, 2006), multi-tasking and task switching (Salvucci & Taatgen, 2008), language learning (Taatgen & Dijkstra, 2003), interruptions and errors (Trafton, Altmann, & Ratwani, 2011;Trafton, Jacobs, & Harrison, 2012), and work-load and fatigue (Gunzelmann, Byrne, Gluck, & Moore, 2009). …”

Section: Module Interactionmentioning

confidence: 99%

ACT-R/E: An Embodied Cognitive Architecture for Human-Robot Interaction

Trafton

Hiatt

Harrison

et al. 2013

JHRI

151

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We present ACT-R/E (Adaptive Character of Thought-Rational / Embodied), a cognitive architecture for human-robot interaction. Our reason for using ACT-R/E is two-fold. First, ACT-R/E enables researchers to build good embodied models of people to understand how and why people think the way they do. Then, we leverage that knowledge of people by using it to predict what a person will do in different situations; e.g., that a person may forget something and may need to be reminded or that a person cannot see everything the robot sees. We also discuss methods of how to evaluate a cognitive architecture and show numerous, empirically validated examples of ACT-R/E models.

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Section: Module Interactionmentioning

confidence: 99%

ACT-R/E: An Embodied Cognitive Architecture for Human-Robot Interaction

Trafton

Hiatt

Harrison

et al. 2013

JHRI

151

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“…This integrated account captured changes in the complete PVT response time distribution, including false starts, alert responses, lapses, and sleep attacks (Gunzelmann et al, 2009a). The same account made realistic predictions of the effects of fatigue on dual-task performance (e.g., Gunzelmann, Byrne, Gluck, & Moore, 2009) and lane deviation in driving (Gunzelmann, Moore, Salvucci, & Gluck, 2011). …”

Section: Introductionmentioning

confidence: 95%

“…Therefore, to better understand the effects of fatigue from sleep loss, it is necessary to consider the range of cognitive processes evoked during task performance (Whitney & Hinson, 2010). One way to do so is through the use of computational cognitive models (Gunzelmann, Gross, Gluck, & Dinges, 2009; Gunzelmann et al, 2015; Jackson et al, 2013). …”

Section: Introductionmentioning

confidence: 99%

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

2017

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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.

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“…The awareness of the increased accident risk in the transport sector, has led to development and use of computerised mathematical process models, dual-models and 3-process models for predicting level of fatigue and its effect on cognitive functions, e.g. alertness/performance in daily life, for navy, airline and railway applications [9][10][11] as well as multimodal fatigue measure assessment for measuring early onset of drivers fatigue [12], but so far these have not been implemented in the fishing industry.…”

Section: Introductionmentioning

confidence: 99%