Kevin A. Gluck

Sleep Deprivation and Sustained Attention Performance: Integrating Mathematical and Cognitive Modeling

A long history of research has revealed many neurophysiological changes and concomitant behavioral impacts of sleep deprivation, sleep restriction, and circadian rhythms. Little research, however, has been conducted in the area of computational cognitive modeling to understand the information processing mechanisms through which neurobehavioral factors operate to produce degradations in human performance. Our approach to understanding this relationship is to link predictions of overall cognitive functioning, or alertness, from existing biomathematical models to information processing parameters in a cognitive architecture, leveraging the strengths from each to develop a more comprehensive explanation. The integration of these methodologies is used to account for changes in human performance on a sustained attention task across 88u2003h of total sleep deprivation. The integrated model captures changes due to time awake and circadian rhythms, and it also provides an account for underlying changes in the cognitive processes that give rise to those effects. The results show the potential for developing mechanistic accounts of how fatigue impacts cognition, and they illustrate the increased explanatory power that is possible by combining theoretical insights from multiple methodologies.

Sleep loss and driver performance: Quantitative predictions with zero free parameters

Fatigue has been implicated in an alarming number of motor vehicle accidents, costing billions of dollars and thousands of lives. Unfortunately, the ability to predict performance impairments in complex task domains like driving is limited by a gap in our understanding of the explanatory mechanisms. In this paper, we describe an attempt to generate a priori predictions of degradations in driver performance due to sleep deprivation. We accomplish this by integrating an existing account of the effects of sleep loss and circadian rhythms on sustained attention performance with a validated model of driver behavior. The predicted results account for published qualitative trends for driving across multiple days of restricted sleep and total sleep deprivation. The quantitative results show that the models performance is worse at baseline and degrades less severely than human driving, and expose some critical areas for future research. Overall, the results illustrate the potential value of model reuse and integration for improving our understanding of important psychological phenomena and for making useful predictions of performance in applied, naturalistic task contexts.

A computational model of spatial visualization capacity.

Visualizing spatial material is a cornerstone of human problem solving, but human visualization capacity is sharply limited. To investigate the sources of this limit, we developed a new task to measure visualization accuracy for verbally-described spatial paths (similar to street directions), and implemented a computational process model to perform it. In this model, developed within the Adaptive Control of Thought-Rational (ACT-R) architecture, visualization capacity is limited by three mechanisms. Two of these (associative interference and decay) are longstanding characteristics of ACT-Rs declarative memory. A third (spatial interference) is a new mechanism motivated by spatial proximity effects in our data. We tested the model in two experiments, one with parameter-value fitting, and a replication without further fitting. Correspondence between model and data was close in both experiments, suggesting that the model may be useful for understanding why visualizing new, complex spatial material is so difficult.

Cognitive Architectures for Human Factors in Aviation

There has been a great deal of investment and resultant progress in the development and evaluation of, improvements to, and compar- isons of cognitive architectures over the last several decades. Not all — however, certainly the majority — of that work has taken place since the publication of Weiner and Nagels (1988) fi rst volume on Human Factors in Aviation, so it is understandable both that there was no mention of computational cognitive modeling or cognitive architectures in that fi rst edition, and also that the editors of the second edition are interested in expanding coverage of the text to include this relevant development in the scientifi c study of human performance and learning. The overarching interest and motivation for the existence of the avia- tion human factors community is improving the operational safety of current and future aviation systems. The people serving in the roles of pilots, navigators, maintainers, controllers, or other user-operator positions in all aviation-related sociotechnical systems are both enabled and constrained by their cognitive architectures. By improv- ing our understanding of the human cognitive architecture, we improve our understanding of an important component of the larger

Broader Bandwidth in Student Modeling: What if ITS were EyeTS?

The ability of an ITS to develop an accurate student model is inherently limited by the bandwidth of information available. We have completed an exploratory research project showing that eye movement data provide a means of broadening this bandwidth. This paper describes three examples in which more information about cognitive process is available from having access to a students eye movements than is available simply from key presses and mouse clicks.

Diminished access to declarative knowledge with sleep deprivation

Inadequate sleep affects cognitive functioning, with often subtle and occasionally catastrophic personal and societal consequences. Unfortunately, this topic has received little attention in the cognitive modeling literature, despite the potential payoff. In this paper, we provide evidence regarding the impact of sleep deprivation on a particular component of cognitive performance, the ability to access and use declarative knowledge. Every 2h throughout an extended period of sleep deprivation, participants completed 50 trials of a serial addition/subtraction task requiring knowledge of single-digit arithmetic facts. Over the course of 88h awake, response times increased while accuracy declined. A computational model accounts for the degradation in performance through a reduction in the activation of declarative knowledge. This knowledge is required for successful completion of the serial addition/subtraction task, but access to the declarative knowledge is impaired as sleep deprivation increases and alertness declines. Importantly, the mechanism provides a generalizable quantitative account relevant to other tasks and contexts. It also provides a process-level understanding of how cognitive performance declines with increasing levels of sleep loss.

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

Objective: The effects of fatigue on multiple-task performance were explored through computational cognitive modeling. Background: Fatigue typically has a negative impact on human performance. Biomathematical models exist that characterize the dynamics of human alertness, but the link between alertness and in situ performance on specific tasks is tenuous. Cognitive architectures offer a principled means of establishing that link. Method: We implemented mechanisms for fatigue, which produce microlapses in cognitive processing, into an existing model, adaptive control of thought—rational, and validated the performance predictions with Bratzke, Rolke, Ulrich, and Peters data on fatigue and multiple-task performance. Results: The microlapse model replicated the human performance results very well with zero free parameters, although the fit was improved when we allowed two individual differences parameters to vary. Conclusion: Increased frequency of microlapses as a result of fatigue provides a parsimonious explanation for the impact of fatigue on dual-task performance and is consistent with previous research. Application: Our results illustrate how using biomathematical models of fatigue in conjunction with a cognitive architecture can result in accurate predictions of the effects of fatigue on dual-task performance. Extending and generalizing this capability has potential utility in any safety-critical domain in which fatigue may affect performance.

Mechanisms for Robust Cognition

To function well in an unpredictable environment using unreliable components, a system must have a high degree of robustness. Robustness is fundamental to biological systems and is an objective in the design of engineered systems such as airplane engines and buildings. Cognitive systems, like biological and engineered systems, exist within variable environments. This raises the question, how do cognitive systems achieve similarly high degrees of robustness? The aim of this study was to identify a set of mechanisms that enhance robustness in cognitive systems. We identify three mechanisms that enhance robustness in biological and engineered systems: system control, redundancy, and adaptability. After surveying the psychological literature for evidence of these mechanisms, we provide simulations illustrating how each contributes to robust cognition in a different psychological domain: psychomotor vigilance, semantic memory, and strategy selection. These simulations highlight features of a mathematical approach for quantifying robustness, and they provide concrete examples of mechanisms for robust cognition.

SAwSu: An Integrated Model of Associative and Reinforcement Learning

Successfully explaining and replicating the complexity and generality of human and animal learning will require the integration of a variety of learning mechanisms. Here, we introduce a computational model which integrates associative learning (AL) and reinforcement learning (RL). We contrast the integrated model with standalone AL and RL models in three simulation studies. First, a synthetic grid-navigation task is employed to highlight performance advantages for the integrated model in an environment where the reward structure is both diverse and dynamic. The second and third simulations contrast the performances of the three models in behavioral experiments, demonstrating advantages for the integrated model in accounting for behavioral data.

Simultaneous performance exploration and optimized search with volunteer computing

Volunteer computing is a powerful platform for solving complex scientific problems. MindModeling@Home is a volunteer computing project available to the cognitive modeling community for conducting research to better understand the human mind. We are interested in optimizing search processes on volunteer resources, yet we are also interested in exploring and understanding changes in model performance across interacting, non-linear mechanisms and parameter spaces. To support both of these goals, we have developed a stochastic optimization approach and integrated it with MindModeling@Home. We tested this approach with a cognitive model on a sample parameter space, demonstrating significant decreases in computational resource utilization and search runtime, while also providing useful visual representations of performance surfaces. Future work will focus on scaling the technique to more volunteers and larger parameter spaces, as well as optimizing the performance of the search algorithm in regards to the challenges inherent with volunteer computing.