Abstract not found

Abstract not found

Computational models have emerged as a powerful tool for studying the complex task of driving, allowing researchers to simulate driver behavior and explore the parameters and constraints of this behavior. In this paper we investigate the advantages of developing rigorous computational models of driver behavior in cognitive architectures — computational frameworks with underlying psychological theories that incorporate basic properties and limitations of the human system. In particular, we describe an integrated driver model developed in the ACT-R cognitive architecture and demonstrate how this model accounts for the steering profiles, lateral-position profiles, and gaze distributions of human drivers during lane keeping, curve negotiation, and lane changing. The model has implications both for theoretical accounts of complex dynamic tasks in the context of cognitive architectures and for practical applications in predicting and recognizing driver behavior and distraction.

This research adopted a model of goal activation to study the mechanisms underlying interrupted task performance. The effects of interruption timing, type of interruption, and age on task time and primary task resumption time were explored under conditions in which attention was switched back and forth between two tasks, much as when drivers shift attention between attending to the road and to an in-vehicle task. The timing of interruptions had a significant impact on task resumption times, indicating that the most costly time to interrupt task performance is during the middle of a task. However, this effect was overshadowed by age-related performance decrements for older participants. Interruptions that prevented strategic rehearsal of goals resulted in longer resumption times as compared with interruptions that allowed rehearsal. Actual or potential applications of this research include the design of in-vehicle device user interfaces, the timing of invehicle messages, and current metrics for assessing driver distraction.

Emerging parallel processing and increased flexibility during the acquisition of cognitive skills form a combination that is hard to reconcile with rule-based models that often produce brittle behavior. Rule-based models can exhibit these properties by adhering to 2 principles: that the model gradually learns task-specific rules from instructions and experience, and that bottom-up processing is used whenever possible. In a model of learning perfect time-sharing in dual tasks (Schumacher et al., 2001), speedup learning and bottom-up activation of instructions can explain parallel behavior. In a model of a

We report an investigation into the processes involved in a common graph reading task using two types of Cartesian graph. We describe an experiment and eye movement study, the results of which show that optimal scan paths assumed in the task analysis approximate the detailed sequences of saccades made by individuals. The research demonstrates the computational inequivalence of two sets of informationally equivalent graphs and illustrates how the computational advantages of a representation outweigh factors such as user unfamiliarity. We describe two models using the ACT rational perceptual motor (ACT-R/PM) cognitive architecture, that replicate the pattern of observed response latencies and the complex scan paths revealed by the eye movement study. Finally, we outline three guidelines for designers of visual displays: Designers should (a) consider how different quantities are encoded within any chosen representational format (b) consider the full range of alternative varieties of a given task, and (c) balance the cost of familiarization with the computational advantages of less familiar representations. Actual or potential applications of this research include informing the design and selection of appropriate visual displays and illustrating the practice and utility of task analysis, eye tracking, and cognitive modeling for understanding interactive tasks with external representations.

... In this article I define a taxonomy and three dimensions of simulated task environments. The dimensions are based on viewing simulated task environments from the perspectives of the researcher, the task, and the participants. Research on complex systems is inherently complex. It is my hope that the terms and distinctions introduced in this article will further the scientific enterprise by enabling us to spend less time explaining our paradigms and more time communicating our results

The time to resume task goals after an interruption varied depending on the duration and cognitive demand of interruptions, as predicted by the memory for goals model (Altmann & Trafton, 2002). Three experiments using an interleaved tasks interruption paradigm showed that longer and more demanding interruptions led to longer resumption times in a hierarchical, interactive task. The resumption time profile for durations up to one minute supported the role of decay in defining resumption costs, and the interaction between duration and demand supported the importance of goal rehearsal in mitigating decay. These findings supported the memory for goals model, and had practical implications for context where tasks are frequently interleaved such as office settings, driving, emergency rooms, and aircraft cockpits.

Objective: To replicate a successful laboratory slip-class error paradigm and, more importantly, to further understand the underlying causes of errors made in that paradigm. Background: Routine procedural errors are facts of everyday life but have received limited controlled empirical study, despite the sometimes severe consequences associated with such errors. This research concerns one such error, postcompletion error (M. D. Byrne & S. Bovair, 1997), which is a lapse that occurs after the main goal of a task has been satisfied. Method: In the two experiments conducted, participants were trained to criterion on a routine procedural task and were then brought back to the lab for a later session or sessions in which performance on task execution was measured. In the second experiment, a variety of motivational manipulations, retraining, and task redesign were compared. Results: Experiment 1 demonstrated a substantial reduction of error rate generated by a simple design change (alteration of when feedback about goal completion occurred). Furthermore, the reduction in error rate came with no penalty in terms of overall speed of performance. Experiment 2 showed that this more appropriate design is superior to motivationally oriented interventions, retraining, and even midtask redesign. As in Experiment 1, Experiment 2 revealed no speed-accuracy tradeoff. Conclusion: These experiments provide evidence that controlled laboratory studies of slip-class errors can be meaningful and highlight the centrality of cognitive factors (particularly goal structure) in such errors. Application: Potential applications include design of interfaces and their related procedures as well as error-mitigation techniques.

Explicit information-seeking actions are needed to evaluate alternative actions in problem-solving tasks. Information-seeking costs are often traded off against the utility of information. We present three experiments that show how subjects adapt to the cost and information structures of environments in a map-navigation task. We found that subjects often stabilize at suboptimal levels of performance. A Bayesian satisficing model (BSM) is proposed and implemented in the ACT-R architecture to predict information-seeking behavior. The BSM uses a local decision rule and a global Bayesian learning mechanism to decide when to stop seeking information. The model matched the human data well, suggesting that adaptation to cost and information structures can be achieved by a simple local decision rule. The local decision rule, however, often limits exploration of the environment and leads to suboptimal performance. We propose that suboptimal performance is an emergent property of the dynamic interactions between cognition and the environment.