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A cognitive modeling account of simultaneous learning and fatigue effects
Current understanding of sources of fatigue and of how fatigue affects performance in prolonged cognitive tasks is limited. We have observed that participants improve in response time but decrease in accuracy after extended repetitive work in a data entry task. We attributed the increase in errors to accumulating fatigue and the reduction in response time to learning. The concurrent effects of fatigue and learning seem intuitively reasonable but have not been explained computationally. This paper presents a cognitive computational model of these effects. The model, developed using the ACT-R cognitive architecture (1 and 3), accounts for learning and fatigue effects through a time-dependent modification of architectural parameters. The model is tested against human data from two independent experiments. Best fit to human accuracy and total response time was found from a modulation of both cognitive and arousal processes. Implications for training and skill acquisition research are discussed.