Attention, Automaticity and Priority Learning
CARNEGIE-MELLON UNIV PITTSBURGH PA ARTIFICIAL INTELLIGENCE AND PSYCHOLOGY PROJECT
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It is widely held that there is a distinction between attentive and automatic cognitive processing. In research on attention using visual search tasks, the detection performance of human subjects in consistent mapping paradigms is generally regarded as indicating a shift, with practice, from serial, attentional, controlled processing to a parallel, automatic processing, while detection performance in varied mapping paradigms is taken to indicate that processing remains under attentional control. This paper proposes a priority learning mechanism to model the effects of practice and the development of automaticity, in visual search tasks. A connectionist simulation model implements this learning algorithm. Five prominent features of visual search practice effects are simulated. These are 1 in consistent mapping tasks, practice reduces processing time, particularly the slope of reaction times as a function of the number of comparisons 2 in varied mapping tasks, there is no change in the slope of the reaction time function 3 both the consistent and varied effects can occur concurrently 4 reversing the target and distractor sets produces strong interference effects and 5 the benefits of practice are a function of the degree of consistency.