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C. K. Abbey, M. P. Eckstein, S. S. Shimozaki; The efficiency of perceptual learning in a visual detection task. Journal of Vision 2001;1(3):28. doi: https://doi.org/10.1167/1.3.28.
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© ARVO (1962-2015); The Authors (2016-present)
Purpose: In the context of a visual task, the notion of perceptual learning is often used to explain sequential improvements in task performance. In this work, we use the ideal observer as a benchmark for measuring the efficiency of perceptual learning in humans. In the task we investigate, the ideal observer “learns” by exploiting redundant information in earlier trials to optimally weight feature responses. The goal is to understand how stimulus presentation and feedback influence the efficiency of human perceptual learning. Methods: We conducted a series of forced-choice localization experiments using noisy images in which a target appeared randomly in one of eight locations. Each experiment consisted of sets of four “learning” trials. At the beginning of a set of learning trials, one of four possible signals was chosen at random and used throughout the learning set. We investigated the effect of different stimulus presentation times (unlimited vs. 200ms) and different feedback methods (location − 200ms, stimulus + location − 200ms, stimulus + location − unlimited) on sequential task performance. We compared human observer performance to the performance of the Bayesian ideal observer in this task. The ideal observer optimally updates its prior information about the signal profile throughout the learning trials. Results: In all cases, our observers showed improved performance going from the first learning trial to the fourth. This indicates that learning did indeed occur in our observers. However, efficiency with respect to the ideal observer dropped after the first learning trial, except in the condition with unlimited presentation time. Conclusions: In general, efficiency with respect to the ideal observer is relatively constant across learning trials. However, for short stimulus presentation times, efficiency decreases after the first trial, indicating a slower rate of learning.
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