Human performance in a large range of visual tasks improves with practice. One process by which observers' accuracy increases is due to an improvement in their ability to use task-relevant (signal) information. However, the dynamics of the human neural algorithm mediating this learning process is mostly unknown. We hypothesize that a new experimental paradigm can help elucidate the human learning algorithm as well as identify the human sources of learning inefficiency by allowing comparisons of empirically measured human perceptual learning performance to that of an optimal Bayesian learner and other suboptimal learning models. To achieve this goal we propose four Specific Aims: (1) to develop an experimental paradigm that allows the investigator to compare human perceptual learning to the learning of an optimal Bayesian learner as well as other suboptimal learning models for five specific tasks involving learning about different visual attributes; (2) to develop a battery of diagnostic tests to compare human and model learning; (3) to measure human visual performance psychophysically for the five proposed visual tasks and compare them to model performance using the battery of diagnostic tests; and (4) to use the developed experimental and theoretical framework to evaluate the efficiency of the human use of different modes of feedback on learning. The proposed work will improve our understanding of the human neural algorithms mediating the dynamics of adult perceptual learning and identify different sources of inefficiency in human learning. Finally, the experimental protocols and theoretical framework proposed will provide a novel, powerful and flexible framework that other researchers can use to evaluate normal adult and infant perceptual learning in a variety of tasks and sensory modalities, to assess learning in humans with visual disorders and/or learning disabilities, and could potentially be used in conjunction with cell recording and/or brain imaging. ? ?
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