When faced with a new perceptual task such as object discrimination, face recognition, and or visual search humans are often uncertain about which visual properties (i.e., features) are relevant and which are irrelevant to the new task (Eleanor Gibson, 1969, 1991). With practice humans learn to use task-relevant features and ignore irrelevant features and thus improve their performance. We have developed a new paradigm (optimal learning paradigm, OPL) that allows to compare how well humans learn (via attentional weighting optimization) compared to an optimal Bayesian learner. This approach allows to us to identify what are the sources of suboptimality in human perceptual learning.
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