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David Knill, Oh-Sang Kwon; Learning category contingent speed priors for object interception. Journal of Vision 2013;13(9):775. doi: 10.1167/13.9.775.
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© ARVO (1962-2015); The Authors (2016-present)
Introduction: When planning movements to intercept a moving target, humans use adaptive statistical models of speed distributions and temporal correlations to estimate target velocity. The current experiment tested whether and how subjects learn category-contingent statistical models of object speed when presented with targets with mixed distributions. Methods: Subjects viewed target objects that moved briefly (200 – 300 msec.) in a virtual display before disappearing behind a variable-length occluder. Their task was to hit an "impact-zone" drawn on the occluder when the object passed behind it. In a first ("hard") experiment, two types of targets were randomly interleaved from trial-to-trial - red-square targets had speeds drawn from a distribution with a small variance while green-circular targets had speeds drawn from a distribution with the same mean but with a large variance (or vice-versa for half of the subjects). In a second ("easy") experiment, the means of the distributions also differed significantly. Results: In both experiments, subjects showed a significantly larger bias to the mean for the low-variance category than the high, had a marginally significant greater bias to the previous stimulus when it was from the same category than when it was from a different category and adapted their timing behavior to the feedback on the previous trial significantly more when the previous stimulus was from the same category. Despite these differences, their behavior on a trial was still significantly affected by both the speed of the target and the hitting error on the previous trial even when it was from a different category. Conclusions: Subjects partially learn separate priors for different categories, but retain temporal biasing affects from stimuli across categories. This suggests a model in which observers use categorization cues probabilistically, rather than as absolute cues to category membership for purposes of imposing prior models on speed estimates.
Meeting abstract presented at VSS 2013
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