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Optimal inference of sameness.


ABSTRACT: Deciding whether a set of objects are the same or different is a cornerstone of perception and cognition. Surprisingly, no principled quantitative model of sameness judgment exists. We tested whether human sameness judgment under sensory noise can be modeled as a form of probabilistically optimal inference. An optimal observer would compare the reliability-weighted variance of the sensory measurements with a set size-dependent criterion. We conducted two experiments, in which we varied set size and individual stimulus reliabilities. We found that the optimal-observer model accurately describes human behavior, outperforms plausible alternatives in a rigorous model comparison, and accounts for three key findings in the animal cognition literature. Our results provide a normative footing for the study of sameness judgment and indicate that the notion of perception as near-optimal inference extends to abstract relations.

SUBMITTER: van den Berg R 

PROVIDER: S-EPMC3286990 | biostudies-literature | 2012 Feb

REPOSITORIES: biostudies-literature

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Optimal inference of sameness.

van den Berg Ronald R   Vogel Michael M   Josic Kresimir K   Ma Wei Ji WJ  

Proceedings of the National Academy of Sciences of the United States of America 20120206 8


Deciding whether a set of objects are the same or different is a cornerstone of perception and cognition. Surprisingly, no principled quantitative model of sameness judgment exists. We tested whether human sameness judgment under sensory noise can be modeled as a form of probabilistically optimal inference. An optimal observer would compare the reliability-weighted variance of the sensory measurements with a set size-dependent criterion. We conducted two experiments, in which we varied set size  ...[more]

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