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 nearoptimal inference extends to abstract relations.
Original language | English (US) |
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Pages (from-to) | 3178-3183 |
Number of pages | 6 |
Journal | Proceedings of the National Academy of Sciences of the United States of America |
Volume | 109 |
Issue number | 8 |
DOIs | |
State | Published - Feb 21 2012 |
Keywords
- Bayesian inference
- Decision making
- Ideal observer
- Vision
ASJC Scopus subject areas
- General