期刊
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
卷 109, 期 8, 页码 3178-3183出版社
NATL ACAD SCIENCES
DOI: 10.1073/pnas.1108790109
关键词
Bayesian inference; ideal observer; decision making; vision
资金
- National Eye Institute [R01EY020958]
- Netherlands Organisation for Scientific Research
- National Science Foundation [DMS-0817649, DMS-1122094]
- Texas Advanced Research/Technology Program
- Division Of Mathematical Sciences
- Direct For Mathematical & Physical Scien [0817649] Funding Source: National Science Foundation
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.
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