Optimal inference of sameness.
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.
Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA.
Ronald
R
van den Berg
Michael
M
Vogel
Kresimir
K
Josic
Wei Ji
WJ
Ma
D000818:Animals; D003071:Cognition / Q000502:physiology*; D006801:Humans; D007600:Judgment; D008954:Models, Biological; D010465:Perception / Q000502:physiology*; D011594:Psychometrics