2012
DOI: 10.1073/pnas.1108790109
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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 … Show more

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Cited by 37 publications
(32 citation statements)
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“…Despite its simplicity, the task contains both ambiguity and variable sensory uncertainty-essential elements of natural forms of categorization. Evidence that organisms possess and use trial-to-trial knowledge of sensory uncertainty is abundant in perception (18,36,37), but rare in tasks with nontrivial stimulus categories (38,39). It might be worth studying the effects of sensory uncertainty on decision boundaries in more complex or more cognitive forms of categorization.…”
Section: Discussionmentioning
confidence: 99%
“…Despite its simplicity, the task contains both ambiguity and variable sensory uncertainty-essential elements of natural forms of categorization. Evidence that organisms possess and use trial-to-trial knowledge of sensory uncertainty is abundant in perception (18,36,37), but rare in tasks with nontrivial stimulus categories (38,39). It might be worth studying the effects of sensory uncertainty on decision boundaries in more complex or more cognitive forms of categorization.…”
Section: Discussionmentioning
confidence: 99%
“…14]. Other studies construct Bayesian models of how subjects estimate the structure of the world [1518]. A thriving community tries to relate actual behavior to predictions of optimal Bayesian behavior.…”
Section: Assuming That the Brain Solves Problems Close To The Bayesiamentioning
confidence: 99%
“…These models often focus on various decision rules that can be applied to the measurements. By contrast, the measurements themselves are usually modeled in a rather stereotypical fashion namely, as independent and normally distributed (e.g., Peterson, Birdsall, & Fox, 1954; Nolte & Jaarsma, 1967; Pelli, 1985; Graham, Kramer, & Yager, 1987; Palmer, Ames, & Lindsey, 1993; Baldassi & Burr, 2000; Baldassi & Verghese, 2002; van den Berg, Vogel, Josić, & Ma, 2012; Ma, Navalpakkam, Beck, van den Berg, & Pouget, 2011; Mazyar, van den Berg, & Ma, 2012). Both the assumption of independence and the assumption of gaussianity can be questioned.…”
Section: Introductionmentioning
confidence: 99%