Event Abstract Back to Event Optimal inference of sameness and difference Christopher Summerfield1*, Vincent De Gardelle2, Sophie Avery1 and Steven Buckingham1 1 University of Oxford, Department of Experimental Psychology, United Kingdom 2 Laboratoire Psychologie de la Perception (CNRS UMR 8158) Université Paris Descartes, France Cognitive scientists have long debated the mechanisms by which observers judge perceptual similarity and difference. One classic finding is that human observers are faster to judge two successively-occurring visual stimuli (reference and probe) to be the same than different, but slower to detect difference on only one dimension (e.g. colour) than two or more dimensions (e.g. colour and shape). This fast-same effect is counterintuitive, because visual similarity can only be verified by an exhaustive search over all relevant features or dimensions, predicting slower decision latencies for same than different responses. A further puzzle is that the effect is sensitive to whether the sameness is defined by a conjunctive or a disjunctive rule – the criterion effect. Researchers have struggled to provide a unified account of perceptual comparison that can accommodate these two phenomena. Here, we show that an ideal observer model in which stimulus features are processed simultaneously can account for both effects. The model predicts decision latencies for humans making both conjunctive and disjunctive perceptual comparison judgments about visual stimuli with both discrete and continuously-varying feature information. The model has a single free parameter, encoding an observer’s prior belief that recently occurring perceptual events will be repeated in the near future. These findings contribute to a growing literature arguing that the human visual system performs perceptual inference in a statistically optimal fashion. Keywords: Bayesian decision theory, ideal observer models, Visual psychophysics Conference: Neural Coding, Decision-Making & Integration in Time, Rauischholzhausen, Germany, 26 Apr - 29 Apr, 2012. Presentation Type: Poster Presentation Topic: Neural Coding, Decision-Making & Integration in Time Citation: Summerfield C, De Gardelle V, Avery S and Buckingham S (2012). Optimal inference of sameness and difference. Front. Neurosci. Conference Abstract: Neural Coding, Decision-Making & Integration in Time. doi: 10.3389/conf.fnins.2012.86.00017 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 12 Jan 2012; Published Online: 16 Jan 2012. * Correspondence: Dr. Christopher Summerfield, University of Oxford, Department of Experimental Psychology, Oxford, United Kingdom, christopher.summerfield@psy.ox.ac.uk Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract Supplemental Data The Authors in Frontiers Christopher Summerfield Vincent De Gardelle Sophie Avery Steven Buckingham Google Christopher Summerfield Vincent De Gardelle Sophie Avery Steven Buckingham Google Scholar Christopher Summerfield Vincent De Gardelle Sophie Avery Steven Buckingham PubMed Christopher Summerfield Vincent De Gardelle Sophie Avery Steven Buckingham Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.