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Steven Gross, Chaz Firestone; Prior repulsion: "Anti-Bayesian" updating in visual cognition. Journal of Vision 2018;18(10):12. doi: https://doi.org/10.1167/18.10.12.
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© ARVO (1962-2015); The Authors (2016-present)
How do prior assumptions about uncertain data inform our inferences about those data? Increasingly, such inferences are thought to work in the mind the way they should work in principle — with our interpretations of uncertain evidence being nudged towards our prior hypotheses in a "rational" manner approximating Bayesian inference. By contrast, here we explore a class of phenomena that appear to defy such normative principles of inference: Whereas inferences about new data are typically attracted toward prior expectations, we demonstrate how inferences may also be repelled away from prior expectations. In seven experiments, subjects briefly saw arrays of two spatially intermixed sets of objects (e.g. several dozen squares and circles). Over the course of the session, subjects learned that one set was typically more numerous than the other — for example, that there are typically more squares than circles. Surprisingly, upon forming the expectation that they would continue to see more squares, subjects who were then shown an equal number of squares and circles (such that it was unclear exactly which had more) judged the circles to be more numerous, seemingly adjusting their inferences away from their prior hypothesis about what they would see. Six follow-up experiments show how this effect is not explained by low-level sensory adaptation (occurring even when various sensory dimensions are equated), generalizes to many kinds of stimuli (including colors, and configurally-defined letters), and is robust to different measures (not only forced-choice ["which has more?"] but also precise enumeration ["how many are there?"]). We discuss how this "expectation contrast" effect is a genuine case of adjusting "away" from our priors, in seeming defiance of normative principles of inference. We also point to a broader class of phenomena that may behave in this way, and explore their consequences for Bayesian models of perception and cognition.
Meeting abstract presented at VSS 2018
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