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The influence of natural image statistics on upright orientation judgements.

Authors :
A-Izzeddin EJ
Mattingley JB
Harrison WJ
Source :
Cognition [Cognition] 2024 Jan; Vol. 242, pp. 105631. Date of Electronic Publication: 2023 Oct 10.
Publication Year :
2024

Abstract

Humans have well-documented priors for many features present in nature that guide visual perception. Despite being putatively grounded in the statistical regularities of the environment, scene priors are frequently violated due to the inherent variability of visual features from one scene to the next. However, these repeated violations do not appreciably challenge visuo-cognitive function, necessitating the broad use of priors in conjunction with context-specific information. We investigated the trade-off between participants' internal expectations formed from both longer-term priors and those formed from immediate contextual information using a perceptual inference task and naturalistic stimuli. Notably, our task required participants to make perceptual inferences about naturalistic images using their own internal criteria, rather than making comparative judgements. Nonetheless, we show that observers' performance is well approximated by a model that makes inferences using a prior for low-level image statistics, aggregated over many images. We further show that the dependence on this prior is rapidly re-weighted against contextual information, even when misleading. Our results therefore provide insight into how apparent high-level interpretations of scene appearances follow from the most basic of perceptual processes, which are grounded in the statistics of natural images.<br />Competing Interests: Declaration of Competing Interest The authors have no conflicts of interest to declare.<br /> (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)

Details

Language :
English
ISSN :
1873-7838
Volume :
242
Database :
MEDLINE
Journal :
Cognition
Publication Type :
Academic Journal
Accession number :
37820487
Full Text :
https://doi.org/10.1016/j.cognition.2023.105631