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Published VersionCC BY V4.0, Open Access
Journal article
Low-level features predict perceived similarity for naturalistic images
Journal of Vision, Vol.25(12), pp.1-20
2025
PMID: 41055419
Abstract
The mechanisms by which humans perceptually organize individual regions of a visual scene to generate a coherent scene representation remain largely unknown. Our perception of statistical regularities has been relatively well-studied in simple stimuli, and explicit computational mechanisms that use low-level image features (e.g., luminance, contrast energy) to explain these perceptions have been described. Here, we investigate to what extent observers can effectively use such low-level information present in isolated naturalistic scene regions to facilitate associations between said regions. Across two experiments, participants were shown an isolated reference patch, then required to select which of two subsequently presented patches came from the same scene as the reference (two-alternative forced choice method). In Experiment 1, participants made their judgments based on unaltered image patches, and were consistently above chance when performing such association judgments. Additionally, participants' responses were well-predicted by a generalized linear multilevel model using predictors based on low-level feature similarity metrics (specifically, pixel-wise luminance and phase-invariant structure correlations). In Experiment 2, participants were presented with unaltered image regions, thresholded image regions, or regions reduced to only their edge content. Performance for thresholded and edge regions was significantly poorer than for unaltered image regions. Nonetheless, the model still correlated well with participants' judgments. Our findings suggest that image region associations can be accounted for using low-level feature correlations, suggesting such basic features are strongly associated with those underlying judgments made for complex visual stimuli.
Details
- Title
- Low-level features predict perceived similarity for naturalistic images
- Authors
- Emily J A-Izzeddin (Corresponding Author) - Justus-Liebig-Universität GießenThomas S A Wallis - Technische Universität DarmstadtJason B Mattingley - The University of QueenslandWilliam J Harrison - University of the Sunshine Coast, Queensland, School of Health - Psychology
- Publication details
- Journal of Vision, Vol.25(12), pp.1-20
- Publisher
- Association for Research in Vision and Ophthalmology
- Date published
- 2025
- DOI
- 10.1167/jov.25.12.11
- ISSN
- 1534-7362
- PMID
- 41055419
- Copyright note
- Copyright 2025 The Authors. This work is licensed under a Creative Commons Attribution 4.0 International License.
- Data Availability
- Behavioral data and GLMM code are available on the Open Science Framework: https://osf.io/ykerd/?view_only=0d3bbfc76768449eb59d3ad011d51979.
- Grants
- Grant note
- E.J.A. was supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)—SFB/TRR 135 (project no. 222641018, project C1). This research was co-funded by Research Cluster “The Adaptive Mind,” funded by the Excellence Program of the Hessian Ministry of Higher Education, Science, Research and the Arts.
- Organisation Unit
- Healthy Ageing Research Cluster; School of Health - Psychology
- Language
- English
- Record Identifier
- 991168126502621
- Output Type
- Journal article
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web Of Science research areas
- Ophthalmology