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Preprint VersionCC BY-NC V4.0, Open Access
Preprint
Low-level features predict perceived similarity for naturalistic images
bioRxiv, Vol.19 August 2024
Cold Spring Harbor Laboratory Press
2024
Abstract
The mechanisms by which humans perceptually organise 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 standard patch, then required to select which of two subsequently presented patches came from the same scene as the standard (2AFC). In Experiment 1, participants were consistently above chance when performing such association judgements. Additionally, participants’ responses were well-predicted by a generalised linear multilevel model (GLMM) employing predictors based on low-level feature similarity metrics (specifically, pixel-wise luminance and phase-invariant structure correlations). In Experiment 2, participants were presented with thresholded image regions, or regions reduced to only their edge content. Their performance was significantly poorer when they viewed unaltered image regions. Nonetheless, the model still correlated well with participants’ judgments. Our findings suggest that image region associations can be reduced to low-level feature correlations, providing evidence for the contribution of such basic features to judgements made on complex visual stimuli.
Details
- Title
- Low-level features predict perceived similarity for naturalistic images
- Authors
- Emily J A-Izzeddin (Corresponding Author) - The University of QueenslandThomas S A Wallis - TU DarmstadtJason B Mattingley - The University of QueenslandWill Harrison - University of the Sunshine Coast, Queensland, School of Health - Psychology
- Publication details
- bioRxiv, Vol.19 August 2024
- Publisher
- Cold Spring Harbor Laboratory Press
- Date published
- 2024
- DOI
- 10.1101/2024.08.15.607867
- ISSN
- 2692-8205
- Copyright note
- The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
- Organisation Unit
- Healthy Ageing Research Cluster; School of Health - Psychology
- Language
- English
- Record Identifier
- 991068898202621
- Output Type
- Preprint
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