Assessing psychological, neural and AI-based representational geometries across multiple similarity properties
Abstract
Assessing similarity between objects is a fundamental cognitive process, and similarity judgments can assess many different attributes, including objective properties (color, shape) or subjective appraisals (preference, scariness). Here, we investigated how psychological similarity across eight different properties relates to neural representational geometries and artificial models. We first collected large-scale pairwise similarity ratings for 280 objects. Using representational similarity analysis (RSA) and multidimensional scaling, we identified three mental representational spaces: one defined by multiple object features and general similarity, a second by affective evaluations, and a third by color. Combining fMRI and RSA in 80 participants, we found that object feature ratings significantly correlated with representational structures in visual and prefrontal cortices. Unexpectedly, affective ratings also correlated with patterns in visual cortices. Crucially, regional variability strongly shaped these correspondences: visual cortices exhibited high noise ceilings and consistent alignment with behavior, whereas associative regions showed greater across-subjects variability and weaker correlations. Finally, algorithms and neural networks with high classification performance on specific properties strongly correlated with behavioral and fMRI RSA matrices. Together, these results demonstrate that similarity is a multidimensional construct that comprises multiple representational spaces whose neural correspondence is shaped by representational content, regional variability, and modeling methodology.
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BibTeX
@misc{gamba2026assessing,
title = {{Assessing psychological, neural and AI-based representational geometries across multiple similarity properties}},
author = {Johan Alejandro Gamba and Megan A. K. Peters and Vincent Taschereau-Dumouchel and Brian Odegaard},
howpublished = {PsyArXiv},
year = {2026},
month = oct,
doi = {10.31234/osf.io/fz94u_v3},
url = {https://osf.io/preprints/psyarxiv/fz94u_v3/},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Assessing psychological, neural and AI-based representational geometries across multiple similarity properties
AU - Johan Alejandro Gamba
AU - Megan A. K. Peters
AU - Vincent Taschereau-Dumouchel
AU - Brian Odegaard
PY - 2026
DA - 2026/10/07
PB - PsyArXiv
DO - 10.31234/osf.io/fz94u_v3
UR - https://osf.io/preprints/psyarxiv/fz94u_v3/
AB - Assessing similarity between objects is a fundamental cognitive process, and similarity judgments can assess many different attributes, including objective properties (color, shape) or subjective appraisals (preference, scariness). Here, we investigated how psychological similarity across eight different properties relates to neural representational geometries and artificial models. We first collected large-scale pairwise similarity ratings for 280 objects. Using representational similarity analysis (RSA) and multidimensional scaling, we identified three mental representational spaces: one defined by multiple object features and general similarity, a second by affective evaluations, and a third by color. Combining fMRI and RSA in 80 participants, we found that object feature ratings significantly correlated with representational structures in visual and prefrontal cortices. Unexpectedly, affective ratings also correlated with patterns in visual cortices. Crucially, regional variability strongly shaped these correspondences: visual cortices exhibited high noise ceilings and consistent alignment with behavior, whereas associative regions showed greater across-subjects variability and weaker correlations. Finally, algorithms and neural networks with high classification performance on specific properties strongly correlated with behavioral and fMRI RSA matrices. Together, these results demonstrate that similarity is a multidimensional construct that comprises multiple representational spaces whose neural correspondence is shaped by representational content, regional variability, and modeling methodology.
ER -