Individual differences reveal shared computational structure in perceptual decision-making
- 1Georgia Institute of Technology
Abstract
To discover computational mechanisms of cognition, models typically specify a fixed psychological architecture, treating individual differences as variations within a predefined parameterization. Here, we take a different approach by using the population structure of individual differences to directly drive model discovery. We develop a low-rank neural network that uses this `population geometry' to learn shared computational components and participant-specific weights from trial-level choices, allowing cross-subject variation to constrain the model's architecture. Applied to perceptual decision-making in the Bernoulli Clicks Task across primary and replication datasets, out-of-sample prediction reveals a six-dimensional representation for individual differences: two continuous dimensions of temporal evidence weighting and two of choice-outcome history dependence, alongside participant-specific side bias and signal-to-noise ratio. This low-dimensional organization replicated in an independent dataset. Together, these findings reveal that seemingly disparate decision heuristics reflect different weightings over a small set of shared computational components, and establish a principled methodology for using behavioral variability to discover the mechanistic architecture of cognition.
Related
- Color naming needs a non-uniform perceptual prior: Individual differences in hue scaling and the vision–language interface
- Individual differences in Game Transfer Phenomena are associated with creativity and positive schizotypy but not with perceptual priming
- Artificial observers and real individual differences: A roadmap for a computational psychometrics of vision
- Individual differences in ethanol drinking meal structure are shaped by social environments
- Transcutaneous vagus nerve stimulation boosts evidence accumulation during perceptual decision making
BibTeX
@misc{xue2026individual,
title = {{Individual differences reveal shared computational structure in perceptual decision-making}},
author = {J. Xue and S. F. Feng and H.-D. Xiong and S. Wang and R. C. Wilson},
howpublished = {bioRxiv},
year = {2026},
month = nov,
doi = {10.64898/2026.11.04.764343},
url = {https://www.biorxiv.org/content/10.64898/2026.11.04.764343v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - Individual differences reveal shared computational structure in perceptual decision-making
AU - J. Xue
AU - S. F. Feng
AU - H.-D. Xiong
AU - S. Wang
AU - R. C. Wilson
PY - 2026
DA - 2026/11/04
PB - bioRxiv
DO - 10.64898/2026.11.04.764343
UR - https://www.biorxiv.org/content/10.64898/2026.11.04.764343v1
AB - To discover computational mechanisms of cognition, models typically specify a fixed psychological architecture, treating individual differences as variations within a predefined parameterization. Here, we take a different approach by using the population structure of individual differences to directly drive model discovery. We develop a low-rank neural network that uses this `population geometry' to learn shared computational components and participant-specific weights from trial-level choices, allowing cross-subject variation to constrain the model's architecture. Applied to perceptual decision-making in the Bernoulli Clicks Task across primary and replication datasets, out-of-sample prediction reveals a six-dimensional representation for individual differences: two continuous dimensions of temporal evidence weighting and two of choice-outcome history dependence, alongside participant-specific side bias and signal-to-noise ratio. This low-dimensional organization replicated in an independent dataset. Together, these findings reveal that seemingly disparate decision heuristics reflect different weightings over a small set of shared computational components, and establish a principled methodology for using behavioral variability to discover the mechanistic architecture of cognition.
ER -