Jacob S. Prince, George A. Alvarez, and Talia Konkle
Harvard University, Dept. of Psychology
This repository houses code for generating figures in the following Science Advances paper: https://www.science.org/doi/10.1126/sciadv.adl1776
All plotting routines depend on helper functions that exist in the following utilities repository:
https://github.com/jacob-prince/jsputils
For questions, please feel free to email me: jacob.samuel.prince@gmail.com.
Abstract:
Modular and distributed coding theories of category selectivity along the human ventral visual stream have long existed in tension. Here, we present a reconciling framework—contrastive coding—based on a series of analyses relating category selectivity within biological and artificial neural networks. We discover that, in models trained with contrastive self-supervised objectives over a rich natural image diet, category-selective tuning naturally emerges for faces, bodies, scenes, and words. Further, lesions of these model units lead to selective, dissociable recognition deficits, highlighting their distinct functional roles in information processing. Finally, these pre-identified units can predict neural responses in all corresponding face-, scene-, body-, and word-selective regions of human visual cortex, under a highly constrained sparse positive encoding procedure. The success of this single model indicates that brain-like functional specialization can emerge without category-specific learning pressures, as the system learns to untangle rich image content. Contrastive coding, therefore, provides a unifying account of object category emergence and representation in the human brain.