Facial expression discrimination emerges from neural subspaces shared with detection and identity
Bibliographic record
Abstract
Abstract Understanding how the human brain decodes facial expressions remains a fundamental challenge, requiring computational models that tightly connect neural responses to behavior. Here, we demonstrate that rhesus macaques provide a unique and powerful animal model to uncover the neural computations behind human facial expression discrimination, bridging critical gaps between behavior, neural activity, and computational theory. Despite the challenges of establishing reliable behavioral paradigms in macaques, we developed a robust discrimination task spanning six emotional categories, yielding strong, image-by-image behavioral correspondence between macaques and humans. By systematically comparing artificial neural networks (ANNs) to macaque behavior and IT neural data, we found that traditional action unit–based models fail to capture image-level behavioral structure, while ANNs with IT-like internal representations outperform all others. Neural recordings showed that the specific IT population responses (70–100 ms) carried the strongest predictive power for facial expression discrimination, underscoring the primacy of feedforward codes in guiding behavior. Expression coding in IT was significantly shaped by face-selective neurons that also encoded identity. This convergence points to a shared functional subspace in IT, where stable (identity) and dynamic (expression) information coexist along overlapping dimensions. Such an architecture moves beyond the classical view of segregated pathways, revealing a general coding principle by which IT flexibly supports multiple socially relevant functions within a common representational geometry.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".