Symmetry processing in the macaque visual cortex
Bibliographic record
Abstract
Abstract Symmetry is a highly salient feature of the natural world that is perceived by many species of the animal kingdom and that impacts a large array of behaviours such as partner selection or food choice. In humans, the cerebral areas processing symmetry are now well identified from neuroimaging measurements. However, we currently lack an animal model to explore the underlying neural mechanisms. Macaque is a potentially good candidate, but a previous comparative study (1) found that functional magnetic resonance imaging (fMRI) responses to mirror symmetry in this species were substantially weaker than those observed in humans under similar experimental conditions. Here, we re-examined symmetry processing in macaques from a broader perspective, using both rotation (experiment 1) and reflection (experiment 2) symmetry. Our experimental design was directly derived from that of a previous human fMRI study (2), in order to facilitate the comparison between the two primate species. Highly consistent responses to symmetry were found in a large network of areas (notably V3, V3A, V4, V4A and PITd), in line with what has been observed in humans. Within this network, response properties in areas V3 and V4 (notably their dependency on the rotation symmetry order) were strikingly similar to those observed in their human counterparts. Our results suggest that the cortical networks that process symmetry in humans and macaques are much more similar than previously reported and point toward macaque as a relevant model for understanding symmetry processing. Significance statement Symmetry processing is an important aspect of human visual perception. We currently lack an animal model for characterizing the neural mechanisms that underlie it at the microscopic scale. Here, we use fMRI measurements in macaques to demonstrate that the cortical responses to symmetry in this species are comparable to those observed in humans under similar experimental conditions to a much higher extent than previously documented. Our results call for a re-examination of the relevance of the macaque model for symmetry processing in humans and open the door to an exploration of the underlying neural mechanisms at the single-cell level, notably in V3, an area often neglected in most current models of visual processing.
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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".