Invariant inter-subject relational structures in high order human visual cortex
Bibliographic record
Abstract
It is a fundamental of behavior that different individuals see the world in a largely similar manner. This is an essential basis for humans’ ability to cooperate and communicate. However, what are the neural properties that underlie these inter-subject commonalities of our visual world? Finding out what aspects of neural coding remain invariant across individuals’ brains will shed light not only on this fundamental question but will also point to the neural coding scheme at the basis of visual perception. Here, we address this question by obtaining intracranial recordings from three groups of patients taking part in a visual recognition task (overall 19 patients and 244 high-order visual contacts included in the analyses) and examining the neural coding scheme that was most consistent across individuals’ visual cortex. Our results highlight relational coding – expressed by the set of similarity distances between profiles of pattern activations—as the most consistent representation across individuals. Alternative coding schemes, such as activation pattern coding or linear coding, failed to achieve similar inter-subject consistency. Our results thus support relational coding as the central neural code underlying individuals’ shared perceptual content in the human brain. The neuronal coding principles that underlie inter-individual perceptual similarities remains unclear. Here, the authors compared the stability of relational similarity versus activation patterns across brains and found that relational similarity was preferentially consistent across individuals, potentially underlying shared inter-subject perception.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".