Investigating the representational transformations underlying the learning of exceptions in visual categories
Bibliographic record
Abstract
Categories in the real world often contain perceptually incoherent objects, such as structural exceptions that resemble members of competing categories and oddball exceptions that encompass features distinct from any known categories. Prominent theories suggest that learning exceptions depends on flexible transformations of category representations, yet evidence of such representational dynamics in the brain is limited. Here, we had participants learn competing visual categories that included both structural and oddball exceptions while fMRI data was collected before, in the middle of, and after category learning. Representational similarity analysis of neural patterns for category stimuli at each learning stage revealed that exception learning induced unique transformations in object representations in distinct brain regions. Specifically, the introduction of exceptions led to an increase in feature-specific information in visual cortex representations. In contrast, representations in the prefrontal cortex exhibited an increase in prototype information consistent with coding for category regularities. Notably, subfields of the hippocampal formation also showed distinct transformations—feature-specific information increased in dentate gyrus representations and decreased in CA1 representations. These results align with the dentate gyrus’ theorized role in constructing item-specific representations and CA1’s role in generalization across related experiences. Moreover, we found that exception learning induced distinct representational transformations for structural and oddball exceptions. Particularly, within the representational spaces of the prefrontal and temporal cortices, structural exceptions became uniquely more differentiated from regular category members through learning. This finding aligns with the expectation that learning structural exceptions relies on distinguishing them from perceptually confusable items in the competing category via differentiation. Altogether, our results demonstrate that object representations can be flexibly and selectively transformed across the brain to support the learning of category regularities and their exceptions.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".