Pattern Separation Contributes to Categorical Face Perception
Bibliographic record
Abstract
Abstract Traditionally considered a memory structure, the hippocampus has been shown to contribute to non-memory functions, from perception to language. Recent evidence suggests that the ability to differentiate highly confusable faces could involve pattern separation, a mnemonic process mediated by the hippocampal dentate gyrus (DG). Hippocampal involvement, however, may depend on existing face memories. To investigate these possibilities, we tested BL, a rare individual with bilateral lesions selective to the DG, and healthy controls. Both were administered morphed images of famous and nonfamous faces in a categorical perception (CP) identification and discrimination experiment. All participants exhibited nonlinear identification of famous faces with a midpoint category boundary. Controls identified newly learned nonfamous faces with lesser fidelity, while BL showed a notable shift in category boundary. When discriminating face pairs, controls showed typical CP effects of better between-category than within-category discrimination — but only for famous faces. BL showed extreme within-category “compression,” reflecting his tendency to pattern complete following suboptimal pattern separation. We provide the first evidence that pattern separation contributes to CP of faces.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".