Category specificity in the medial temporal lobe: A systematic review
Bibliographic record
Abstract
Theoretical accounts of medial temporal lobe (MTL) function ascribe different functions to subregions of the MTL including perirhinal, entorhinal, parahippocampal cortices, and the hippocampus. Some have suggested that the functional roles of these subregions vary in terms of their category specificity, showing preferential coding for certain stimulus types, but the evidence for this functional organization is mixed. In this systematic review, we evaluate existing evidence for regional specialization in the MTL for three categories of visual stimuli: faces, objects, and scenes. We review and synthesize across univariate and multivariate neuroimaging studies, as well as neuropsychological studies of cases with lesions to the MTL. Neuroimaging evidence suggests that faces activate the perirhinal cortex, entorhinal cortex, and the anterior hippocampus, while scenes engage the parahippocampal cortex and both the anterior and posterior hippocampus, depending on the contrast condition. There is some evidence for object-related activity in anterior MTL regions when compared to scenes, and in posterior MTL regions when compared to faces, suggesting that aspects of object representations may share similarities with face and scene representations. While neuroimaging evidence suggests some hippocampal specialization for faces and scenes, neuropsychological evidence shows that hippocampal damage leads to impairments in scene memory and perception, but does not entail equivalent impairments for faces in cases where the perirhinal cortex remains intact. Regional specialization based on stimulus categories has implications for understanding the mechanisms of MTL subregions, and highlights the need for the development of theoretical models of MTL function that can accommodate the differential patterns of specificity observed in the MTL.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".