Medial temporal lobe activity during complex discrimination of faces, objects, and scenes: Effects of viewpoint
Bibliographic record
Abstract
The medial temporal lobe (MTL), a set of heavily interconnected structures including the hippocampus and underlying entorhinal, perirhinal and parahippocampal cortex, is traditionally believed to be part of a unitary system dedicated to declarative memory. Recent studies, however, demonstrated perceptual impairments in amnesic individuals with MTL damage, with hippocampal lesions causing scene discrimination deficits, and perirhinal lesions causing object and face discrimination deficits. The degree of impairment on these tasks was influenced by the need to process complex conjunctions of features: discriminations requiring the integration of multiple visual features caused deficits, whereas discriminations that could be solved on the basis of a single feature did not. Here, we address these issues with functional neuroimaging in healthy participants as they performed a version of the oddity discrimination task used previously in patients. Three different types of stimuli (faces, scenes, novel objects) were presented from either identical or different viewpoints. Consistent with studies in patients, we observed increased perirhinal activity when participants distinguished between faces and objects presented from different, compared to identical, viewpoints. The posterior hippocampus, by contrast, showed an effect of viewpoint for both faces and scenes. These findings provide convergent evidence that the MTL is involved in processes beyond long-term declarative memory and suggest a critical role for these structures in integrating complex features of faces, objects, and scenes into view-invariant, abstract representations.
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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".