The medial temporal lobe is critical for spatial relational perception
Bibliographic record
Abstract
Abstract The medial temporal lobe (MTL) is traditionally considered to be a system that is specialized for long-term memory. Recent work has challenged this notion by demonstrating that this region can contribute to many domains of cognition beyond long-term memory, including perception and attention. One potential reason why the MTL (and hippocampus specifically) contributes broadly to cognition is that it contains relational representations — representations of multidimensional features of experience and their unique relationship to one another — that are useful in many different cognitive domains. Here, we explore the hypothesis that the hippocampus/MTL plays a critical role in attention and perception via relational representations. We compared human participants with MTL damage to healthy age- and education-matched individuals on attention tasks that varied in relational processing demands. On each trial, participants viewed two images (rooms with paintings). On ‘ similar room ’ trials, they judged whether the rooms had the same spatial layout from a different perspective. On ‘ similar art ’ trials, they judged whether the paintings could have been painted by the same artist. On ‘ identical ’ trials, participants simply had to detect identical paintings or rooms. Patients were significantly and selectively impaired on the similar room task. This work provides further evidence that the hippocampus/MTL plays a ubiquitous role in cognition by virtue of its relational and spatial representations, and highlights its important contributions to rapid perceptual processes that benefit from attention.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".