Resolving cross-domain semantic interference among object concepts requires medial temporal lobe cortex
Bibliographic record
Abstract
Semantic features, such as prototypical visual form or function, are often shared across multiple object concepts. How, then, are we able to resolve interference between object concepts that look alike but perform different functions (e.g., hairdryer and gun) or that do similar things but look rather dissimilar (e.g., hairdryer and comb)? We examined this issue in the current neuropsychological single-case study by asking whether perirhinal cortex (PRC) critically enables resolution of interference among object concepts at the level of their conceptually- and visually-based semantic features. We tested three patients with differing lesion profiles using a novel discrimination task involving stimuli for which visual and conceptual similarity were not linked across object concepts. We found that D.A., an individual with a brain lesion that includes PRC, was impaired at discriminating among object concepts when there was a high degree conceptual and visual semantic feature overlap among choices. We replicated this result in a second testing session. Conversely, patients with selective hippocampal or ventromedial prefrontal cortical lesions were unimpaired on this task. Importantly, D.A.’s performance was intact when (i) conceptual and visual interference among object concepts was minimized, and (ii) when the discriminations involved simple stimuli that did not require assessment of multiple stimulus dimensions. These results reveal a novel semantic deficit in a patient with PRC damage, suggesting that this structure represents object concepts in a manner that can be flexibly reshaped to emphasize task relevant semantic features.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".