Resolving Cross-modal Semantic Interference among Object Concepts Requires Medial Temporal Lobe Cortex
Bibliographic record
Abstract
The ability to flexibly categorize object concepts is essential to semantic cognition because the features that make two objects similar in one context may be irrelevant and even constitute interference in another. Thus, adaptive behavior in complex and dynamic environments requires the resolution of feature-based interference. In the current case study, we placed visual and functional semantic features in opposition across object concepts in two categorization tasks. Successful performance required the resolution of functional interference in a visual categorization task and the resolution of visual interference in a functional categorization task. In Experiment 1, we found that patient D. A., an individual with bilateral temporal lobe lesions, was unable to categorize object concepts in a context-dependent manner. His impairment was characterized by an increased tendency to incorrectly group objects that were similar on the task-irrelevant dimension, revealing an inability to resolve cross-modal semantic interference. In Experiment 2, D. A.'s categorization accuracy was comparable to controls when lures were removed, indicating that his impairment is unique to contexts that involve cross-modal interference. In Experiment 3, he again performed as well as controls when categorizing simple concepts, suggesting that his impairment is specific to categorization of complex object concepts. These results advance our understanding of the anterior temporal lobe as a system that represents object concepts in a manner that enables flexible semantic cognition. Specifically, they reveal a dissociation between semantic representations that contribute to the resolution of cross-modal interference and those that contribute to the resolution of interference within a given modality.
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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) | 0.001 | 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".