Influence of Conceptual Knowledge on Visual Object Discrimination: Insights from Semantic Dementia and MTL Amnesia
Bibliographic record
Abstract
Recent evidence suggests that the perirhinal cortex is involved in perception of complex objects with ambiguous features. Anterior regions of the temporal lobes, including the perirhinal cortex as well as lateral cortex, are also thought to play a critical role in semantic memory. To understand how semantic factors might contribute to perceptual discrimination of complex objects, we studied visual object discrimination in patients with semantic dementia (SD)-a neurodegenerative condition characterized by progressive deterioration of semantic knowledge and atrophy to anterior temporal lobes (including perirhinal cortex). In 3 experiments, we assessed discrimination of meaningful (e.g., familiar real-world objects) and novel (e.g., blobs) objects with varying feature ambiguity levels. In a fourth experiment, we compared SD patients with amnesic patients with nonprogressive medial temporal lobe (MTL) lesions and less impaired semantic memory. Across studies, patients with perirhinal damage were impaired at discriminating objects with a high, but not low, degree of feature ambiguity, consistent with previous work indicating a perceptual role for this structure. Stimulus meaningfulness, however, differentially influenced performance in SD patients compared with MTL amnesics, suggesting that perceptual representations of complex objects (dependent upon perirhinal cortex) interact with higher-order abstract conceptual representations, even for tasks with no overt semantic component.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".