What do Alzheimer's disease patients know about animals? It depends on task structure and presentation format
Bibliographic record
Abstract
Deficits on tasks requiring semantic memory in Alzheimer's disease (AD) may be due to storage loss, a retrieval deficit, or both. To address this question, we administered multiple tasks involving 9 exemplars of the category "animals," presented as both words and pictures, to 12 AD patients and 12 nondemented individuals. Participants made semantic judgments by class (sorting task), similarity (triadic comparison task), and dimensional attributes (ordering task). Relative to control participants, AD patients were impaired on an unstructured sorting task, but did not differ on a constrained sorting task. On the triadic comparison task, the patients were as likely to make judgments based on size as domesticity attributes, whereas control participants made judgments based primarily on domesticity. The patients' judgments were also less consistent across tasks than those of control participants. On the ordering tasks, performance was generally comparable between groups with pictures but not words, suggesting that pictures enable AD patients to access information from semantic memory that is less accessible with lexical stimuli. These results suggest that AD patients' semantic judgments are impaired when the retrieval context is unstructured, but perform normally under supportive retrieval conditions.
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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 | 0.010 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".