Semantic inhibition impairment in mild cognitive impairment: A distinctive feature of upcoming cognitive decline?
Bibliographic record
Abstract
This study aimed to measure semantic inhibitory capacities in persons with a diagnosis of Alzheimer's disease (AD) or mild cognitive impairment (MCI), in healthy older and younger adults. This was done by relying on a computerized adaptation of the Hayling task, designed to diminish the likelihood of using alternative noninhibitory strategies. Participants with both AD and MCI showed impaired performance on the inhibition condition. Participants with AD showed both poorer score and an increased number of errors, whereas persons with MCI obtained lower score. There was also an effect of normal aging in the inhibition condition when considering reaction time only. In participants with MCI and AD, there was a significant correlation between lexico-semantic capacities and performance on the automatic condition. Follow-up analysis revealed that participants with MCI who experienced a subsequent significant cognitive decline had impaired performance in the inhibition condition at the time of the experiment, while participants with MCI who remained stable did not. Overall, results indicate that semantic inhibition of a prepotent response is impaired in participants with MCI and may have predictive value regarding future decline, supporting its prognostic role in the early identification of dementia.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".