Lexicality judgements in healthy aging and in individuals with Alzheimer's disease
Bibliographic record
Abstract
Neighbourhood density (N) has been shown to influence how lexical stimuli are accessed. In young adults, a large N is facilitatory for words but inhibitory for pseudowords in English. While there is a paucity of studies probing N as people age, results to date point towards changes in lexical processing that occur with aging. We are not aware of any studies that have sought to investigate N in Alzheimer’s disease (AD) in English. Results from the lexical decision task reported here support previous N findings for young adults. However, older adults and those with AD showed a different pattern of performance. Both were slower to respond to and made more errors to high versus low N pseudowords but, unlike young adults, older adult groups showed a decrease in sensitivity to N for words. Results suggest that the aging process may change how N is processed; older individuals are no longer as sensitive to N and this appears to be further altered by AD. In the context of the multiple read-out model of lexical processing, this change may be due to a longer time required to activate lexical neighbours which, in turn, results in differential N effects for words and pseudowords.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".