Processing lexicality in healthy aging and Alzheimer’s disease
Bibliographic record
Abstract
Abstract To explore how processing lexicality may change with aging and in the presence of Alzheimer’s disease (AD), we conducted two experiments investigating lexicality judgements using an on-line behavioural psycholinguistic methodology and electrophysiological/event-related potential (ERP) methods; oddball lexical decision tasks. Results from these lexical decision tasks showed that while those with AD show similar rates of accuracy for their lexical decision as compared older adults (OA), they are particularly slowed when making judgements for pseudowords. Our results from the ERP tasks also showed that the two groups behaved differently with regard to elicitation of the P3 ERP response, which indicates differences in how these two groups form lexical categories. The pattern of ERP responses suggests that older adults are sensitive to the orthography/phonology of the stimuli during the course of lexical processing as compared to participants with AD who show less sensitivity to orthographic/phonological cues. Additionally, the ERP P3 amplitude results suggest further linguistically related differences between healthy older adults and those with AD, and highlight the importance and usefulness of combining behavioural psycholinguistic and ERP methodologies.
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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 | 0.002 |
| 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 | 0.001 |
| 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".