Normal and Pathological Aging of Attention in Presymptomatic Huntington’s, Huntington’s and Alzheimer’s Disease, and Nondemented Elderly Subjects
Bibliographic record
Abstract
BACKGROUND: Attention models view attention as having at least two components: endogenous attention defined as executive and directed by voluntary acts, and exogenous attention defined as automatic and directed by external stimulation. METHODS: Three studies (2 of our own) were designed to evaluate the decline of these two components of attention in normal aging and two neurodegenerative diseases. Standardized tests derived from Posner's model of visuospatial attention were administered to normal healthy elderly participants (n = 13), patients suffering from Huntington's disease (HD; n = 17) and Alzheimer's disease (n = 15), and matched control subjects (n = 57). Outcome measures were reaction time (RT) and RT difference score (defined as invalid RT minus valid RT). RESULTS: In healthy elderly participants, the decline was more pronounced for endogenous attention in situations of perceptual conflict. In Alzheimer's disease, there was a significant decline in both attention components, while in HD, voluntary attention was markedly impaired and automatic attention preserved. CONCLUSIONS: Normal aging and HD are characterized by decreased endogenous attention in situations of perceptual conflict. Our data support previous findings that older people display impairment of attention in complex perceptual situations. We propose a model which allows for the separation of attention pathologies, thus improving therapeutic strategies for patients and elderly.
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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 | 0.001 |
| 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".