Exploring Visual Selective Attention towards Novel Stimuli in Alzheimer's Disease Patients
Bibliographic record
Abstract
BACKGROUND: Alzheimer's disease (AD) is associated with selective attention impairments, which could contribute to cognitive and functional deficits. Selective attention can be explored through examination of novelty preference. AIMS: In this study, we quantified novelty preference in AD patients by measuring visual scanning behaviour using an eye tracking paradigm. METHODS: Mild-to-moderate AD patients and elderly controls viewed slides containing novel and repeated images simultaneously. The outcome measure was time spent on specific images, with novelty preference defined by greater relative fixation time (RFT) on novel versus repeated images. Cognitive status (Standardized Mini-Mental State Examination, SMMSE) and attention (Digit Span, DS) were also measured. RESULTS: AD patients (age 79.2 ± 6.7 years, SMMSE 22.2 ± 4.0, n = 41) and controls (age 76.2 ± 6.4 years, SMMSE 28.1 ± 2.0, n = 24) were similar in age, education and sex. Compared with controls, AD patients had lower RFT on novel than on repeated images (F1,63 = 11.18, p = 0.001). Further, reduced RFT was associated with lower scores on SMMSE (r63 = 0.288, p = 0.020) and DS (r63 = 0.269, p = 0.030). Within individuals, novelty preference was detected in 92.3% of patients and in 100% of controls. CONCLUSION: These findings suggest that novelty preference, measured by visual scanning behaviour, can differentiate cognitively healthy and impaired people and may offer a nonverbal, less cognitively demanding method of assessing selective attention.
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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 | 0.001 |
| 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 | 0.000 | 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".