Visual Selective Attention Toward Novel Stimuli Predicts Cognitive Decline 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. Using visual scanning parameters, selective attention toward novel stimuli, or novelty preference, can be measured by a non-verbal, non-invasive method that may be of value in predicting disease progression. OBJECTIVE: In this longitudinal study, we explored whether novelty preference can predict cognitive decline in AD patients. METHODS: Mild to moderate AD patients viewed slides containing both novel and repeat images. The number of fixations, the average fixation time, and the relative fixation time on the two types of images were measured by an eye-tracking system. Novelty preference was estimated by the differences between the visual scanning parameters on novel and repeat images. Cognition and attention were assessed using the Standardized Mini-Mental Status Examination (sMMSE) and the Conners' Continuous Performance Test (CPT), respectively. Cognition was re-assessed every 6 months for up to 2 years. RESULTS: Multivariate linear regressions of 32 AD patients (14 females, age = 77.9±7.8, baseline sMMSE = 22.2±4.4) indicated that reduced time spent on novel images (t = 2.78, p = 0.010) was also associated with greater decline in sMMSE scores (R2 = 0.41, Adjusted R2 = 0.35, F3,28 = 6.51, p = 0.002), adjusting for attention and baseline sMMSE. CONCLUSION: These results suggest that novelty preference, measured by visual attention scanning technology, may reflect pathophysiological processes that could predict disease progression in the cognitively-impaired.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".