Narrative video scene description task discriminates between levels of cognitive impairment in Alzheimer’s disease.
Bibliographic record
Abstract
The process of interpreting and acting upon the visual environment requires both intact cognitive and visual systems. The narrative description (ND) task, initially developed to detect changes in ecologically relevant visual function in people with impaired vision, is an objective measure of the ability to perceive, understand, and describe a visual scene in a movie clip. OBJECTIVE: Because the ND task draws heavily on semantic and working memory ability in addition to basic visual perception, we aimed to assess the discriminative performance of this task across levels of cognitive impairment. METHOD: We recruited 56 participants with cognitive status ranging from normal cognition to mild dementia (median age 82, range 66 to 99 years) to watch 20 30-s video clips and describe the visual content without time constraints. These verbal responses were transcribed and processed to generate ND shared word scores using a "wisdom of the crowd," natural-language processing approach. We compared ND scores across diagnostic groups, and used linear mixed models to examine decrements in task performance. RESULTS: There was a stepwise decline of ND scores with increasing levels of cognitive impairment. Additional analyses showed that ND performance was highly related to performance on the Montreal Cognitive Assessment (MoCA) and domain-specific neuropsychological tests for semantic fluency and set shifting. Other models demonstrated differences in ND performance related video content between cognitively normal and impaired participants. CONCLUSION: The ND test was able to detect decrements in task performance between levels of cognitive impairment and was related to other global neuropsychological measures. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 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".