Semantic processing in subjective cognitive decline: An eye-tracking study.
Bibliographic record
Abstract
OBJECTIVES: Alzheimer's disease progresses through several stages, starting with a preclinical phase characterized by subjective cognitive decline (SCD), where individuals express concerns about their memory despite normal cognitive test results. Recent research has indicated subtle semantic difficulties in SCD, prompting the need for a deeper investigation into cognitive processing during this phase. This study aimed to investigate the cognitive processing of famous and unfamiliar faces in individuals with SCD compared to healthy controls, focusing on semantic memory deficits assessment. METHOD: Twenty-seven participants with SCD and 26 control participants performed a judgment task involving famous and unfamiliar faces while their eye movements were recorded. Mean fixation times, number of revisitations, and number of fixations were analyzed between the two groups. RESULTS: The SCD group exhibited no significant differences in mean fixation times and in the number of revisited regions between famous and unfamiliar faces, in contrast to the control group, which showed distinct patterns in processing these categories of stimuli. CONCLUSION: These findings suggest that individuals with SCD process famous faces similarly to unfamiliar faces, indicating a potential weakening of semantic processing in SCD. This may have implications for early detection of cognitive decline in Alzheimer's disease. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
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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 | 0.002 |
| 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 | 0.000 | 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".