The Contribution of the Face-Name Associative Recognition Test to Objectifying the Impairment of Associative Memory in Subjective Cognitive Decline
Bibliographic record
Abstract
OBJECTIVE: Subjective cognitive decline (SCD) is defined as a self-reported perception of cognitive decline that occurs without clear objective signs of cognitive impairment. There is still uncertainty in the literature about the reliability of SCD as an accurate indicator of the early stages of major neurocognitive disorders. Furthermore, objectifying cognitive impairment in SCD is difficult, mainly due to the insensitivity of the assessment instruments. The main objective of this study was to investigate the potential contribution of the face-name associative recognition test (FNART) to the objective identification of memory impairment in SCD. METHOD: A research sample of 69 adults with SCD and 69 healthy controls (HCs) recruited in the community were administered in the FNART, which included 32 photographs of neutral faces associated with 32 first names. RESULTS: The total score of the HC group in the FNART was significantly better than that of the SCD group. Moreover, analyses based on the serial position of the stimuli showed that the SCD group performed significantly worse than the HC group only for the middle items (stimuli placed at the beginning or end of learning lists are more likely to be recalled than those presented in the middle), while no primacy and recency effects were found in the HCs. CONCLUSIONS: These findings indicate that associative episodic memory is more vulnerable in individuals with subjective cognitive decline (SCD) compared to those without cognitive complaints. Additionally, they suggest that the FNART may be effective in identifying cognitive decline in the preclinical stage of Alzheimer's disease.
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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 | 0.005 |
| 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) | 0.002 | 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".