Measurement of individual differences in face-identity processing abilities in older adults
Bibliographic record
Abstract
BACKGROUND: Face-identity processing declines with age. Few studies have examined whether face-identity processing abilities can be measured independently from general cognitive abilities in older adults (OA). This question has practical implications for the assessment of face-identity processing abilities in OA and theoretical implications for the notion of face processing as a specific ability. The present study examined the specificity of face memory and face matching abilities in OA aged 50 + . METHODS: Performance of younger adults (YA) and OA was measured on face tasks: Cambridge Face Memory Task (CFMT), the Glasgow Face Matching Task (GFMT), holistic processing; and tasks of general cognition: fluid intelligence, selective attention, and mental rotation. Data were analyzed using multiple regression models encompassing (i) the CFMT/GFMT and measures of general cognition; and (ii) all face processing tasks. RESULTS: Across the two age groups, models encompassing all face tasks were significant and accounted for more variance in the data than models encompassing the CFMT/GFMT and measures of general cognition. General cognitive abilities accounted for 17% of variance for the GFMT (p < 0.01) and 3% for the CFMT (p > 0.05). DISCUSSION: Our results suggest that face memory can be measured independently from general cognition using the CFMT in OA. Implications for the notion of a general face processing factor across the adult lifespan are discussed.
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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 | 0.003 |
| 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".