The Effects of Prior Exposure on Face Processing in Younger and Older Adults
Bibliographic record
Abstract
Older adults differ from their younger counterparts in the way they view faces. We assessed whether older adults can use past experience to mitigate these typical face-processing differences; that is, we examined whether there are age-related differences in the use of memory to support current processing. Eye movements of older and younger adults were monitored as they viewed faces that varied in the type/amount of prior exposure. Prior exposure was manipulated by including famous and novel faces, and by presenting faces up to five times. We expected that older adults may have difficulty quickly establishing new representations to aid in the processing of recently presented faces, but would be able to invoke face representations that have been stored in memory long ago to aid in the processing of famous faces. Indeed, younger adults displayed effects of recent exposure with a decrease in the total fixations to the faces and a gradual increase in the proportion of fixations to the eyes. These effects of recent exposure were largely absent in older adults. In contrast, the effect of fame, revealed by a subtle increase in fixations to the inner features of famous compared to non-famous faces, was similar for younger and older adults. Our results suggest that older adults' current processing can benefit from lifetime experience, however the full benefit of recent experience on face processing is not realized in older adults.
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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 | 0.000 | 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) | 0.003 | 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".