Perceptual Similarity Can Drive Age-Related Elevation of False Recognition
Bibliographic record
Abstract
Older adults consistently show elevated rates of false recognition of new items that are related to studied items. This finding has been largely attributed to a greater tendency for older adults to rely on conceptual gist during memory recognition tasks. However, perceptual factors may also be implicated considering that related items are not only conceptually but also perceptually similar. While some findings do suggest that age-related increases in false recognition can be driven by perceptual factors, little is known about the nature and circumstances under which these factors operate. To address this gap, we measured basic visual ability as well as false recognition for four different image categories (upright faces, inverted faces, chairs, houses) in younger (n= 34) and older (n=34) adults. Each image category represented different levels of variability in perceptual similarity and pre-experimental exposure. Perceptual similarity was objectively defined on the basis of the low-level properties of the images. We found evidence that perceptual ability mediates elevated false recognition in older adults for perceptually similar but not perceptually dissimilar items. Perceptual factors were particularly salient for upright faces, where age-related differences in false alarms for faces were eliminated when perceptual ability was included as a covariate. We conclude that both perceptual and conceptual similarity can drive age-related differences in false recognition.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".