Age-related dedifferentiation and hyperdifferentiation of perceptual and mnemonic representations
Bibliographic record
Abstract
Abstract Preliminary evidence indicates that occipito-temporal activation patterns for different visual stimuli are less distinct in older (OAs) than younger (YAs) adults, suggesting a dedifferentiation of visual representations with aging. Yet, it is unclear if this deficit (1) affects only sensory or also categorical aspects of representations during visual perception (perceptual representations), and (2) affects only perceptual or also mnemonic representations. To investigate these issues, we fMRI-scanned YAs and OAs viewing and then remembering visual scenes. First, using representational similarity analyses, we distinguished sensory vs. categorical features of perceptual representations. We found that, compared to YAs, sensory features in early visual cortex were less differentiated in OAs (i.e., age-related dedifferentiation), replicating previous research, whereas categorical features in anterior temporal lobe (ATL) were more differentiated in OAs. This is, to our knowledge, the first report of an age-related hyperdifferentiation . Second, we assessed the quality of mnemonic representations by measuring encoding-retrieval similarity (ERS) in activation patterns. We found that aging impaired mnemonic representations in early visual cortex and hippocampus but enhanced mnemonic representations in ATL. Thus, both perceptual and mnemonic representations in ATL were enhanced by aging. In sum, our findings suggest that aging impairs visual and mnemonic representations in posterior brain regions but enhances them in anterior regions.
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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 | 0.000 |
| 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".