Human Ageing is Associated with More Rigid Concept Spaces
Bibliographic record
Abstract
Abstract Prevalence-induced concept change describes a cognitive mechanism by which someone’s definition of a concept shifts as the prevalence of instances of that concept changes. The phenomenon has real-world implications because this sensitivity to environmental characteristics may lead to substantial biases in judgements. While prevalence-induced concept change has been established in young adults, it is unclear how it changes as a function of human ageing. In this cross-sectional study, we explore how prevalence-induced concept change affects older adults’ lower-level, perceptual, and higher-order, ethical, judgements. We find that older adults are less sensitive to prevalence-induced concept change than younger adults across domains. Using a combination of computational and experimental approaches, we demonstrate that these changes in judgements are sensitive to the pace with which the stimuli occur in the environment and are affected by the effort that subjects invest in order to make accurate decisions. Based on findings from three experiments we argue that older adults’ concept spaces are more rigid than those of younger adults. However, what appear as an age-related cognitive “deficit” may turn out to be beneficial because it makes older adults less susceptible to biases in judgments.
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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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".