Bibliographic record
Abstract
Prevalence-induced concept change describes a cognitivemechanism by which someone’s definition of a concept shiftsas the prevalence of exemplars of that concept changes. Forinstance, in a task where people have to judge whether thecolour of an ambiguously-coloured dot is blue or purple, if thefrequency of objectively blue dots in the environmentdecreases, people expand their concept of blueness and judgemore dots to be blue than they did initially. In a series ofexperiments, Levari et al. (2018) demonstrated that thisphenomenon extends to higher-order decision-making, suchas ethical judgments as well. What these findings suggest isthat conceptual spaces (whether it’s about colours or ethicalstatements) in humans are not fixed, but are sensitive tochange. While Levari et al. (2018) established thisphenomenon in young adults, it is unclear how it affects olderadults: do they outsource control and become moresusceptible to concept change or are they rigid enough in theirbeliefs to be resistant to it? In the current study, we explorehow prevalence-induced concept change affects older adults’lower-level, perceptual, and higher- order, ethical,decision-making. We find that older adults are less sensitiveto prevalence-induced concept change than younger adultsacross both domains. A computational model reveals thatthese differences might in part be explained by older adults’tendency to perseverate (repeat responses). Our resultssuggest that older adults’ concept space may be less flexiblethan younger adults’ when faced with a changing world.
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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 | 0.002 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".