Wise or decrepit? The effects of age-related primes on a manual aiming task
Bibliographic record
Abstract
Age-related stereotypes have a more negative undertone in North America than in eastern regions of the world. Individuals' behaviour, memory, handwriting and walking has been shown to be affected through implicitly primed age-related stereotypes where performance is worse following a negative prime and improved following a positive prime. No research, however, has looked at the effect of age related primes on a manual aiming task. A manual aiming task may allow for more sensitive, trial-by-trial testing of the cognitive processing of age-related stereotypes. The purpose of this project was to investigate the effect that implicit, age-related stereotypes have on an upper-limb reaching task in both older and younger adults. Participants were exposed to four blocks of trials, which were either blocked (positive or negative) or variable (positive and negative), by stereotype. Participants initiated a trial by placing their index finger on a 'start' button at the bottom of the screen. Following what was perceived by the participant as an on-screen flash – which was actually the stereotype associated word (e.g., negative stereotype: decrepit) – participants reached to a centrally located target. It was hypothesized that movement parameters in older adults would be affected congruent to the stereotype with which they were primed; younger adults would show less of an effect. Unexpectedly, younger adults move faster when primed with negative vs positive stereotypes, while there was no change in movement parameter seen in older adults. The results imply that trial-by-trial effects of implicit primes may differ from blocked priming protocols.
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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 | 0.004 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".