Quick to Act, Quick to Forget: The Link between Impulsiveness and Prospective Memory
Bibliographic record
Abstract
Several traits of impulsiveness (e.g. lack of planning and perseverance, difficulty focusing attention) seem intimately connected to the skills required for successful prospective memory performance. This is the first study to examine whether the various inter–correlated dimensions of impulsiveness are related to problems with prospective memory. Undergraduate students (N = 184) completed the Barratt Impulsiveness Scale 11, the Prospective Memory Questionnaire, the Prospective and Retrospective Memory Questionnaire, and two objective prospective memory tests. Results revealed consistent correlations between the various dimensions of impulsiveness (attentional, motor, non–planning) and self–reported problems with prospective memory. Subsequent regression analyses indicated that attentional impulsiveness is a unique predictor of self–reported problems with internally cued prospective memory, and non–planning impulsiveness is a unique predictor of self–reported problems with episodic and overall prospective memory. Similarly, findings from the objective prospective tests showed that non–planning impulsiveness was related to worse performance on the two prospective memory tests. Whereas non–planning impulsiveness was also related to using fewer prospective memory–aiding strategies, mediation analyses showed that use of these strategies does not account for any of the detected relationships. Because the findings suggest that a failure to plan does not underlie the detected effects, other potential explanations for the relationships are discussed. Copyright © 2013 European Association of Personality Psychology
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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 | 0.011 |
| 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 | 0.001 | 0.001 |
| 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".