The self: Your own worst enemy? A test of the self-invoking trigger hypothesis
Bibliographic record
Abstract
The self-invoking trigger hypothesis was proposed by Wulf and Lewthwaite [Wulf, G., & Lewthwaite, R. (2010). Effortless motor learning? An external focus of attention enhances movement effectiveness and efficiency. In B. Bruya (Ed.), Effortless attention: A new perspective in attention and action (pp. 75-101). Cambridge, MA: MIT Press] as a mechanism underlying the robust effect of attentional focus on motor learning and performance. One component of this hypothesis, relevant beyond the attentional focus effect, suggests that causing individuals to access their self-schema will negatively impact their learning and performance of a motor skill. The purpose of the present two studies was to provide an initial test of the performance and learning aspects of the self-invoking trigger hypothesis by asking participants in one group to think about themselves between trial blocks-presumably activating their self-schema-to compare their performance and learning to that of a control group. In Experiment 1, participants performed 2 blocks of 10 trials on a throwing task. In one condition, participants were asked between blocks to think about their past throwing experience. While a control group maintained their performance across blocks, the self group's performance was degraded on the second block. In Experiment 2, participants were asked to practice a wiffleball hitting task on two separate days. Participants returned on a third day to perform retention and transfer tests without the self-activating manipulation. Results indicated that the self group learned the hitting task less effectively than the control group. The findings reported here provide initial support for the self-invoking trigger hypothesis.
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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 | 0.016 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".