Target size manipulations affect self-efficacy, success expectations, and processing durations but do not impact motivation and behavioural indices of performance and learning in dart-throwing
Bibliographic record
Abstract
We evaluated if and how success perceptions, through target size manipulations, impacted learner expectancies, motivation, and behavioural outcomes in a dart-throwing task. This work was based on the OPTIMAL theory and predictions regarding moderating roles of expectations and efficacy on learning (potentially as a result of dopaminergic signals related to reward and reward prediction error). Novices (n = 29) were assigned to either a (horizontal target, 10 cm high) or (2 cm high) group for one session of practice (t = 90). The Small-band group took longer to plan and process feedback in pre- and post-throw periods respectively, and showed larger joint amplitudes early in practice compared to the Large-band group. As expected, the Large-band group made more hits and had heightened expectancies compared to the Small-band group. Remarkably, the Large-band group performed above their expectations more than the Small-band group even though their expectancies were already elevated by the manipulation. Despite enhanced expectancies and as such more unexpected success, the groups did not differ on motivation and behavioural indices of performance and learning. This research questions assumptions and results related to success-related manipulations for task performance.Acknowledgments: Discovery research grant awarded to Hodges from the Natural Sciences and Engineering Research Council of Canada
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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 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".