Target Size Manipulations Affect Error-Processing Duration and Success Perceptions but not Behavioural Indices of Learning
Bibliographic record
Abstract
We evaluated if and how success perceptions, through target size manipulations, impact processes related to motor learning. This work was based on recent literature suggesting that expectations and self-efficacy exert a direct impact on learning. We measured arousal, kinematics, learner expectancies, motivation, and outcomes in a dart-throwing task. Novices (n = 29) were assigned to either a “Large-target” (horizontal target, 10-cm high) or “Small-target” (2-cm high) group for practice (t = 90), and both groups completed 24-h retention tests. The Small-target group took longer to plan and process feedback in the pre-throw and post-throw periods, respectively, and showed larger joint amplitudes early in practice compared to the Large-target group. As predicted, the Large-target group made more hits and had heightened outcome expectancies compared to the Small-target group. Surprisingly, only the Large-target group performed better than they expected. Despite the Large-target group having more target hits, enhanced expectancies, and more unexpected success, this group did not outperform the Small-target group on behavioural indices of performance and learning. This research questions assumptions and results related to success-related manipulations for task performance and mechanisms related to target size manipulations.
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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 | 0.003 |
| 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".