Providing knowledge of results based on an absolute performance bandwidth results in illusions of competency
Bibliographic record
Abstract
Previous research that provided knowledge of results (KR) based on relative performance showed learning was enhanced when KR was given after the 3 best, rather than the 3 worst trials in a 6 trial block (Chiviacowsky & Wulf, 2007). However, a distinction based on relative performance is problematic for 2 reasons: 1) similar learning experiences are afforded between groups based on KR content and, 2) KR presented as worst may not truly reflect a bad trial and vice-versa. The present study addressed this issue by using an absolute distinction between bad and good trials in a dart throwing task where vision was removed upon release of the dart. The task goal was to hit the bullseye (12pts) and groups either received KR after Bad trials (1-5pts) only or Good trials (8-12pts) only. Judgments of learning were made by participants after each practice block and prior to all learning tests to examine metacognitive predictions of the degree to which the task had been mastered. There were no performance differences between groups in all experimental phases (p's >.05); however, groups differed in perceptions of learning. In all phases, the KR-Good group showed illusions of competence (Jacoby et al., 1994) with an inflated sense of learning, yet a depressed perception was found in the KR-Bad group (p's
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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 | 0.005 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".