Providing knowledge of results based on an absolute performance bandwidth results in illusions of competency
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it