Fast-tracking improvements of metacognitive assessments of visual working memory
Bibliographic record
Abstract
Not only is visual working memory (VWM) limited in capacity, some VWM representations may be maintained inaccurately even though we feel confident about them. Given that such confident errors can cause severe costs (e.g., traffic accidents), we tested a new approach to improve observers’ insights into the accuracy of their representations. Previously, we successfully reduced confident errors through a 1.5-hour-long VWM training during which participants received performance feedback based on the accuracy of metacognitive assessment of their VWM representations. Participants remembered a briefly presented array of six colored squares, then reported each item with their confidence in the accuracy of their report. Critically, they received 10, 5, or 0 points for an accurate VWM report coupled with high, low, or no confidence, respectively. Conversely, they lost 10, 5, or 0 points for inaccurate responses coupled with high, low, or no confidence. This training reduced the occurrence of confident errors (i.e., errors coupled with high confidence). Interestingly, training benefits emerged in the first several minutes of training. Thus, in the current study, we tested whether shorter training (10 mins) was sufficient to produce a training benefit that also generalizes across different stimulus types. Specifically, participants (n = 78) performed two VWM tasks (10 mins each) where they remembered an array of six colored squares or oriented bars and reported each item with their confidence (high, low, or no confidence). After measuring baseline performance, participants repeated the color or orientation VWM task with feedback. Participants then repeated the two VWM tasks without feedback to assess the training benefit and its stimulus generalizability. Here, despite a shorter training session, participants reduced confident errors during and after training relative to baseline. This benefit also generalized to the untrained stimulus. Taken together, our results demonstrate rapid and stimulus-general improvement of metacognitive assessment of VWM representations.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".