More liberal and less sensitive: Individual differences in visual working memory capacity predicts the metacognitive assessment of representational accuracy
Bibliographic record
Abstract
Visual working memory (VWM) allows us to actively represent task-relevant visual information so that we can use it to guide our behavior. However, not only is its capacity limited, but its representational accuracy also varies. Thus, to avoid guiding our behaviors on inaccurate VWM representations, we need to metacognitively assess the accuracy of VWM representations. Here, across four experiments (total n = 663), we demonstrated that humans’ metacognitive assessment for VWM representations are suboptimal such that our VWM report is not always accurate even when endorsed with 100% confidence in its accuracy. Furthermore, the poor metacognitive assessment was particularly evident in low-capacity individuals, and it stemmed from two dissociable mechanisms, namely overly liberal confidence assignment and reduced metacognitive sensitivity to VWM representational accuracy. Taken together, by elucidating multiple mechanisms of overconfident errors, our results offer a novel insight into the nature of individual differences in VWM performance.
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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 | 0.002 | 0.021 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".