Spontaneous detection of Visual Working Memory failures and subsequent performance recovery
Bibliographic record
Abstract
Visual Working Memory (VWM) performance fluctuates from moment to moment with periodic failures. To test whether individuals are aware of upcoming failures, we developed a VWM bet task in which participants made a trial-by-trial prediction on VWM performance. Previously, we demonstrated that 1) individuals were unaware of upcoming VWM failures, and 2) it takes time for VWM performance to fully recover from failures. Interestingly, when informed of the failures through feedback, participants adjusted their following bets according to their recovering performance. Thus, in this study, we tested whether the metacognitive awareness of VWM performance recovery was induced by the feedback signaling the occurrence of a VWM failure. More precisely, in each trial, participants were first asked to place a bet on how many of four colored squares they would be able to remember. Then, they remembered the colored squares over a short retention interval and recalled each stimulus. When participants accurately recalled the same or a greater number of squares as their bets, they earned the points associated with their bets. To discourage overconfident bets, participants gained zero points when they recalled less than their bets. To discourage underconfident bets, participants were informed that the task would end as soon as they earned a pre-determined number of points (i.e., 60 points). Importantly, participants were not shown the points they earned during each trial. If feedback is necessary for participants to realize the occurrence of VWM failures and the subsequent slow recovery of VWM performance, they should not reduce their bets following the failures. Contrary to this prediction, we found that participants reduced their bets in accordance with the slow recovery of VWM performance. Our result, thus, demonstrates that individuals spontaneously recognize the occurrence of VWM failures and the subsequent slow recovery of 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 | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".