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Record W4386249323 · doi:10.1167/jov.23.9.5373

Spontaneous detection of Visual Working Memory failures and subsequent performance recovery

2023· article· en· W4386249323 on OpenAlexaff
Olga Kozlova, Keisuke Fukuda

Bibliographic record

VenueJournal of Vision · 2023
Typearticle
Languageen
FieldNeuroscience
TopicNeural and Behavioral Psychology Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsWorking memoryPsychologyTask (project management)Stimulus (psychology)Cognitive psychologyAudiologySocial psychologyComputer scienceCognitionMedicinePsychiatry

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.076
GPT teacher head0.358
Teacher spread0.282 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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