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Record W4401152354 · doi:10.3389/fcogn.2024.1404909

Neural evidence of deprioritizing to-be-forgotten information in visual working memory

2024· article· en· W4401152354 on OpenAlexaff
Katherine Moen, Melissa R. Beck, Scarlett Horner, Steven G. Greening

Bibliographic record

VenueFrontiers in Cognition · 2024
Typearticle
Languageen
FieldNeuroscience
TopicNeural and Behavioral Psychology Studies
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsForgettingMotivated forgettingWorking memoryPsychologyFunctional magnetic resonance imagingCognitive psychologyStimulus (psychology)CognitionVisual memoryNeurosciencePrefrontal cortexCued speech

Abstract

fetched live from OpenAlex

Introduction: Although evidence supports the effective use of a cue to forget an encoded stimulus, the mechanisms of this forgetting are not well understood. Evidence from item-method directed forgetting in long-term memory reveals greater prefrontal and parietal activation for information that is cued to be forgotten. Activation in those brain regions is typically associated with increased effort and cognitive control. Method: To test the mechanism of directed forgetting in visual working memory, we used stimuli that rely on distinct brain regions such as faces and buildings and varied memory stability. Participants completed a directed forgetting task with faces and buildings, and memory stability was manipulated by presenting some stimuli repeatedly throughout the study, and other stimuli were only presented once. Results and discussion: Functional magnetic resonance imaging (fMRI) results from the parahippocampal place area suggest that to-be-remembered buildings elicit higher activation than to-be-forgotten buildings. In addition, dorsolateral prefrontal cortex activation changed throughout the trial period, possibly suggesting that the cue led to information being dropped from visual working memory, or through a shift in attention, as occurs with the retro-cue paradigm. Several explanations for these results are discussed.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.607
Threshold uncertainty score0.389

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.148
GPT teacher head0.385
Teacher spread0.237 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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
Published2024
Admission routes1
Has abstractyes

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