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Record W1139543291 · doi:10.1167/15.9.18

Updating visual working memory in the change detection paradigm

2015· article· en· W1139543291 on OpenAlexafffund
Yoav Kessler, Rachel Rac-Lubashevsky, Carmel Lichtstein, Hadar Markus, Almog Simchon, Morris Moscovitch

Bibliographic record

VenueJournal of Vision · 2015
Typearticle
Languageen
FieldNeuroscience
TopicNeural and Behavioral Psychology Studies
Canadian institutionsBaycrest HospitalUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaEuropean Commission
KeywordsWorking memorySet (abstract data type)Computer scienceTask (project management)Visual memoryEvent-related potentialCognitive psychologyVisual short-term memoryChange detectionIconic memoryPsychologyCognitionArtificial intelligenceNeuroscience

Abstract

fetched live from OpenAlex

An updating version of a visual change detection paradigm was used to investigate the behavioral outcomes and event-related potential (ERP) correlates of visual working memory updating. In each trial, participants were either presented with a memory array followed by a test probe, or with two successive memory arrays. Participants were instructed to update their working memory with the information in the second array. The second array differed from the first one in all, some, or none of the items. When a subset of the items was updated, the probe could appear in the location of a repeated item or of an updated item. Two experiments are reported, using set-sizes of six and two items, respectively. Both experiments show a benefit for probing a repeated item compared to an updated item. This result is consistent with an item-specific updating process. Experiment 2 also revealed two distinct updating-related ERP components, observed in both contralateral and ipsilateral visual hemifields. Frontal electrodes were sensitive to the number of changed items in the array. This ERP component was interpreted as reflecting the modification of information in working memory. Lateral-posterior electrodes only showed a difference between a full repetition of the array and updating, regardless of the number of updated items. This component was interpreted as reflecting attention to task-relevant information rather than the updating process per se. The finding of item-specific updating supports discrete-item architecture models of working memory.

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)0.001
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 score0.599
Threshold uncertainty score0.137

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.315
GPT teacher head0.442
Teacher spread0.128 · 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

Citations9
Published2015
Admission routes2
Has abstractyes

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