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Record W2132707294 · doi:10.1073/pnas.0805423105

Neural predictors of moment-to-moment fluctuations in cognitive flexibility

2008· article· en· W2132707294 on OpenAlexfundno aff
Andrew B. Leber, Nicholas B. Turk‐Browne, Marvin M. Chun

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2008
Typearticle
Languageen
FieldNeuroscience
TopicNeural and Behavioral Psychology Studies
Canadian institutionsnot available
FundersNational Eye InstituteNational Institute of Mental HealthNatural Sciences and Engineering Research Council of CanadaNational Institutes of Health
KeywordsCognitive flexibilityFlexibility (engineering)Prefrontal cortexAnterior cingulate cortexTask (project management)Posterior parietal cortexCognitionPsychologyTask switchingNeuroscienceCognitive psychologyMoment (physics)Computer sciencePhysicsMathematicsStatistics

Abstract

fetched live from OpenAlex

Cognitive flexibility is a crucial human ability allowing efficient adaptation to changing task challenges. Although a person's degree of flexibility can vary from moment to moment, the conditions regulating such fluctuations are not well understood. Using a task-switching procedure with fMRI, we found several brain regions in which neural activity preceding each trial predicted subsequent cognitive flexibility. Specifically, as pretrial activity increased, performance improved on trials when the task switched but did not improve when the task repeated. Regions from which flexibility could be predicted reliably included the basal ganglia, anterior cingulate cortex, prefrontal cortex, and posterior parietal cortex. Although further analysis revealed similarities across the regions in how flexibility was predicted, results supported the existence of multiple independent sources of prediction. These results reveal distinct neural mechanisms underlying fluctuations in cognitive flexibility.

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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.316
Threshold uncertainty score0.693

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.252
GPT teacher head0.418
Teacher spread0.165 · 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

Citations180
Published2008
Admission routes1
Has abstractyes

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