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Record W2262450756 · doi:10.1038/ncomms10120

Testing necessary regional frontal contributions to value assessment and fixation-based updating

2015· article· en· W2262450756 on OpenAlexafffund
Avinash R. Vaidya, Lesley K. Fellows

Bibliographic record

VenueNature Communications · 2015
Typearticle
Languageen
FieldNeuroscience
TopicNeural and Behavioral Psychology Studies
Canadian institutionsMcGill UniversityMontreal Neurological Institute and Hospital
FundersCanadian Institutes of Health ResearchMcGill University
KeywordsValue (mathematics)Computer scienceFixation (population genetics)Machine learningBiologyGenetics

Abstract

fetched live from OpenAlex

Value-based decisions are biased by the time people spend viewing each option: Options fixated longer are chosen more often, even when previously rated as less appealing. This bias is thought to reflect 'value updating' as new evidence is accumulated. Prior work has shown that ventromedial prefrontal cortex (PFC) carries a fixation-dependent value comparison signal, while other studies implicate dorsomedial PFC in representing the value of alternative options. Here, we test whether these regions are necessary for fixation-related value updating in 33 people with frontal lobe damage and 27 healthy controls performing a simple choice task. We show that damage to dorsomedial PFC leads to an exaggerated influence of fixations on choice, while damage to ventromedial or lateral PFC has no effect on this bias. These findings suggest a critical role for dorsomedial, and not ventromedial PFC, in mediating the relative influence of current fixations and a priori value on choice.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.220
GPT teacher head0.466
Teacher spread0.247 · 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

Citations60
Published2015
Admission routes2
Has abstractyes

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