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Record W2951816886 · doi:10.7554/elife.44939

The dorsomedial prefrontal cortex computes task-invariant relative subjective value for self and other

2019· article· en· W2951816886 on OpenAlexfundno aff
Matthew Piva, Kayla Velnoskey, Ruonan Jia, Amrita R. Nair, Ifat Levy, Steve W. C. Chang

Bibliographic record

VenueeLife · 2019
Typearticle
Languageen
FieldNeuroscience
TopicNeural and Behavioral Psychology Studies
Canadian institutionsnot available
FundersNational Center for Advancing Translational SciencesNatural Sciences and Engineering Research Council of Canada
KeywordsVentromedial prefrontal cortexPrefrontal cortexPsychologyCognitive psychologyUnivariateValuation (finance)CognitionMultivariate statisticsNeuroscienceComputer science

Abstract

fetched live from OpenAlex

Few studies have addressed the neural computations underlying decisions made for others despite the importance of this ubiquitous behavior. Using participant-specific behavioral modeling with univariate and multivariate fMRI approaches, we investigated the neural correlates of decision-making for self and other in two independent tasks, including intertemporal and risky choice. Modeling subjective valuation indicated that participants distinguished between themselves and others with dissimilar preferences. Activity in the dorsomedial prefrontal cortex (dmPFC) and ventromedial prefrontal cortex (vmPFC) was consistently modulated by relative subjective value. Multi-voxel pattern analysis indicated that activity in the dmPFC uniquely encoded relative subjective value and generalized across self and other and across both tasks. Furthermore, agent cross-decoding accuracy between self and other in the dmPFC was related to self-reported social attitudes. These findings indicate that the dmPFC emerges as a medial prefrontal node that utilizes a task-invariant mechanism for computing relative subjective value for self and other.

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 score0.671
Threshold uncertainty score0.295

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.051
GPT teacher head0.323
Teacher spread0.272 · 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

Citations84
Published2019
Admission routes1
Has abstractyes

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