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Record W2562585953 · doi:10.1162/jocn_a_01088

The Ventromedial Frontal Lobe Contributes to Forming Effective Solutions to Real-world Problems

2016· article· en· W2562585953 on OpenAlexaff
Sarah L. Peters, Lesley K. Fellows, Signy Sheldon

Bibliographic record

VenueJournal of Cognitive Neuroscience · 2016
Typearticle
Languageen
FieldNeuroscience
TopicNeural and Behavioral Psychology Studies
Canadian institutionsMcGill University
Fundersnot available
KeywordsPsychologyFrontal lobeVentromedial prefrontal cortexCognitive psychologyNeuroscienceCognition

Abstract

fetched live from OpenAlex

Although the ventromedial frontal lobe (VMF) has been implicated in several complex cognitive tasks such as decision-making and problem solving, the processes for which this region is critical remain unclear. Laboratory studies have largely focused on how the VMF contributes to decision-making when outcomes or options are provided, but in the real world generating appropriate options is likely a crucial and rate-limiting initial step. Here, we determined how VMF damage affected the option generation phase of naturalistic problem solving. A group of patients with VMF damage and two controls groups-age-matched healthy participants and patients with frontal damage sparing VMF-were asked to generate as many options as possible to five scenarios depicting open-ended, real-world problems (e.g., having lunch at a restaurant and forgetting your wallet at home). Both the number of options and the effectiveness of each option generated were examined. Damage to VMF led to a significant reduction in both the number of options produced across all problem-solving scenarios and the ability to generate effective options, most notably for scenarios that were social in nature. We discuss these findings in terms of the mechanisms by which the VMF may contribute to option generation, focusing on proposals suggesting this region is important for integrating subjective value and retrieving schematic representations.

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)0.005
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 score0.363
Threshold uncertainty score0.705

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
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 head0.090
GPT teacher head0.370
Teacher spread0.281 · 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

Citations27
Published2016
Admission routes1
Has abstractyes

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