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Record W2613965546 · doi:10.1111/psyp.12883

Brain activities associated with learning of the Monty Hall Dilemma task

2017· article· en· W2613965546 on OpenAlexaff
Takahiro Hirao, Timothy I. Murphy, Hiroaki Masaki

Bibliographic record

VenuePsychophysiology · 2017
Typearticle
Languageen
FieldDecision Sciences
TopicDecision-Making and Behavioral Economics
Canadian institutionsBrock University
FundersJapan Society for the Promotion of ScienceWaseda UniversityMinistry of Education, Culture, Sports, Science and Technology
KeywordsCounterintuitivePsychologyDilemmaNegativity effectTask (project management)Cognitive psychologyPrisoner's dilemmaProbabilistic logicSocial psychologyStimulus (psychology)Anticipation (artificial intelligence)Artificial intelligenceComputer science

Abstract

fetched live from OpenAlex

The Monty Hall Dilemma (MHD) poses a counterintuitive probabilistic problem to the players of this game. In the MHD task, a participant chooses one of three options where only one contains a reward. After one of the unchosen options (always no reward) is disclosed, the participant is asked to make a final decision: either change to the remaining option or stick with their first choice. Although the probability of winning if they change is higher (2/3) compared to sticking with their first choice (1/3), most people stick with their original selection and often lose. In accordance with previous research, repetitive exposure to the MHD task increases the change behavior without any obvious understanding of the mathematical reasons why changing increases their chance of being rewarded. We recorded the stimulus-preceding negativity (SPN), an ERP that might reflect the informative value of the feedback. In the second half of the task, feedback was predicted to be less informative because learning had taken place. Indeed, the SPN amplitude became smaller over the frontal region. Also, the SPN amplitude was larger for change than for stick trials. These results suggest that learning in the MHD might be manifest in affective-motivational anticipation as indicated by the SPN.

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)0.004
Version: codex-gemma-dda1882f352aValidation 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 score0.681
Threshold uncertainty score0.453

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0020.000
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 head0.088
GPT teacher head0.383
Teacher spread0.295 · 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 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

Citations10
Published2017
Admission routes1
Has abstractyes

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