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Record W4283524093 · doi:10.1101/2022.06.21.496956

Blocking D2/D3 dopamine receptors increases volatility of beliefs when we learn to trust others

2022· preprint· en· W4283524093 on OpenAlexaff
Nace Mikuš, Christoph Eisenegger, Christoph Mathys, Luke Clark, Ulrich Müller, Trevor W. Robbins, Claus Lamm, Michael Naef

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2022
Typepreprint
Languageen
FieldSocial Sciences
TopicExperimental Behavioral Economics Studies
Canadian institutionsUniversity of British Columbia
FundersNIHR Cambridge Biomedical Research CentreVienna Science and Technology FundMedical Research CouncilNational Institute for Health and Care ResearchSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Science Foundation
KeywordsSulpirideDopaminePsychologyDopamine receptor D2Prosocial behaviorVolatility (finance)Dopamine receptorAntagonistSocial psychologyProxy (statistics)ReceptorInternal medicineComputer scienceMedicineEconometricsNeuroscienceEconomicsDopaminergicMachine learning

Abstract

fetched live from OpenAlex

Abstract The ability to flexibly adjust beliefs about other people is crucial for human social functioning. Dopamine has been proposed to regulate the precision of beliefs, but direct behavioural evidence of this is lacking. We investigated how a relatively high dose of the selective D2/D3 dopamine receptor antagonist sulpiride impacts learning about other people’s prosocial attitudes in a repeated trust game. Using a Bayesian model of belief updating, we show that sulpiride increased the volatility of beliefs, which led to higher precision-weights on prediction errors. This effect was entirely driven by participants with genetically conferring higher dopamine availability (Taq1a polymorphism). Higher precision weights were reflected in higher reciprocal behaviour in the repeated trust game but not in single-round trust games. This finding suggests that antipsychotic medication might acutely reduce rigidity of pathological beliefs.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.539
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.028
GPT teacher head0.277
Teacher spread0.249 · 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.

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

Citations4
Published2022
Admission routes1
Has abstractyes

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