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Record W2942038618 · doi:10.1037/emo0000589

Common variants of the oxytocin receptor gene do not predict the positive mood benefits of prosocial spending.

2019· article· en· W2942038618 on OpenAlexaff
Ashley V. Whillans, Lara B. Aknin, Colin J.D. Ross, Lihan Chen, Frances S. Chen

Bibliographic record

VenueEmotion · 2019
Typearticle
Languageen
FieldPsychology
TopicNeuroendocrine regulation and behavior
Canadian institutionsSimon Fraser UniversityUniversity of British Columbia
Fundersnot available
KeywordsOxytocin receptorGenerosityPsychologyProsocial behaviorPsycINFOMoodDevelopmental psychologyClinical psychologyOxytocinSocial psychologyMEDLINE

Abstract

fetched live from OpenAlex

Who benefits most from helping others? Previous research suggests that common polymorphisms of the oxytocin receptor gene (OXTR) predict whether people behave generously and experience increases in positive mood in response to socially focused experiences in daily life. Building on these findings, we conducted an experiment with a large, ethnically homogenous sample (N = 437) to examine whether individual differences in three frequently studied single nucleotide polymorphisms of OXTR (rs53576, rs2268498, rs2254298) also predict differences in the positive mood benefits of financial generosity. Consistent with past research, participants who were randomly assigned to purchase items for others (vs. themselves) reported greater positive affect. Contrary to predictions, using Bayesian statistics, we found conclusive evidence that the benefits of generosity were not moderated by individual differences in OXTR single nucleotide polymorphisms. The current work highlights the importance of publishing null results to build cumulative knowledge linking neurobiological factors to positive emotional experiences. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.336
Threshold uncertainty score0.886

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)0.0010.000

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 head0.023
GPT teacher head0.291
Teacher spread0.269 · 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

Citations13
Published2019
Admission routes1
Has abstractyes

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