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Record W3125682745 · doi:10.1371/journal.pone.0054632

Serotonergic Genotypes, Neuroticism, and Financial Choices

2013· article· en· W3125682745 on OpenAlexaff
Camelia M. Kuhnen, Gregory R. Samanez‐Larkin, Brian Knutson

Bibliographic record

VenuePLoS ONE · 2013
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Literacy, Pension, Retirement Analysis
Canadian institutionsKellogg's (Canada)
FundersNational Institute on AgingFINRA Investor Education Foundation
KeywordsNeuroticismSerotonin transporterAlleleSerotonergicBig Five personality traitsPersonalityTraitPsychologyMediationCognitionFinanceGenotypeBiologyGeneticsSerotoninGeneEconomicsSocial psychologyNeuroscience

Abstract

fetched live from OpenAlex

Life financial outcomes carry a significant heritable component, but the mechanisms by which genes influence financial choices remain unclear. Focusing on a polymorphism in the promoter region of the serotonin transporter gene (5-HTTLPR), we found that individuals possessing the short allele of this gene invested less in equities, were less engaged in actively making investment decisions, and had fewer credit lines. Short allele carriers also showed higher levels of the personality trait neuroticism, despite not differing from others with respect to cognitive skills, education, or wealth. Mediation analysis suggested that the presence of the 5-HTTLPR short allele decreased real life measures of financial risk taking through its influence on neuroticism. These findings show that 5-HTTLPR short allele carriers avoid risky and complex financial choices due to negative emotional reactions, and have implications for understanding and managing individual differences in financial choice.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0020.000

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 head0.024
GPT teacher head0.186
Teacher spread0.162 · 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 source (direct Gemma or distilled Codex), 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

Citations69
Published2013
Admission routes1
Has abstractyes

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Same venuePLoS ONESame topicFinancial Literacy, Pension, Retirement AnalysisFrench-language works237,207