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Record W2005019135 · doi:10.1037/a0019466

Positive mood effects on delay discounting.

2010· article· en· W2005019135 on OpenAlexaff
Jacob B. Hirsh, Alex Guindon, Dominique Morisano, Jordan B. Peterson

Bibliographic record

VenueEmotion · 2010
Typearticle
Languageen
FieldDecision Sciences
TopicDecision-Making and Behavioral Economics
Canadian institutionsCentre for Addiction and Mental HealthUniversity of Toronto
Fundersnot available
KeywordsPsychologyExtraversion and introversionDelay discountingMoodAffect (linguistics)Delay of gratificationDiscountingPersonalityDevelopmental psychologyTemporal discountingSituational ethicsSocial psychologyImpulsivityBig Five personality traits

Abstract

fetched live from OpenAlex

Delay discounting is the process by which the value of an expected reward decreases as the delay to obtaining that reward increases. Individuals with higher discounting rates tend to prefer smaller immediate rewards over larger delayed rewards. Previous research has indicated that personality can influence an individual's discounting rates, with higher levels of Extraversion predicting a preference for immediate gratification. The current study examined how this relationship would be influenced by situational mood inductions. While main effects were observed for both Extraversion and cognitive ability in the prediction of discounting rates, a significant interaction was also observed between Extraversion and positive affect. Extraverted individuals were more likely to prefer an immediate reward when first put in a positive mood. Extraverts thus appear particularly sensitive to impulsive, incentive-reward-driven behavior by temperament and by situational factors heightening positive affect.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.038
GPT teacher head0.362
Teacher spread0.324 · 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

Citations95
Published2010
Admission routes1
Has abstractyes

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