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Record W2077003671 · doi:10.1186/1744-9081-3-45

Decision making: rational or hedonic?

2007· article· en· W2077003671 on OpenAlexafffund
Michel Cabanac, Marie‐Claude Bonniot‐Cabanac

Bibliographic record

VenueBehavioral and Brain Functions · 2007
Typearticle
Languageen
FieldDecision Sciences
TopicDecision-Making and Behavioral Economics
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPleasurePsychologyDimension (graph theory)Social psychologyHedonismReading (process)

Abstract

fetched live from OpenAlex

Three experiments studied the hedonicity of decision making. Participants rated their pleasure/displeasure while reading item-sentences describing political and social problems followed by different decisions (Questionnaire 1). Questionnaire 2 was multiple-choice, grouping the items from Questionnaire 1. In Experiment 1, participants answered Questionnaire 2 rapidly or slowly. Both groups selected what they had rated as pleasant, but the 'leisurely' group maximized pleasure less. In Experiment 2, participants selected the most rational responses. The selected behaviors were pleasant but less than spontaneous behaviors. In Experiment 3, Questionnaire 2 was presented once with items grouped by theme, and once with items shuffled. Participants maximized the pleasure of their decisions, but the items selected on Questionnaires 2 were different when presented in different order. All groups maximized pleasure equally in their decisions.These results support that decisions are made predominantly in the hedonic dimension of consciousness.

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)0.005
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.020
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0010.001
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 head0.176
GPT teacher head0.440
Teacher spread0.264 · 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 designTheoretical or conceptual
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

Citations34
Published2007
Admission routes2
Has abstractyes

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