Pleasure in decision‐making situations: politics and gambling
Bibliographic record
Abstract
This work investigates whether pleasure influences political and gambling decisions. Participants received two questionnaires. On Questionnaire 1, they rated the pleasure/displeasure of a series of items: social and political issues in Experiment 1, winning bets in Experiment 2, and losing bets in Experiment 3. On Questionnaire 2, they indicated the items that they would actually choose in real life. Their choices were then compared with their hedonic ratings on Questionnaire 1. Results showed that participants tended to choose those items they had most highly rated for pleasure on Questionnaire 1. In all cases, the selected outcomes were higher than chance, and thus tended to maximize pleasure, but were significantly lower than the maximum possible, indicating the presence of non‐hedonic criteria. The tendency to maximize pleasure was independent of age, gender, political opinions, and gambling propensity in real life. The results of all three experiments support the hypothesis that decisions are made predominantly, though not exclusively, in the hedonic dimension of conscious experience. A fourth experiment was set to answer a methodological question: a delay of 77 ± 3 days was placed between Questionnaires 1 and 2. The results were similar to those of Experiments 1–3, thus answering the concern.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".