Interoception moderates the relation between alexithymia and risky-choices in a framing task: A proposal of two-stage model of decision-making
Bibliographic record
Abstract
Decision-making depends on the context (frame) in which questions and alternatives are presented. Moreover, research has showed that the ability to detect bodily sensations (interoception) and being able to attribute these changes to emotions correctly (alexithymia) influence how we make decisions. The aim of the present research was to study how interoception and alexithymia might affect the Framing effect (FE), a cognitive bias closely related to emotional system. 42 healthy participants completed the Risky-choice Framing task and their interoception and alexithymia levels were measured. Results showed that the participants were more risk-taking under the negative frames in comparison to the positive ones. In addition, we found that alexithymia and interoception were negatively and positively correlated with the FE, respectively. Finally, the moderation analyses revealed that alexithymia predicted a lower FE only when the interoception was high. Based on previous literature and in our results, we propose a two-stage model of intuitive decision-making.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".