Interoception Drives Increased Rational Decision-Making in Meditators Playing the Ultimatum Game
Bibliographic record
Abstract
Human decision-making is often conceptualized as a competition between cognitive and emotional processes in the brain. Deviations from rational processes are believed to derive from inclusion of emotional factors in decision-making. Here, we investigate whether experienced Buddhist meditators are better equipped to regulate emotional processes compared with controls during economic decision-making in the Ultimatum Game. We show that meditators accept unfair offers on more than half of the trials, whereas controls only accept unfair offers on one-quarter of the trials. By applying fMRI we show that controls recruit the anterior insula during unfair offers. Such responses are powerful predictors of rejecting offers in social interaction. By contrast, meditators display attenuated activity in high-level emotional representations of the anterior insula and increased activity in the low-level interoceptive representations of the posterior insula. In addition we show that a subset of control participants who play rationally (i.e., accepts >85% unfair offers) recruits the dorsolateral prefrontal cortex presumably reflecting increased cognitive demands, whereas rational meditators by contrast display elevated activity in the somatosensory cortex and posterior superior temporal cortex. In summary, when assessing unfairness in the Ultimatum Game, meditators activate a different network of brain areas compared with controls enabling them to uncouple negative emotional reactions from their behavior. These findings highlight the clinically and socially important possibility that sustained training in mindfulness meditation may impact distinct domains of human 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".