Distinctive Neural Substrates of low and high Risky Decision Making: Evidence from the Balloon Analog Risk Task
Bibliographic record
Abstract
Abstract Human beings exhibit varying risk-taking behaviors in response to different risk levels. Despite numerous studies on risk-taking in decision-making, the neural mechanisms of decision-making regarding risk levels remains unclear. To investigate the neural correlates of individual differences in risk-taking under different risk-levels, we analyzed behavioral data of the Balloon Analogue Risk Task (BART) and resting-state functional Magnetic Resonance Imaging (rs-fMRI) data of healthy participants (22–39 years, N = 93) from the University of California, Los Angeles Consortium for Neuropsychiatric Phenomics dataset. In the BART, the participants decided to pump for more points or stop pumping to avoid explosion of the balloons, where the risk level was manipulated by the explosion likelihood which was distinguished by the balloon color (blue for low-, red for high- risk condition). Compared with low-risk condition, the participants pumped less number, exploded more balloons, and showed more variability in pump numbers in high-risk condition, demonstrating the effective manipulation of the risky level. Next, resting state features and functional connectivity (rsFC) strength were associated with behavioral measures in low- and high-risk conditions. We found that the explosion number of balloons were correlated with the low frequency fluctuations (ALFF) in the left dorsolateral prefrontal cortex (L. DLPFC), the rsFC strength between L. DLPFC and the left anterior orbital gyrus in the low-risk condition. In the high-risk condition, we found variability in pump numbers was correlated with the ALFF in the left middle/superior frontal gyrus, the fractional ALFF (fALFF) in the medial segment of precentral gyrus (M. PrG), and the rsFC strength between the M. PrG and bilateral precentral gyrus. Our results highlighted significance of the L. DLPFC in lower risky decision making and the precentral gyrus in higher risky decision making, suggesting that distinctive neural correlates underlie the individual differences of decision-making under different risk level.
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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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".