Neural correlates of high-risk behavior tendencies and impulsivity in an emotional Go/NoGo fMRI task
Bibliographic record
Abstract
Improved neuroscientific understanding of high-risk behaviors such as alcohol binging, drug use, and unsafe sex will lead to therapeutic advances for high-risk groups. High-risk behavior often occurs in an emotionally-charged context, and behavioral inhibition and emotion regulation play important roles in risk-related decision making. High impulsivity is an important potential contributor to high-risk behavior tendencies. We explored the relationships between high-risk behavior tendencies, impulsivity, and fMRI brain activations in an emotional Go/NoGo task. This task presented emotional distractor pictures (aversive vs. neutral) simultaneously with Go/NoGo stimuli (square vs. circle) that required a button press or withholding of the press, respectively. Participants' risk behavior tendencies were assessed with the Cognitive Appraisal of Risky Events (CARE) scale. The Barratt Impulsivity Scale 11 (BIS) was used to assess participant impulsivity. Individuals with higher CARE risk scores exhibited reduced activation related to response inhibition (NoGo-Go) in right orbital frontal cortex (OFC) and ventromedial prefrontal cortex. These regions did not show a significant relationship with impulsivity scores. Conversely, more impulsive individuals showed reduced emotion-related activity (aversive-neutral distractors) in dorsomedial prefrontal cortex, perigenual anterior cingulate cortex, and right posterior OFC. There were distinct neural correlates of high-risk behavior tendency and impulsivity in terms of brain activity in the emotional Go/NoGo task. This dissociation supports the conception of high-risk behavior tendency as a distinct construct from that of impulsivity. Our results suggest that treatment for high-risk behavior may be more effective with a nuanced approach that does not conflate high impulsivity necessarily with high-risk behavior tendencies.
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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 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".