Network failures: When incentives trigger impulsive responses
Bibliographic record
Abstract
Adequate control of impulsive urges to act is demanded in everyday life but is impaired in neuropsychiatric conditions such as stimulant use disorder. Despite intensive research it remains unclear whether failures in impulse control are caused by impaired suppression of behavior or by the over invigoration of behavior by stimuli associated with salient incentives such as drugs, food, and money. We investigated failures in impulse control using functional magnetic resonance imaging (fMRI) to map the neural correlates of premature (impulsive) responses during the anticipation phase of the Monetary Incentive Delay (MID) task in healthy controls (HC), stimulant-dependent individuals (SDIs), and their unaffected first-degree siblings (SIB). We combined task-based fMRI analyses with dynamic causal modeling to show that failures of impulse control were associated with interactions between cingulo-opercular and dorsal striatal networks regardless of group status and incentive type. We further report that group-specific incentive salience plays a critical role in modulating impulsivity in SDIs since drug-related incentives specifically increased premature responding and shifted task modulation away from the dorsal striatal network to the cingulo-opercular network. Our findings thus indicate that impulsive actions are elicited by salient personally-relevant incentive stimuli and those such slips of action recruit a distinct fronto-striatal network.
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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 | 0.000 |
| 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".