Bibliographic record
Abstract
Abstract This chapter will argue that impulse control disorders, including addiction, are the product of an imbalance between two separate but interacting neural systems: (1) an impulsive amygdala-striatum–dependent neural system that promotes automatic and habitual behaviors and (2) a reflective prefrontal cortex–dependent neural system for decision making, forecasting the future consequences of a behavior, and inhibitory control. The reflective system controls the impulsive system via several mechanisms. However, this control is not absolute; hyperactivity within the impulsive system can override the reflective system. While most prior research has focused on the impulsive system (especially the ventral striatum and its mesolimbic dopamine projection) in promoting the motivation and drive to seek drugs, or on the reflective system (prefrontal cortex) and its mechanisms for decision making and impulse control, more recent evidence suggests that a largely overlooked structure, namely the insula, plays a key role in maintaining poor impulse control, including addiction. This review highlights the potential functional role the insula plays in addiction. We propose that the insula translates bottom-up, interoceptive signals into what subjectively may be experienced as an urge or craving, which in turn potentiates the activity of the impulsive system and/or weakens or hijacks the goal-driven cognitive resources that are needed for the normal operation of the reflective system.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".