Insight Into the Relationship Between Impulsivity and Substance Abuse From Studies Using Animal Models
Bibliographic record
Abstract
Drug use disorders are often accompanied by deficits in the capacity to efficiently process reward-related information and to monitor, suppress, or override reward-controlled behavior when goals are in conflict with aversive or immediate outcomes. This emerging deficit in behavioral flexibility and impulse control may be a central component of the progression to addiction, as behavior becomes increasingly driven by drugs and drug-associated cues at the expense of more advantageous activities. Understanding how neural mechanisms implicated in impulse control are affected by addictive drugs may therefore prove a useful strategy in the search for new treatment options. Animal models of impulsivity and addiction could make a significant contribution to this endeavor. Here, some of the more common behavioral paradigms used to measure different aspects of impulsivity across species are outlined, and the importance of the response to reward-paired cues in such paradigms is discussed. Naturally occurring differences in forms of impulsivity have been found to be predictive of future drug self-administration, but drug exposure can also increase impulsive responding. Such data are in keeping with the suggestion that impulsivity may contribute to multiple stages within the spiral of addiction. From a neurobiological perspective, converging evidence from rat, monkey, and human studies suggest that compromised functioning within the orbitofrontal cortex may critically contribute to the cognitive sequelae of drug abuse. Changes in gene transcription and protein expression within this region may provide insight into the mechanism underlying drug-induced cortical hypofunction, reflecting new molecular targets for the treatment of uncontrolled drug-seeking and drug-taking behavior.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".