Drug addiction and alteration of decision making process
Bibliographic record
Abstract
Beyond cigarette consumption, nicotine addiction also affects general cognitive functions and decision making processes, with for example, a tendency for smokers to prefer immediate small rewards over bigger, delayed ones. Numerous theories have been developed to model addiction, but an important framework states that addictive drugs modify the activity of dopamine (DA) neurons in reward signaling and in decision making. A powerful computational theory relies on unexpected reward for generating phasic DA release, which acts as teaching signals for appropriate learning and behavioral conditioning. In our lab, we have developed approaches in mice to investigate, from the molecular to the cognitive level, the mechanisms underlying nicotine addiction and of the resulting modifications of the decision making system and individual behaviors. We have dissected how nicotine, through its action on DA cells modifies different traits of an individual, from its reaction to stress, its social behavior or its exploration / exploitation balance. Addiction can thus be viewed as the result of a maladaptive decision process, but may also be considered as a particular and extreme situation illustrating the impact of DA dynamics modifications on personality.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".