Faculty Opinions recommendation of Individual differences in the attribution of incentive salience to reward-related cues: Implications for addiction.
Bibliographic record
Abstract
Drugs of abuse acquire different degrees of control over thoughts and actions based not only on the effects of drugs themselves, but also on predispositions of the individual.Those individuals who become addicted are unable to shift their thoughts and actions away from drugs and drug-associated stimuli.Thus in addicts, exposure to places or things (cues) that have been previously associated with drug-taking often instigates renewed drug-taking.We and others have postulated that drugassociated cues acquire the ability to maintain and instigate drug-taking behavior in part because they acquire incentive motivational properties through Pavlovian (stimulus-stimulus) learning.In the case of compulsive behavioral disorders, including addiction, such cues may be attributed with pathological incentive value ("incentive salience").For this reason, we have recently begun to explore individual differences in the tendency to attribute incentive salience to cues that predict rewards.When discrete cues are associated with the non-contingent delivery of food or drug rewards some animals come to quickly approach and engage the cue even if it is located at a distance from where the reward will be delivered.In these animals the reward-predictive cue itself becomes attractive, eliciting approach towards it, presumably because it is attributed with incentive salience.Animals that develop this type of conditional response are called "sign-trackers".Other animals, "goal-trackers", do not approach the reward-predictive cue, but upon cue presentation they immediately go to the location where food will be delivered (the "goal").For goal-trackers the reward-predictive cue is not attractive, presumably because it is not attributed with incentive salience.We review here preliminary data suggesting that these individual differences in the tendency to attribute incentive salience to cues predictive of reward may confer vulnerability or resistance to compulsive behavioral disorders, including addiction.It will be important, therefore, to study how environmental, neurobiological and genetic interactions determine the extent to which individuals attribute incentive value to reward-predictive stimuli.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.009 | 0.004 |
| Insufficient payload (model declined to judge) | 0.635 | 0.389 |
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".