The priming effect of rewards and the role of dopamine transmission
Bibliographic record
Abstract
After receiving a reward, motivation to obtain more is boosted. For example, a taste of chocolate drives me to want and consume more chocolate—sometimes to the point that I finish an entire bar! This phenomenon is called the priming effect of rewards. The priming effect of rewards has primarily been studied with electrical brain stimulation. Rats primed with brain stimulation have been shown to prefer brain stimulation over competing rewards. Additionally, they work harder for more rewarding brain stimulation. Although over half a century of research implicates dopamine transmission in reward and motivation, the priming effect may not depend on dopamine transmission. \nThis thesis investigated the priming effect of electrical brain stimulation and food and the role of dopamine transmission. First, expanding on the original work on the priming effect of electrical brain stimulation, we examined whether the priming effect depends on the strength and cost of reward. We showed that the priming effect of electrical brain stimulation is more likely to be observed when the reward intensity is high and the cost is low. Secondly, we investigated whether the priming effect generalizes to other rewards such as food. We demonstrated that food also elicits a priming effect. Lastly, it was studied whether dopamine transmission is necessary for the priming effect of electrical brain stimulation and food. We showed that the priming effect of those rewards persists following dopamine receptor antagonism. \nAlthough dopamine transmission is important for reward and motivation, the present thesis provides evidence that it may not be essential for the priming effect. This emphasizes the need to reconsider and investigate the role of non-dopamine systems in reward and motivation.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".