The priming effect of rewarding brain stimulation in rats depends on both the cost and strength of reward but survives blockade of D2‐like dopamine receptors
Bibliographic record
Abstract
Receipt of an intense reward boosts motivation to work for more of that reward. This phenomenon is called the priming effect of rewards. Using a novel measurement method, we show that the priming effect of rewarding electrical brain stimulation depends on the cost, as well as on the strength, of the anticipated reward. Previous research on the priming effect of electrical brain stimulation utilized a runway paradigm in which running speed serves as the measure of motivation. In the present study, the measure of motivation was the vigour with which rats executed a two-lever response chain, in a standard operant-conditioning chamber, to earn rewarding electrical stimulation of the lateral hypothalamus. In a second experiment, we introduced a modification that entails self-administered priming stimulation and alternating blocks of primed and unprimed trials. Reliable, consistent priming effects of substantial magnitude were obtained in the modified paradigm, which is closely analogous to the runway paradigm. In a third experiment, the modified paradigm served to assess the dependence of the priming effect on dopamine D2-like receptors. The priming effect proved resilient to the effect of eticlopride, a selective D2-like receptor antagonist. These results are discussed within the framework of a new model of brain reward circuitry in which non-dopaminergic medial forebrain bundle fibers and dopamine axons provide parallel inputs to the final common paths for reward and incentive 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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".