Validation and extension of the reward-mountain model
Bibliographic record
Abstract
The reward-mountain model relates the vigor of reward seeking to the strength and cost of reward. Application of this model provides information about the stage of processing at which manipulations such as drug administration, lesions, deprivation states, and optogenetic interventions act to alter reward seeking. The model has been updated by incorporation of new information about frequency following in the directly stimulated neurons responsible for brain stimulation reward and about the function that maps objective opportunity costs into subjective ones. The behavioral methods for applying the model have been updated and improved as well. To assess the impact of these changes, two related predictions of the model that were supported by earlier work have been retested: (1) altering the duration of rewarding brain stimulation should change the pulse frequency required to produce a reward of half-maximal intensity, and (2) this manipulation should not change the opportunity cost at which half-maximal performance is directed at earning a maximally intense reward. Prediction 1 was supported in all six subjects, but prediction 2 was supported in only three. The latter finding is interpreted to reflect recruitment, at some stimulation sites, of a heterogeneous reward substrate comprising dual, parallel circuits that integrate the stimulation-induced neural signals.
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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 | 0.014 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".