The reinforcement mountain: Allocation of behavior as a function of the rate and intensity of rewarding brain stimulation.
Bibliographic record
Abstract
The single-operant matching law has been used to describe the relationship between time allocated to pursuit of brain stimulation reward (BSR) and the obtained rate of reinforcement. We generalize this relationship to a third dimension by including the strength of the stimulation (the number of pulses per train) as an independent dimension, and we dub the resulting 3-dimensional structure "the reinforcement mountain." The validity of generalizing the single-operant matching law in this way was assessed by determining the changes in the position of the mountain produced by increasing the stimulation current or the train duration. Most of the predictions were supported, and the mountain model fitted the data closely. It is argued that application of this model can remove ambiguity inherent in 2-dimensional descriptions of operant performance and can reveal whether lesions, drugs, or physiological manipulations that alter performance for BSR act before or after the output of the ("reward-growth") function that translates the electrically induced impulse flow into the intensity of the BSR.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".