The effect of probability discounting on reward seeking: a three-dimensional perspective
Bibliographic record
Abstract
Rats will work for electrical stimulation of the medial forebrain bundle. The rewarding effect arises from the volleys of action potentials fired by the stimulation and subsequent spatio-temporal integration of their post-synpatic impact. The proportion of time allocated to self-stimulation depends on the intensity of the rewarding effect as well as on other key determinants of decision-making, such as subjective opportunity costs and reward probability. We have proposed that a 3D model relating time allocation to the intensity and cost of reward can distinguish manipulations acting prior to the output of the spatio-temporal integrator from those acting at or beyond it. Here, we test this proposition by varying reward probability, a variable that influences the computation of payoff in the 3D model downstream from the output of the integrator. On riskless trials, reward was delivered on every occasion that the rat held down the lever for a cumulative duration called the "price," whereas on risky trials, reward was delivered with probability 0.75 or 0.50. According to the model, the 3D structure relating time allocation to reward intensity and price is shifted leftward along the price axis by reductions in reward probability; the magnitude of the shift estimates the change in subjective probability. The predictions were borne out: reducing reward probability shifted the 3D structure systematically along the price axis while producing only small, inconsistent displacements along the pulse-frequency axis. The results confirm that the model can accurately distinguish manipulations acting at or beyond the spatio-temporal integrator and strengthen the conclusions of previous studies showing similar shifts following dopaminergic manipulations. Subjective and objective reward probabilities appeared indistinguishable over the range of 0.5 ≤ p ≤ 1.0.
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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 | 0.001 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".