Predictable and unpredictable rewards produce similar changes in dopamine tone.
Bibliographic record
Abstract
Unpredicted rewards trigger more vigorous phasic responses in midbrain dopamine (DA) neurons than predicted rewards. However, recent evidence suggests that reward predictability may fail to influence DA signaling over longer scales: In rats passively receiving rewarding electrical brain stimulation, the concentration of DA in dialysate obtained from nucleus accumbens probes was similar regardless of whether reward onset was predictable (G. Hernandez et al., 2006). The present experiment followed up on these findings by requiring the rats to work for the rewarding stimulation, thus confirming whether they indeed learned the timing and predictability of reward delivery. Performance under fixed-interval and variable-interval schedules was compared, and DA levels in the nucleus accumbens were measured by means of in vivo microdialysis. The observed patterns of operant responding indicate that the rats working under the fixed-interval schedule learned to predict the time of reward availability, whereas the rats working under the variable-interval schedule did not. Nonetheless, indistinguishable changes in DA concentration were observed in the 2 groups. Thus, reward predictability had no discernable effect on a measure believed to track the slower components of DA signaling.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".