Predictable and unpredictable rewards produce similar changes in dopamine tone.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it