Striatal dopamine reflects individual long-term learning trajectories
Bibliographic record
Abstract
Abstract Learning from naïve to expert occurs over long periods of time, accompanied by changes in the brain’s neuronal signals. The principles governing behavioural and neuronal dynamics during long-term learning remain unknown. We developed a psychophysical visual decision task for mice that allowed for studying learning trajectories from naïve to expert. Mice adopted sequences of strategies that became more stimulus-dependent over time, showing substantial diversity in the strategies they transitioned through and settled on. Remarkably, these transitions were systematic; the initial strategy of naïve mice predicted their strategy several weeks later. Longitudinal imaging of dopamine release in dorsal striatum demonstrated that dopamine signals evolved over learning, reflecting stimulus-choice associations linked to each individual’s strategy. A deep neural network model trained on the task with reinforcement learning captured behavioural and dopamine trajectories. The model’s learning dynamics accounted for the mice’s diverse and systematic learning trajectories through a hierarchy of saddle points. The model used prediction errors mirroring recorded dopamine signals to update its parameters, offering a concrete account of striatal dopamine’s role in long-term learning. Our results demonstrate that long-term learning is governed by diverse yet systematic transitions through behavioural strategies, and that dopamine signals exhibit key characteristics to support this learning.
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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 | 0.000 |
| 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) | 0.001 | 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".