Inferring learning rules during de novo task learning
Bibliographic record
Abstract
Identifying the learning rules that govern behavior is a central problem in neuroscience. While reinforcement learning (RL) offers a unifying theoretical framework, most empirical studies of animal learning behavior have focused on non-stationary environments (e.g. changing reward probabilities in a known task), as opposed to acquiring an entirely new task from scratch. Here we introduce a statistical framework to infer reinforcement learning rules directly from single-animal behavior. Applied to mice learning a perceptual decision-making task, our approach reveals that policy-gradient-like rules capture de novo task learning better than classical temporal-difference algorithms. By fitting flexible parametric learning rules, we uncover systematic deviations from standard RL models, including side-specific learning rates and negative reward baselines. Together, these parameters account for side-biased learning, as well as forgetting and consecutive errors due to aversive responses to incorrect trials. Extending the framework with latent, dynamic learning rates further reveals that animals adapt their learning rates over training and across curricula. These results provide a statistical account of how animals learn from scratch and highlight key departures from classical reinforcement learning algorithms.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".