A bio-inspired reinforcement learning model that accounts for fast adaptation after punishment
Bibliographic record
Abstract
Humans and animals can quickly learn a new strategy when a previously-rewarding strategy is punished. It is difficult to model this with reinforcement learning methods, because they tend to perseverate on previously-learned strategies - a hallmark of impaired response to punishment. Past work has addressed this by augmenting conventional reinforcement learning equations with ad hoc parameters or parallel learning systems. This produces reinforcement learning models that account for reversal learning, but are more abstract, complex, and somewhat detached from neural substrates. Here we use a different approach: we generalize a recently-discovered neuron-level learning rule, on the assumption that it captures a basic principle of learning that may occur at the whole-brain-level. Surprisingly, this gives a new reinforcement learning rule that accounts for adaptation and lose-shift behavior, and uses only the same parameters as conventional reinforcement learning equations. In the new rule, the normal reward prediction errors that drive reinforcement learning are scaled by the likelihood the agent assigns to the action that triggered a reward or punishment. The new rule demonstrates quick adaptation in card sorting and variable Iowa gambling tasks, and also exhibits a human-like paradox-of-choice effect. It will be useful for experimental researchers modeling learning and behavior.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".