Humans forage for reward in reinforcement learning tasks
Bibliographic record
Abstract
How do we make good decisions in uncertain environments? In psychology and neuroscience, the classic view is that we calculate the value of each option, compare them, and choose the most rewarding modulo exploratory noise. An ethologist, conversely, would argue that we commit to one option until its value drops below a threshold and then explore alternatives. Because the fields use incompatible methods, it remains unclear which view better describes human decision-making. Here, we found that humans use compare-to-threshold computations in classic compare-alternative tasks. Because compare-alternative computations are central to the reinforcement-learning (RL) models typically used in the cognitive and brain sciences, we developed a novel compare-to-threshold model ("foraging"). Compared to previous RL models, the foraging model better fit participant behavior, better predicted the tendency to repeat choices, and predicted held-out participants that were almost impossible under compare-alternative models. These results suggest that humans use compare-to-threshold computations in sequential decision-making.
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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 | 0.001 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".