Bounded optimality of time investments in rats, mice, and humans
Bibliographic record
Abstract
Summary Time is our scarcest resource. Allocating time optimally presents a universal challenge for all organisms because the future benefits of time investments are uncertain. We developed a normative framework for assessing bounded optimality in time allocation, emphasizing the accuracy of future predictions, independent of subjective costs and benefits. In a common decision task across humans, rats, and mice, we varied uncertainty by titrating ambiguous sensory evidence and measured the time each subject was willing to invest post-decision. We observed that all species and subjects invested more time when they were more likely to be correct, which reflected a statistical confidence of uncertain evidence. Time allocation strategy approached the lower bound of optimality, indicating an accurate decision-by-decision assessment of confidence in the likelihood that waiting will pay off – independent of the subjective payoff values and time costs. We demonstrate that an elementary algorithm based on a drift-diffusion process algorithm can implement this optimal time investment strategy. These results illuminate the computational mechanisms governing rational time investment, showing that humans, rats, and mice can maximize payoffs via confidence-guided time allocation. Highlights Computational and behavioral framework to assess bounded optimality of investments. Humans, rats, and mice invest more time to obtain more likely payoffs, in proportion to statistical confidence. Time investment was close to optimal model predictions, reflecting bounded optimality of investments under uncertainty. Bounded-optimal time investment may be an evolutionary ancient adaptive behavioral strategy.
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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 | 0.005 |
| 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 | 0.000 | 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".