Bibliographic record
Abstract
Abstract Many conventional interval timing tasks do not contain asymmetric cost (loss) functions and thereby favor high temporal accuracy. On the other hand, asymmetric cost functions that differentially penalize/reinforce the early or late responses result in adaptive biases (shift) in timed responses due to timing uncertainty. Consequently, optimal performance in these tasks entails the normative parametrization of adaptive timing biases by the level of timing uncertainty. Differential reinforcement of response duration (DRRD) is one of these tasks that require mice to actively respond (e.g., continuously depressing a lever) for a minimum amount of time to be reinforced. The active production of a time interval by mice in DRRD differentiates this task from the differential reinforcement of low rates of responding (DRL) task as a passive waiting task that was used in earlier studies to investigate the optimality of adaptive biases in timing behavior. We tested 21 Th-Cre male mice (9 weeks old) in a DRRD task with a minimum requirement of 2 s. Mean response durations were positively biased (longer than the minimum requirement), and the extent of bias was predicted by the level of endogenous timing uncertainty. Mice nearly maximized the reward rate in this task. These results contribute to the accumulating evidence supporting optimal temporal risk assessment in non-human animals.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.022 | 0.006 |
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; both teacher heads agree on what is shown here.
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".