Hidden temporal structure of the ongoing task impacts detection strategy and is reflected in pupillary dynamics
Bibliographic record
Abstract
Abstract Estimating temporal regularities in incoming sensory inputs supports optimal decisions in noisy environments. In particular, inferred temporal structure can ease the detection of likely target events. Here we postulated that timely urgency signals can adapt subjects’ decision-making to the ongoing task temporal structure, possibly through neuromodulatory tone. To test this hypothesis, we used an auditory change detection task in which targets followed a block-based temporal contingency, unbeknownst to participants. False alarm occurrences were driven by the distribution of target timings, indicating that participants adapted their behavior to the ongoing temporal structure. Task-evoked pupillary responses were larger for blocks with earliest target timings, and correlated with individual subjects’ behavioral adaptation. Individual pupil responses matched an urgency signal extracted from a decision model fitted to behavior. This work demonstrates that internal temporal expectation can be tracked through pupillary dynamics, suggesting a role of neuromodulatory systems in context-dependent modulation of decision variable dynamics.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".