Decomposing Effects of Time on Task Reveals an Anteroposterior Gradient of Perceptual Decision Regions
Bibliographic record
Abstract
In perceptual decision making, the selection of an appropriate action depends critically on an organism's ability to use sensory inputs to accumulate evidence for a decision. However, differentiating decision-related processes from effects of "time on task" can be difficult. Here we combine the response signal paradigm, in which the experimenter rather than the subject dictates the time of the response, and independent components analysis (ICA) to search for signatures consistent with time on task and decision making, respectively, throughout the brain. Using this novel approach, we identify two such independent components from BOLD activity related to a random dot motion task: one sensitive to the main effect of stimulus duration, and one to both the main effect of motion coherence and its interaction with duration. Furthermore, we demonstrate that these two components are expressed differently throughout the brain, with activity in occipital regions most reflective of the former, activity within intraparietal sulcus modulated by both factors, and more anterior regions including the anterior insula, pre-SMA, and inferior frontal sulcus driven almost exclusively by the latter. Consistent with these ICA findings, cluster analysis identifies a posterior-to-anterior gradient that differentiates regions sensitive to time on task from regions whose activity is strongly tied to motion coherence. Together, these findings demonstrate that progressively more anterior regions are likely to participate in progressively more proximate decision-related processes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".