When Time Shapes Behavior: fMRI Evidence of Brain Correlates of Temporal Monitoring
Bibliographic record
Abstract
Time processing may shape behavior in several ways, although the underlying neural correlates are still poorly understood. When preparatory intervals between stimuli vary randomly in a block, for instance, responses are faster as the interval gets longer. This effect, known as variable foreperiod (FP) effect, has been attributed to a process monitoring the conditional probability of stimulus occurrence as the interval increases. Previous evidence points to the right dorsolateral prefrontal cortex (DLPFC) as a possible node for this time-monitoring process. The present study addresses this hypothesis with functional magnetic resonance imaging (fMRI). Block-design fMRI was used on 14 young participants while they performed a visual discrimination task with fixed and variable preparatory intervals (FPs) of 1 and 3 sec. In the variable versus fixed FP contrast, the right DLPFC and a visual area were more activated in the subgroup of participants who showed a reliable variable FP effect than in another subgroup who did not show that effect. Only the activation in the right DLPFC was supported by a significant interaction between FP condition (variable vs. fixed) and group. This finding may reflect possible differences in the strategy adopted by the two subgroups of participants while performing the task. Although results suggest that many brain areas may be involved in preparation over time, the role of the right DLPFC is critical to observe the strategically mediated behavioral effects in the variable FP paradigm.
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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 | 0.002 |
| 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 | 0.000 | 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".