The Temporal Profile of Dual-task Interference in the Human Brain
Bibliographic record
Abstract
Abstract Due to the brain’s limited cognitive capacity, simultaneous execution of multiple tasks can lead to performance impairments, mainly when the tasks occur closely in time. This limitation is known as dual-task interference. We aimed to investigate the time course of this phenomenon in the brain, utilizing a combination of EEG, multivariate pattern analysis (MVPA), and drift-diffusion modeling (DDM). Here, participants first performed a tone discrimination task, followed by a lane-change task with either short or long onset time differences (Stimulus Onset Asynchrony, SOA), in a simulated driving environment. As expected, the dual-task interference increased the second task’s (lane-change) reaction time. The DDM analysis indicated that this increase was attributable to changes in both the decision time and the post-decision time. Our MVPA findings revealed a decrease in decoding accuracy for the lane-change task in short SOA compared to both long SOA and single-task conditions throughout the trial, highlighting the presence of interference. Moreover, the temporal generalization analysis identified a significant interference effect in short SOA compared to long SOA and single-task conditions after ∼250 ms relative to stimulus onset. Additionally, the conditional generalization analysis showed a delayed response after ∼450 ms. Searchlight analysis illustrated the progression of this information reduction, starting in occipital, parietal, and parieto-occipital leads responsible for perceptual and central processing and then transferring to the frontal leads for mapping decisions onto motor actions. Consistent with the hybrid dual-task interference theory, our results suggest that the processing of the two tasks occurs in a partial parallel manner for the first few hundred milliseconds and primarily in the perceptual and decision-processing stages. Subsequently, another competition arises between the two tasks to route information to motor areas for execution, resulting in the second task’s serial processing and delay or lengthening.
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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".