Stable Task Representations under Attentional Load Revealed with Multivariate Pattern Analysis of Human Brain Activity
Bibliographic record
Abstract
Performing multiple tasks concurrently places a load on limited attentional resources and results in disrupted task performance. Although human neuroimaging studies have investigated the neural correlates of attentional load, how attentional load affects task processing is poorly understood. Here, task-related neural activity was investigated using fMRI with conventional univariate analysis and multivariate pattern analysis (MVPA) while participants performed blocks of prosaccades and antisaccades, either with or without a rapid serial visual presentation (RSVP) task. Performing prosaccades and antisaccades with RSVP increased error rates and RTs, decreased mean activation in frontoparietal brain areas associated with oculomotor control, and eliminated differences in activation between prosaccades and antisaccades. However, task identity could be decoded from spatial patterns of activation both in the absence and presence of an attentional load. Furthermore, in the FEFs and intraparietal sulcus, these spatial representations were found to be similar using cross-trial-type MVPA, which suggests stability under attentional load. These results demonstrate that attentional load may disrupt the strength of task-related neural activity, rather than the identity of task representations.
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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 | 0.001 |
| 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".