The Role of the Dorsolateral Prefrontal Cortex in Bimodal Divided Attention: Two Transcranial Magnetic Stimulation Studies
Bibliographic record
Abstract
The neural processes underlying the ability to divide attention between multiple sensory modalities remain poorly understood. To investigate the role of the dorsolateral prefrontal cortex (DLPFC) in bimodal divided attention, we completed two repetitive transcranial magnetic stimulation (rTMS) studies. We tested the hypothesis that the DLPFC is necessary in the ability to divide attention across modalities. This hypothesis originated as a result of a previous fMRI study in which the posterior DLPFC was active during a bimodal divided attention condition [Johnson, J. A., & Zatorre, R. J. Neural substrates for dividing and focusing attention between simultaneous auditory and visual events. Neuroimage, 2006]. In the current experiments, two separate groups of subjects underwent 10 min of slow rTMS to temporarily disrupt function of the DLPFC. In both groups, the ability to divide attention between unrelated auditory and visual stimuli decreased following DLPFC disruption compared to control site stimulation. Specifically, the ability to divide attention between modalities was hindered, leading to a pattern of behavior similar to bimodal selective attention (ability to attend to one or the other modality but not both). We discuss possible roles of the posterior DLPFC in bimodal divided attention and conclude that the area may be functioning to support the increased working memory load associated with divided, compared to selective attention.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".