Who Comes First? The Role of the Prefrontal and Parietal Cortex in Cognitive Control
Bibliographic record
Abstract
Cognitive control processes enable us to adjust our behavior to changing environmental demands. Although neuropsychological studies suggest that the critical cortical region for cognitive control is the prefrontal cortex, neuro-imaging studies have emphasized the interplay of prefrontal and parietal cortices. This raises the fundamental question about the different contributions of prefrontal and parietal areas in cognitive control. It was assumed that the prefrontal cortex biases processing in posterior brain regions. This assumption leads to the hypothesis that neural activity in the prefrontal cortex should precede parietal activity in cognitive control. The present study tested this assumption by combining results from functional magnetic resonance imaging (fMRI) providing high spatial resolution and event-related potentials (ERPs) to gain high temporal resolution. We collected ERP data using a modified task-switching paradigm. In this paradigm, a situation where the same task was indicated by two different cues was compared with a situation where two cues indicated different tasks. Only the latter condition required updating of the task set. Task-set updating was associated with a midline negative ERP deflection peaking around 470 msec. We placed dipoles in regions activated in a previous fMRI study that used the same paradigm (left inferior frontal junction, right inferior frontal gyrus, right parietal cortex) and fitted their directions and magnitudes to the ERP effect. The frontal dipoles contributed to the ERP effect earlier than the parietal dipole, providing support for the view that the prefrontal cortex is involved in updating of general task representations and biases relevant stimulus-response associations in the parietal cortex.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".