Stimulation in the Dorsolateral Prefrontal Cortex Changes Subjective Evaluation of Percepts
Bibliographic record
Abstract
Nelson and Narens have proposed a metacognition model that dissociates the objective processing of information (object-level) and the subjective evaluation of the performance (i.e., the metalevel). Neurophysiological evidence also indicates that the prefrontal cortices (PFC) are the brain areas which perform the metalevel function [1]-[3]. A corresponding neural mechanism of Nelson and Narens's model, called dynamic filtering theory [4], [5], indicates that object-level processing is distributed in the posterior cortices and regulated by the prefrontal cortices with a filtering or gating mechanism to select appropriate signals and suppress inappropriate signals and noise. Based on this model, a hypothesis can be developed that, in the case of uncertainty or overloading of object-level processing, the prefrontal cortices will become more active in order to modulate signals and noise. This hypothesis is supported by a recent fMRI study [6] showing that the PFC (Brodmann area 9, BA9) was activated when subjects were overloaded in a bimodal attentional task, compared to a unimodal task. Here, we report a study showing that applying repetitive transmagnetic stimulation (rTMS) over the BA9 in order to interfere with its functional activity resulted in significant increas in guessed responses, compared to three other control conditions (i.e., no-TMS, sham TMS on BA9, and rTMS on Cz). The results are compatible with the dynamic filtering theory and suggest that a malfunction of the PFC would weaken the quality of meta-cognitive percepts and increase the number of guessed responses.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".