Transcranial Magnetic Stimulation of the Human Frontal Eye Field: Effects on Visual Perception and Attention
Bibliographic record
Abstract
When looking at one object, human subjects can shift their attention to another object in their visual field without moving the eyes. Such shifts of attention activate the same brain regions as those involved in the execution of eye movements. Here we investigate the role of one of the main cortical oculomotor area, namely, the frontal eye field (FEF), in shifts of attention. We used transcranial magnetic stimulation (TMS), a technique known to disrupt transiently eye-movements preparation. We hypothesized that if the FEF is a necessary element in the network involved in shifting attention without moving the eyes, then#10; TMS should also disrupt visuospatial attention. For each volunteer, we positioned the TMS coil over the probabilistic anatomical location of the FEF, and we verified that single pulses delayed eye movements. We then applied TMS during a visuospatial attention task. In this task, a central arrow directed shifts of attention and the subject responded by a keypress to a subsequent visual peripheral target without moving the eyes from the central fixation point. In a few trials, the cue was invalid or uninformative, yielding slower responses than when the cue was valid. We delivered single pulses either 53 msec before or 70 msec after target onset. Contrary to our prediction, the main effect of the stimulation was a decrease in reaction time when it was applied 53 msec before target onset. TMS over the left hemisphere facilitated responses to targets in the right hemifield only and for all cueing conditions, whereas TMS over the right hemisphere had a bilateral effect for valid and neutral but not invalid cueing. Thus, TMS interfered with shift of attention only in the case of right hemisphere stimulation: it increased the cost of invalid cueing. Our results suggest that TMS over the FEF facilitates visual detection, and thereby reduces reaction time. This finding provides new insights into the role of the human FEF in processing visual information. The functional asymmetry observed for both facilitation of visual detection and interference with shifts of attention provides further evidence for the dominance of the right hemisphere for those processes. Our results also underline that the disruptive or facilitative effect of TMS over a given region depends upon the behavioral context.
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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 | 0.000 |
| 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".