TMS Over Human Frontal Eye Fields Disrupts Trans-saccadic Memory of Multiple Objects
Bibliographic record
Abstract
We recently showed that transcranial magnetic stimulation (TMS) over the right parietal eye fields disrupts memory of object features and locations across saccades. We applied TMS over the frontal eye fields (FEF) as subjects compared the feature details of visual targets presented either within a single eye fixation (Fixation Task) or across a saccade (Saccade Task). TMS pulses were randomly delivered at one of 3 time intervals around the time of the saccade, or at equivalent times in the Fixation Task. A No-TMS control confirmed that subjects could normally retain approximately 3 visual features. TMS in the Fixation Task had no effect compared with No-TMS, but differences among TMS times were found during right FEF stimulation. TMS over either the right or left FEF disrupted memory performance in the Saccade Task when stimulation coincided most closely with the saccade. The capacity to compare pre-and postsaccadic features was reduced to 1-2 objects, as expected if the spatial aspect of memory was disrupted. These findings suggest that the FEF plays a role in the spatial processing involved in trans-saccadic memory of visual features. We propose that this process employs saccade-related feedback signals similar to those observed in spatial updating.
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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) | 0.001 | 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".