Transcranial Magnetic Stimulation over Posterior Parietal Cortex Disrupts Transsaccadic Memory of Multiple Objects
Bibliographic record
Abstract
The posterior parietal cortex (PPC) plays a role in spatial updating of goals for eye and arm movements across saccades, but less is known about its role in updating perceptual memory. We reported previously that transsaccadic memory has a capacity for storing the orientations of three to four Gabor patches either within a single fixation (fixation task) or between separate fixations (saccade task). Here, we tested the role of the PPC in transsaccadic memory in eight subjects by simultaneously applying single-pulse transcranial magnetic stimulation (TMS) over the right and left PPC, over several control sites, and comparing these to behavioral controls with no TMS. In TMS trials, we randomly delivered pulses at one of three different time intervals around the time of the saccade, or at an equivalent time in the fixation task. Controls confirmed that subjects could normally retain at least three visual features. TMS over the left PPC and a control site had no significant effect on this performance. However, TMS over the right PPC disrupted memory performance in both tasks. This TMS-induced effect was most disruptive in the saccade task, in particular when stimulation coincided more closely with saccade timing. Here, the capacity to compare presaccadic and postsaccadic features was reduced to one object, as expected if the spatial aspect of memory was disrupted. This finding suggests that right PPC plays a role in the spatial processing involved in transsaccadic memory of visual features. We propose that this process uses 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".