TMS over the posterior parietal cortex disrupts transsaccadic memory
Bibliographic record
Abstract
We previously reported that transsaccadic memory has a similar capacity for storing simple visual features as basic visual memory (Prime & Crawford, VSS abstracts, 2006). Here, we tested how many object features and locations could be retained across saccades while applying single-pulse transcranial magnetic stimulation (TMS) over the right dorsal posterior parietal cortex (PPC). Five subjects were presented with a random number of targets (1, 3, 4, 5, 6, or 8) with different spatial positions and orientations. Subjects were instructed to fixate and remember the positions and orientations of the targets. Then, subjects made a saccade to a different random location and were presented with a probe at the same location as one of the pre-saccadic targets, but tilted 9E clockwise or counter-clockwise. Subjects made a force-choice response to indicate how the probe's visual feature differed from the original target. In each trial, we randomly delivered a single-pulse at one of seven different time intervals centred around the saccade-go signal (−300ms, −200ms, −100ms, 0ms, +100ms, +200ms, +300ms). Thereby, allowing us to obtain information of the timing of the contribution of the right PPC during task performance (causal chronometry). Our preliminary data shows that performance was disrupted during stimulation of the right PPC, particularly between 100ms to 300ms after the saccade-go signal. Stimulation at the other time intervals showed no statistical differences compared to the baseline (no TMS). The findings suggest that TMS over the right PPC transiently disrupt the putative spatial processing involved in transsaccadic memory.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".