Transcranial magnetic stimulation to lateral occipital cortex disrupts object ensemble processing
Bibliographic record
Abstract
Several neuroimaging studies have reported that object processing selectively activates the lateral occipital area of the brain (LO). Processing in this area is most often studied by presenting a single object to the central visual field. However, the world outside of the laboratory is comprised of multiple objects. Frequently these objects are part of a larger collection or ensemble of objects. For instance, a flower bed or leaves on a tree contain homogeneous repeating and overlapping objects of different sizes and orientations. Recent neuroimaging evidence suggests that such ‘object ensembles’ show activation in area LO, much like single object displays. Additionally, object ensemble activation was observed in the scene selective parahippocampal place area. We asked whether transcranial magnetic stimulation (TMS) to LO would disrupt object ensemble processing, as has been shown with single object displays, which would suggest that ensembles recruit similar cortical areas to isolated objects. If no disruption is observed, it may suggest that object ensembles are processed more like scenes, and rely more on cortical areas associated with scene processing. Participants categorized grayscale photographs of objects ensembles as ‘natural’ or ‘man-made’ while simultaneously receiving a double pulse of TMS at 10 Hz to functionally defined area LO and to the vertex as a control. Preliminary results demonstrate a significant disruption to categorization during the TMS to LO condition compared to the baseline and TMS to vertex conditions. This disruption may reflect an inability to form a statistical summation of the objects within the ensemble required for accurate categorization. Moreover, this finding suggests that area LO quickly processes shape information from more complex stimuli than single objects.
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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".