Transcranial Magnetic Stimulation to the Transverse Occipital Sulcus Affects Scene but Not Object Processing
Bibliographic record
Abstract
Traditionally, it has been theorized that the human visual system identifies and classifies scenes in an object-centered approach, such that scene recognition can only occur once key objects within a scene are identified. Recent research points toward an alternative approach, suggesting that the global image features of a scene are sufficient for the recognition and categorization of a scene. We have previously shown that disrupting object processing with repetitive TMS to object-selective cortex enhances scene processing possibly through a release of inhibitory mechanisms between object and scene pathways [Mullin, C. R., & Steeves, J. K. E. TMS to the lateral occipital cortex disrupts object processing but facilitates scene processing. Journal of Cognitive Neuroscience, 23, 4174-4184, 2011]. Here we show the effects of TMS to the transverse occipital sulcus (TOS), an area implicated in scene perception, on scene and object processing. TMS was delivered to the TOS or the vertex (control site) while participants performed an object and scene natural/nonnatural categorization task. Transiently interrupting the TOS resulted in significantly lower accuracies for scene categorization compared with control conditions. This demonstrates a causal role of the TOS in scene processing and indicates its importance, in addition to the parahippocampal place area and retrosplenial cortex, in the scene processing network. Unlike TMS to object-selective cortex, which facilitates scene categorization, disrupting scene processing through stimulation of the TOS did not affect object categorization. Further analysis revealed a higher proportion of errors for nonnatural scenes that led us to speculate that the TOS may be involved in processing the higher spatial frequency content of a scene. This supports a nonhierarchical model of scene recognition.
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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".