Transcranial Magnetic Stimulation to the Transverse Occipital Sulcus Affects Scene but not Object Processing
Bibliographic record
Abstract
Traditionally, it was theorized that the human visual system identifies and classifies scenes in a bottom-up, object-centered approach, such that scene processing can only occur once the objects within a scene are identified. Conversely, recent research suggests a more top-down approach, such that the global image features of a scene are sufficient for the recognition and categorization of a scene. Moreover, we have shown that disrupting object processing with repetitive transcranial magnetic stimulation (rTMS) actually enhances scene processing possibly through a release of inhibitory mechanisms (Mullin & Steeves, 2011). The present study examines the effects of rTMS to the left transverse occipital sulcus (TOS), an area implicated in scene perception. In two separate sessions, we performed online functionally-guided rTMS to the left TOS and the vertex (control site) while participants performed an object and scene classification task. Each session included no rTMS trials. Participants were presented with a stream of scene and object images and were asked to indicate as quickly and accurately as possible whether they were manmade or natural. Preliminary results suggest that unlike rTMS to object areas, which produces a release of inhibition on scene processing, inhibiting the TOS does not affect object categorization. This suggests that there is not a mutual release of inhibition from scenes to objects in this top-down approach to image processing. However, transiently interrupting the TOS resulted in longer latencies and lower accuracy rates for scene processing compared to the control conditions. Given that the parahippocampal place area (PPA), a key region in the scene processing network, presumably remains intact with rTMS to the TOS, this suggests that the TOS must nonetheless play an important role in this network. Meeting abstract presented at VSS 2012
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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".