The objects behind the scenes: TMS to area LO disrupts object but not scene categorization
Bibliographic record
Abstract
Many influential theories of scene perception are object centered (Biederman, 1981) suggesting that scenes are processed by extension of object processing in a bottom-up fashion. However, an alternative approach to scene processing is that the global gist of a scene can be processed in a top-down manner without the need for first identifying its component objects (Oliva & Torralba, 2001). This suggests that global aspects of a scene may be processed prior to the identification of individual objects. Evidence from a patient with object agnosia and bilateral damage to lateral occipital (LO) cortex, an area associated with object processing (Grill-Spector et al., 2001), also suggests that scene categorization can operate independently of object perception (Steeves et al., 2004). We asked whether or not temporary interruption to area LO in neurologically-intact controls with repetitive transcranial magnetic stimulation (rTMS) impairs object and scene processing. Participants categorized greyscale images of objects and scenes as ‘natural’ or ‘man-made’. Subsequently, we targeted area LO, which had been functionally defined with fMRI, and participants underwent five minutes of rTMS. Immediately following, they completed another version of the object and scene categorization task. Preliminary results show that rTMS to area LO impairs categorization of objects but not scenes. This suggests that the global gist used to rapidly categorize scenes remains intact despite an interruption to object processing brain regions.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".