Consecutive TMS-fMRI Reveals an Inverse Relationship in BOLD Signal between Object and Scene Processing
Bibliographic record
Abstract
The human visual system is capable of recognizing an infinite number of scenes containing an abundance of rich visual information. There are several cortical regions associated with the representation of a scene, including those specialized for object processing (the lateral occipital area [LO]) and for the spatial layout of scenes (the parahippocampal place area). Although behavioral studies have demonstrated that these image categories (scenes and objects) exert an influence on each other such that scene context can facilitate object identification or that scene categorization can be impaired by the presence of a salient object, little is known about the apparent cortical interactions involved in building the conscious representation of a complete scene. It has been shown that transcranial magnetic stimulation (TMS) to the left LO disrupts object categorization but facilitates scene categorization. Here, we show that this effect is also reflected by changes in the BOLD signal such that TMS to the left LO decreases BOLD signal at the stimulation site (LO) while viewing objects and increases BOLD signal in the left PPA when viewing scenes. This suggests that these regions, although likely not on a strict hierarchy of bottom-up coding, share functional communication likely in the form of inhibitory connections.
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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) | 0.003 | 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".