TMS to object-selective LO enhances fMR adaptation to scenes in the PPA
Bibliographic record
Abstract
Damage to object-selective lateral occipital cortex (LO) results in impaired object recognition as evidenced in patients with object agnosia. We recently showed that transcranial magnetic stimulation (TMS) to LO disrupts object processing but enhances scene processing (Mullin & Steeves, 2011). This behavioural dissociation is mirrored in reduced BOLD signal at area LO subsequent to TMS to LO and increased BOLD signal in the scene-selective parahippocampal place area (PPA) (Mullin & Steeves, 2013). We performed consecutive repetitive TMS - fMRI using an fMR adaptation paradigm to determine response properties of object and scene processing regions following TMS to left LO compared to baseline. Participants viewed blocks of variant and invariant objects and scenes. At the TMS target site release from adaptation still occurred when viewing objects, and in the PPA release from adaptation was increased when viewing scenes. These findings suggest that despite disruption to area LO from TMS, it continues to differentiate objects. Remote areas in the network, specifically the PPA, benefit from disruption to LO with enhanced response properties. Meeting abstract presented at VSS 2014
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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) | 0.002 | 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".