Activity patterns in the category‐selective occipitotemporal cortex predict upcoming motor actions
Bibliographic record
Abstract
Converging lines of evidence point to the occipitotemporal cortex (OTC) as a critical structure in visual perception. For instance, human functional magnetic resonance imaging (fMRI) has revealed a modular organisation of object-selective, face-selective, body-selective and scene-selective visual areas in the OTC, and disruptions to the processing within these regions, either in neuropsychological patients or through transcranial magnetic stimulation, can produce category-specific deficits in visual recognition. Here we show, using fMRI and pattern classification methods, that the activity in the OTC also represents how stimuli will be interacted with by the body--a level of processing more traditionally associated with the preparatory activity in sensorimotor circuits of the brain. Combining functional mapping of different OTC areas with a real object-directed delayed movement task, we found that the pre-movement spatial activity patterns across the OTC could be used to predict both the action of an upcoming hand movement (grasping vs. reaching) and the effector (left hand vs. right hand) to be used. Interestingly, we were able to extract this wide range of predictive movement information even though nearly all OTC areas showed either baseline-level or below baseline-level activity prior to action onset. Our characterisation of different OTC areas according to the features of upcoming movements that they could predict also revealed a general gradient of effector-to-action-dependent movement representations along the posterior-anterior OTC axis. These findings suggest that the ventral visual pathway, which is well known to be involved in object recognition and perceptual processing, plays a larger than previously expected role in preparing object-directed hand actions.
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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) | 0.001 | 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".