Distinct perceptual and conceptual representations of natural actions along the lateral and dorsal visual streams: an EEG-fMRI fusion study
Bibliographic record
Abstract
Abstract Actions are the building blocks of our dynamic visual world, yet the neural computations supporting action perception are not well understood. How does perceptual and conceptual information unfold in the brain when we observe what others are doing? We collected EEG and fMRI data while participants viewed short videos and sentences depicting naturalistic actions. Using representational similarity analysis, we found a posterior-to-anterior gradient along the lateral pathway, from perceptual features to conceptual, modality-invariant representations. Among conceptual features, the target of actions (i.e. whether the action was directed at an object, a person, or the self) explained the most unique variance in EEG responses. In fMRI, we found distinct conceptual representations along the ventral, dorsal, and lateral pathways, with the target of actions specifically encoded in lateral occipitotemporal cortex (LOTC) and posterior superior temporal sulcus (pSTS). Finally, EEG-fMRI fusion revealed rapid processing along the lateral and dorsal pathways. Together, our results disentangle the perceptual and conceptual components of action understanding and characterize the underlying spatiotemporal dynamics in the human brain.
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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 | 0.002 |
| 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 | 0.001 |
| 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".