The neural dynamics of natural action understanding
Bibliographic record
Abstract
Humans rapidly make sense of an ever-changing visual world, extracting information about people’s actions in a wide range of settings. Yet it remains unclear how the brain processes this complex information, from the extraction of perceptual details to the emergence of abstract concepts. To address this, we curated a naturalistic dataset of 95 short videos and sentences depicting everyday human actions. We densely labeled each action with perceptual features like scene setting (indoors/outdoors), action-specific features like tool use, and semantic features categorizing actions at different levels of abstraction, from specific action verbs (e.g. chopping) to broad action classes (e.g. manipulation). To investigate when and where these features are processed in the brain, we leveraged a multimodal approach, collecting EEG and fMRI data while participants viewed the action videos and sentences. We applied temporally and spatially resolved representational similarity analysis and variance partitioning to characterize the neural dynamics of action feature representations. We found that action information is extracted in the brain along a temporal gradient, from early perceptual features to later action-specific and semantic information. We mapped action-specific and semantic features to areas in parietal and lateral occipitotemporal cortices. Using cross-decoding across videos and sentences, we identified a late (~500 ms) modality-invariant neural response. Our results characterize the spatiotemporal dynamics of action understanding in the brain, and highlight the shared neural representations of human actions across vision and language.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".