The visual dorsal stream processes tool-use actions regardless of body part even in people born without hands
Bibliographic record
Abstract
The visual dorsal stream subserves vision for action. Previous research has shown that much of the dorsal stream encodes information about actions, particularly hand actions. However, the extent to which this information is abstract, or independent of visual and motor properties, is unclear. A high-level of abstraction would predict a consistent representation of the same actions, even if performed using another body part (effector) with different visual and motor properties. Here, we leveraged functional neuroimaging in typically developed controls and individuals born without hands to understand the abstraction level of motor representation. fMRI data from control subjects (n=13) and people born without both hands (n=3) were collected while participants completed tool-use and grasping actions with their dominant right hand and/or foot. Analyses suggested that actions are indeed represented at a high level of abstraction in the left anterior intraparietal sulcus (aIPS), left premotor cortex (PMd), and left supplementary motor area (SMA). Specifically, univariate activation levels showed similar preferences for tool-use actions for both the hand and foot. Moreover, multivariate action patterns could discriminate between actions, even across effectors. Importantly, action preferences and decoding were found in people born without hands, suggesting motor hand imagery is an unlikely cause for these findings. This implies that some areas of the visuomotor system have abstract representations that extend beyond visual and motor parameters to represent higher-order goals of specific 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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".