How (and why) the visual control of action differs from visual perception
Bibliographic record
Abstract
Vision not only provides us with detailed knowledge of the world beyond our bodies, but it also guides our actions with respect to objects and events in that world. The computations required for vision-for-perception are quite different from those required for vision-for-action. The former uses relational metrics and scene-based frames of reference while the latter uses absolute metrics and effector-based frames of reference. These competing demands on vision have shaped the organization of the visual pathways in the primate brain, particularly within the visual areas of the cerebral cortex. The ventral 'perceptual' stream, projecting from early visual areas to inferior temporal cortex, helps to construct the rich and detailed visual representations of the world that allow us to identify objects and events, attach meaning and significance to them and establish their causal relations. By contrast, the dorsal 'action' stream, projecting from early visual areas to the posterior parietal cortex, plays a critical role in the real-time control of action, transforming information about the location and disposition of goal objects into the coordinate frames of the effectors being used to perform the action. The idea of two visual systems in a single brain might seem initially counterintuitive. Our visual experience of the world is so compelling that it is hard to believe that some other quite independent visual signal-one that we are unaware of-is guiding our movements. But evidence from a broad range of studies from neuropsychology to neuroimaging has shown that the visual signals that give us our experience of objects and events in the world are not the same ones that control our 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.012 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".