Bibliographic record
Abstract
Abstract The visual guidance of goal-directed movements requires transformations of incoming visual information that are different from those required for visual perception. For us to grasp an object successfully, our brain must use just-in-time computations of the object’s real-world size and shape, and its orientation and disposition with respect to our hand. These requirements have led to the emergence of dedicated visuomotor modules in the posterior parietal cortex of the human brain (the dorsal visual stream) that are functionally distinct from networks in the occipito-temporal cortex (the ventral visual stream) that mediate our conscious perception of the world. Although the identification and selection of goal objects and an appropriate course of action depends on the perceptual machinery of the ventral stream and associated cognitive modules, the execution of the subsequent goal-directed action is mediated by dedicated online control systems in the dorsal stream and associated motor areas. The dorsal stream allows an observer to reach out and grasp objects with exquisite ease, but by itself, deals only with objects that are visible at the moment the action is being programmed. The ventral stream, however, allows an observer to escape the present and bring to bear information from the past – including information about the function of objects, their intrinsic properties, and their location with reference to other objects in the world. Ultimately then, both streams contribute to the production of goal-directed actions. The principles underlying this division of labour between the dorsal and ventral streams are relevant to the design and implementation of autonomous robotic systems.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".