The role of the posterior parietal cortex in cognitive-motor integration
Bibliographic record
Abstract
"When interacting with an object within the environment, one must combine visual information with the felt limb position (i.e. proprioception) in order compute an appropriate coordinated muscle plan for accurate motor control. Amongst the vast reciprocally connected parieto-frontal connections responsible for guiding a limb throughout space, the posterior parietal cortex (PPC) remains a front-runner as a crucial node within this network. Our brain is primed to reach directly towards a viewed object, a situation that has been termed "standard". Such direct eye-hand coordination is common across species and is crucial for basic survival. Humans, however, have developed the capacity for tool-use and thus have learned to interact indirectly with an object. In such "non-standard" situations, the directions of gaze and arm movement are spatially decoupled and rely on both the implementation of a cognitive rule and online feedback of the decoupled limb. \n\nThe studies included within this dissertation were designed to further characterize the role of the PPC in different types of visually-guided reaching which require one to think and to act simultaneously (i.e. cognitive-motor integration). To address the relative contribution of different cortical networks responsible for cognitive-motor integration, we tested three patients with optic ataxia (OA; two unilateral - first study, and one bilateral -second study) as well as healthy participants during a cognitively-demanding dual task (third study) on a series of visually-guided reaching tasks each requiring a relative weighting between explicit cognitive control and implicit online control of the spatially decoupled limb. We found that the eye and hand movement performance during decoupled reaching was the most compromised in OA during situations relying on sensorimotor recalibration, and the most compromised in healthy participants during a dual task relying on strategic control. Taken together, these data presented in this dissertation provide further evidence for the existence of alternate task-dependent neural pathways for cognitive-motor integration."
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".