Sensorimotor Integration: A Review of Neural and Computational Models and the Impact of Parkinson’s Disease
Bibliographic record
Abstract
Sensorimotor integration (SMI) is a complex process that allows humans to perceive and interact with their environment. Any impairment in SMI may impact the day-to-day functioning of humans, particularly evident in Parkinson’s Disease (PD). SMI is critical to accurate perception and modulation of motor outputs. Therefore, understanding the associated neural pathways and mathematical underpinnings is crucial. In this article, a systematic review of the proposed neural and computational models associated with SMI is performed. While the neural models discuss the neural architecture and regions, the computational models explore the mathematical or computational mechanisms involved in SMI. The article then explores how PD may impair SMI, reviewing studies that discuss deficits in the perception of various modalities, pointing to an SMI impairment. This helps in understanding the nature of SMI deficits in PD. Overall, the review offers comprehensive insights into the basis of SMI and the effect of PD on SMI, enabling clinicians to better understand the SMI mechanisms and facilitate the development of targeted therapies to mitigate SMI deficits in PD.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".