The role of proprioception in the planning and control of action following sensory deprivation
Bibliographic record
Abstract
Both vision and proprioception operate in the planning and control of action (Touzalin-Chretien et al., 2010). However, vision has been reported as the dominant sensory modality (Heath, 2005). As such, vision is preferred over proprioception when vision is available (Touzalin-Chretien et al., 2010). Of interest, then, is the role of proprioception in motor tasks when vision is absent, and what changes in the brain may result from visual deprivation with respect to proprioception. The current study employed a 2-hour visual deprivation to differentiate proprioception from vision by removing visual input. During the visual deprivation, subjects participated in tactile discrimination tasks to promote proprioceptive plasticity. Prior to and following deprivation participants were asked to perform a grasping task under either visual (Vision) or proprioceptive (No-Vision) control (40 trials each: 160 total). For every trial, an experimenter passively moved the subject's right hand to the target and back to a set origin. In No-Vision trials, participants grasped either a small (5 cm diameter) or large (7 cm diameter) circular target at two different locations (20 or 35 cm) in response to an auditory tone. In Vision trials, vision was made available 2 s prior to the auditory tone until movement end. Participants were instructed to grasp the target as quickly and accurately as possible. PLATO goggles (Translucent Technologies, Inc.) were used to remove visual input during the deprivation and No Vision condition. Kinematic measurements (e.g. grip aperture, reaction time, movement time) were obtained using an Optotrak 3020 (Northern Digital, Inc.). Results indicated augmented use of proprioception for motor planning, but less so for motor control of reaching and grasping strategies following acute visual deprivation. Our results are consistent with findings that sensory deprivation is associated with plasticity and behavioral changes (Merabet & Pascual-Leone, 2010).
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".