Dynamics of sensorimotor reweighting: How light touch alters vestibular-evoked balance responses
Bibliographic record
Abstract
Abstract Integrated multisensory feedback plays a crucial role in balance control. Minimal fingertip contact with a surface (light-touch), reduces center of pressure (CoP) by adding sensory information about postural orientation and balance state. Electrical vestibular stimulation (EVS) can increase sway by adding erroneous vestibular cues. This juxtaposition of conflicting sensory cues can be exploited to explore the dynamics of sensorimotor reweighting. We used continuous stochastic EVS (0-25Hz; ±4mA; 200-300s) to evoke balance responses in CoP (Exp-1, Exp-2) and segment accelerations (Exp-2). Systems analyses (coherence, gain) quantified coupling and size of balance responses to EVS. We had participants either touch (TOUCH; <2N) or not touch (NO-TOUCH) a load cell during EVS (Exp-1, Exp-2), or we intermittently removed the touch surface (Exp-2) to measure the effects of light touch on vestibular-evoked balance responses. We hypothesized that coherence and gain between EVS and CoP would decrease, consistent with the CNS down-weighting vestibular cues that conflict with light touch. Light touch reduced CoP displacement, but increased variation in the CoP signal explained by EVS input. Significant coherence between EVS and CoP was observed up to ∼30Hz in both conditions but was significantly greater in the TOUCH condition from 12-28.5-Hz. Conversely, EVS-CoP gain was 63% lower in TOUCH, compared to NO-TOUCH. Our findings show that light touch can re-weight vestibular-evoked responses by reducing their size but also increasing high frequency vestibular contributions for sway. This suggests that the CNS can use novel sensory inputs to alter balance behavior but cannot completely ignore a salient balance cue. New and Noteworthy This study reveals that minimal fingertip contact (light touch) during balance tasks not only diminishes the impact of electrical vestibular stimulation (EVS) on sway, but also enhances the central nervous systems ability to integrate high-frequency vestibular cues. Specifically, light touch decreases the magnitude of EVS-induced sway while increasing coherence with EVS at higher frequencies, illustrating the central nervous system’s capacity to adaptively reweight sensory inputs for improved balance control without fully disregarding dominant cues.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".