Learned Overweight Internal Model Can Be Activated to Maintain Equilibrium When Tactile Cues Are Uncertain: Evidence From Cortical and Behavioral Approaches
Bibliographic record
Abstract
Human adaptive behavior in sensorimotor control is aimed to increase the confidence in feedforward mechanisms when sensory afferents are uncertain. It is thought that these feedforward mechanisms rely on predictions from internal models. We investigate whether the brain uses an internal model of physical laws (gravitational and inertial forces) to help estimate body equilibrium when tactile inputs from the foot sole are depressed by carrying extra weight. As direct experimental evidence for such a model is limited, we used Judoka athletes thought to have built up internal models of external loads (i.e., opponent weight management) as compared with Non-Athlete participants and Dancers (highly skilled in balance control). Using electroencephalography, we first (experiment 1) tested the hypothesis that the influence of tactile inputs was amplified by descending cortical efferent signals. We compared the amplitude of P1N1 somatosensory cortical potential evoked by electrical stimulation of the foot sole in participants standing still with their eyes closed. We showed smaller P1N1 amplitudes in the Load compared to No Load conditions in both Non-Athletes and Dancers. This decrease neural response to tactile stimulation was associated with greater postural oscillations. By contrast in the Judoka's group, the neural early response to tactile stimulation was unregulated in the Load condition. This suggests that the brain can selectively increase the functional gain of sensory inputs, during challenging equilibrium tasks when tactile inputs were mechanically depressed by wearing a weighted vest. In Judokas, the activation of regions such as the right posterior inferior parietal cortex (PPC) as early as the P1N1 is likely the source of the neural responses being maintained similar in both Load and No Load conditions. An overweight internal model stored in the right PPC known to be involved in maintaining a coherent representation of one's body in space can optimize predictive mechanisms in situations with high balance constraints (Experiment 2). This hypothesis has been confirmed by showing that postural reaction evoked by a translation of the support surface on which participants were standing wearing extra-weight was improved in Judokas.
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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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".