Data and code for "Robotic manipulation of human bipedalism reveals overlapping internal representations of space and time"
Bibliographic record
Abstract
Includes data and code to replicate the figures from "Robotic manipulation of human bipedalism reveals overlapping internal representations of space and time" Abstract: Effective movement control in bipedal postures relies on sensory inputs from the past, which encode dynamic changes in the spatial properties of our movement over time. To uncover how the spatial and temporal properties of bipedal posture interact in the perception and control of upright stance, we implemented a robotic virtualization of human body dynamics to systematically alter body inertia and viscosity as well as sensorimotor delays in 20 healthy participants. Inertia gains below unity as well as negative viscosity gains led to larger postural oscillations and caused participants to exceed the virtual balance limits, mimicking the disruptive effects of imposing an additional 200 ms sensorimotor delay on balance control. When balancing without delays, participants adjusted their inertia gains to below unity and viscosity gains to negative values to match the perceived effects of balancing with an imposed delay. When delays were present, participants increased inertia gains above unity and used positive viscosity gains to align their perception with baseline balance. Building on these findings, 10 naïve participants exhibited improved balance stability and reduced the number of instances they exceeded the limits when balancing with a 200 ms delay appropriately compensated by inertia gains above unity and positive viscosity gains. These results underscore the importance of innovative robotic virtualizations of standing balance to reveal the interconnected representations of space and time that underlie the stable perception and control of bipedal balance. Robotic manipulation of body physics offers a transformative approach to understand how the nervous system processes spatial information over time and could address clinical sensorimotor deficits associated with delays.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.270 | 0.073 |
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".