Automated postural responses are modified in a functional manner by instruction
Bibliographic record
Abstract
The restoration of upright balance after a perturbation relies on highly automated and, to a large extent, stereotyped postural responses. Although these responses occur before voluntary control comes into play, previous research has shown that they can be functionally modulated on the basis of cognitive set (experience, advanced warning, instruction, etc.). It is still unknown, however, how the central nervous system deals with situations in which the postural response is not necessarily helpful in the execution of a task. In the present study, the effects of instruction on automated postural responses in neck, trunk, shoulder, and leg muscles were investigated when people were either instructed to recover balance after being released from an inclined standing posture [balance recovery (BR) trials], or not to recover at all and fall onto a safety mattress in the most comfortable way [fall (F) trials], in both backward and leftward directions. Participants were highly successful in following the instructions, consistently exhibiting stepping responses for balance recovery in BR trials, and suppressing stepping in the F trials. Yet EMG recordings revealed similar postural responses with onset latencies between 70 and 130 ms in both BR and F trials, with slightly delayed responses in F trials. In contrast, very pronounced and early differences were observed between BR and F trials in response amplitudes, which were generally much higher in BR than in F trials, but with clear differentiation between muscles and perturbation directions. These results indicate that a balance perturbation always elicits a postural response, irrespective of the task demands. However, when a specific balance recovery response is not desired after a perturbation, postural responses can be selectively downregulated and integrated into the motor output in a functional and goal-oriented way.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".