First trial and StartReact effects induced by balance perturbations to upright stance
Bibliographic record
Abstract
Postural responses (PR) to a balance perturbation differ between the first and subsequent perturbations. One explanation for this first trial effect is that perturbations act as startling stimuli that initiate a generalized startle response (GSR) as well as the PR. Startling stimuli, such as startling acoustic stimuli (SAS), are known to elicit GSRs, as well as a StartReact effect, in which prepared movements are initiated earlier by a startling stimulus. In this study, a StartReact effect paradigm was used to determine if balance perturbations can also act as startle stimuli. Subjects completed two blocks of simple reaction time trials involving wrist extension to a visual imperative stimulus (IS). Each block included 15 CONTROL trials that involved a warning cue and subsequent IS, followed by 10 repeated TEST trials, where either a SAS (TESTSAS) or a toes-up support-surface rotation (TESTPERT) was presented coincident with the IS. StartReact effects were observed during the first trial in both TESTSAS and TESTPERT conditions as evidenced by significantly earlier wrist movement and muscle onsets compared with CONTROL. Likewise, StartReact effects were observed in all repeated TESTSAS and TESTPERT trials. In contrast, GSRs in sternocleidomastoid and PRs were large in the first trial, but significantly attenuated over repeated presentation of the TESTPERT trials. Results suggest that balance perturbations can act as startling stimuli. Thus first trial effects are likely PRs which are superimposed with a GSR that is initially large, but habituates over time with repeated exposure to the startling influence of the balance perturbation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".