Infant bouncing during spring frequency perturbations
Bibliographic record
Abstract
Infants explore their environments often using repetitive movements of multiple degrees of freedom to create a motor sequence. Previous research suggests that infants suspended in a commercial spring-mounted harness bounce at two frequencies using distinct mechanisms of control (Vallis, 1998). We determined how typically developing infants bounced when the system natural frequency was experimentally modified. Methods Infants aged 5 to 12 months bounced while suspended over two AMTI force platforms from four spring systems with natural frequencies of 1.0 (C1), 1.3 (C2), 1.5 (C3) and 2.0 (C4) Hz. Kinematic data and vertical ground reaction forces (VGRFs) were recorded continuously. Results Across conditions, infants spent a relatively equal percent of time on the ground and in the air during each bounce cycle with the peak VGRFs decreasing as the bounce frequency increased. Bounce heights varied as a function of bounce strategy: a) infants who bounced at similar frequencies across conditions (C1 to C4 from 2.01 to 2.28 Hz) generated similar bounce heights in all conditions; while b) bounce height decreased across conditions in infants whose bounce frequency changed as a function of natural spring frequency (C1: 1.05 Hz; C2: 1.25-1.29 Hz; C3: 1.42-1.43 Hz; C4: 1.75-1.76 Hz). Discussion Infants adapt to different system natural frequencies by either maintaining a fixed frequency across conditions or by adapting the bounce frequency to closely match the system frequency.
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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 | 0.002 |
| 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 | 0.001 |
| 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".