Serial pattern learning during skilled walking
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Rats possess a rich repertoire of sequentially organized, natural behaviors. It is possible that these natural behaviors may reflect implicit learning or relatively fixed movement patterns. The present study was conducted to determine whether factors known to influence implicit learning produce similar effects on the acquisition of skilled walking. Three groups of rats were trained to cross a horizontal ladder with rungs spaced according to three different levels of complexity. All training and testing were performed under dark conditions to assess the influence of non-visual modalities on skilled walking. Although all groups' performance improved throughout training, pattern complexity influenced the rate of improvement. In addition, performance during a probe session provided further evidence that each group encoded the rung spacing pattern experienced during training to create an internal representation. These observations demonstrate that the engram established during repetitive training represents either the temporal or spatial characteristics of rung spacing. These findings indicate that implicit learning contributes to the acquisition of natural sequential behaviors. Furthermore, serial pattern learning of rung spacing provides a novel task to determine sensory and motor contributions to the consolidation of skilled movement.
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.
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 | 0.000 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it