Comparing the effects of faded vs. constant knowledge of results on the acquisition, retention, and transfer of a skilled walking task
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study is to compare the use of faded and constant knowledge of results (KR) on skill acquisition, retention, and transfer in an end-point precision locomotion task. METHODS: Participants were trained in an end-point precision locomotion task where they were asked to match their peak foot height during the swing phase as closely as possible to a target height. Targets were normalized to individual foot trajectory. Participants were randomized to a constant KR group (KR presented after each trial) or a faded KR group (KR provided on 50 % of trials, distributed using a faded procedure). Before acquisition, and immediately, 24, and 48 h after acquisition, participants were tested on their performance of the task. Participants were also tested in a transfer task immediately, 24, and 48 h after acquisition, where they wore an ankle weight of 2.5 % of their body weight to complete the performance test. RESULTS: Thirty-six healthy adults participated in this study. Our findings demonstrate that both constant and faded KR groups showed improvements in performance immediately after acquisition. However, the faded KR group outperformed the constant feedback group at 24 and 48 h with respect to both skill retention and transfer. CONCLUSIONS: Faded KR leads to superior retention and transfer of an end-point precision locomotion task over time. Future research should explore these findings in clinical populations and the incorporation of other feedback modalities.
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 | 0.009 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".