The Role of Enactment in Learning American Sign Language in Younger and Older Adults
Bibliographic record
Abstract
“Tell me, and I will forget. Show me, and I may remember. Involve me, and I will understand” (Confucius, 450 B.C). Philosophers and scientists alike have pondered the question of the mind-body link for centuries. Recently the role of motor information has been examined more specifically for a role in learning and memory. This paper describes a study using an errorless learning protocol to teach characters to young and older persons in American Sign Language. Participants were assigned to one of two groups: recognition (visually recognizing signs) or enactment (physically creating signs). Number of signs recalled and rate of forgetting were compared between groups and across age cohorts. There were no significant differences, within either the younger or older groups for number of items recalled. There were significant differences between recognition and enactment groups for rate of forgetting, within young and old, suggesting that enactment improves the strength of memory for items learned, regardless of age.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".