Using Lego robots to estimate cognitive ability in children who have severe physical disabilities
Bibliographic record
Abstract
PURPOSE: To determine whether low-cost robots provide a means by which children with severe disabilities can demonstrate understanding of cognitive concepts. METHOD: Ten children, ages 4 to 10, diagnosed with cerebral palsy and related motor conditions, participated. Participants had widely variable motor, cognitive and receptive language skills, but all were non-speaking. A Lego Invention 'roverbot' was used to carry out a range of functional tasks from single-switch replay of pre-stored movements to total control of the movement in two dimensions. The level of sophistication achieved on hierarchically arranged play tasks was used to estimate cognitive skills. RESULTS: The 10 children performed at one of the six hierarchically arranged levels from 'no interaction' through 'simple cause and effect' to 'development and execution of a plan'. Teacher interviews revealed that children were interested in the robot, enjoyed interacting with it and demonstrated changes in behaviour and social and language skills following interaction. CONCLUSIONS: Children with severe physical disabilities can control a Lego robot to perform un-structured play tasks. In some cases, they were able to display more sophisticated cognitive skills through manipulating the robot than in traditional standardised tests. Success with the robot could be a proxy measure for children who have cognitive abilities but cannot demonstrate them in standard testing.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.002 | 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".