MétaCan
Menu
Back to cohort
Record W2053899143 · doi:10.3109/17518423.2011.635609

The role of assisted manipulation in cognitive development

2012· review· en· W2053899143 on OpenAlexaff
Albert M. Cook, Kim Adams, Pedro Encarnação, Liliana Alvarez

Bibliographic record

VenueDevelopmental Neurorehabilitation · 2012
Typereview
Languageen
FieldHealth Professions
TopicAssistive Technology in Communication and Mobility
Canadian institutionsGlenrose Rehabilitation HospitalUniversity of Alberta
Fundersnot available
KeywordsAugmentativeCognitionRobotPsychologyAssistive technologyCognitive skillMotor skillHuman–computer interactionDevelopmental psychologyComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

OBJECTIVE: Motor experience plays a central role in cognitive development. Assistive technologies can thus provide augmentative manipulation for children with motor disabilities. This paper explores the use of robots to this end. METHOD: A revision of studies conducted with typically-developing children and children with disabilities regarding the use of robots is presented. This revision provides a description of the cognitive skills required and revealed by the child when using a robot. Opportunities for participation and exploration were identified and further research is discussed. RESULTS: Robots provide insight into the cognitive skills of children with motor disabilities. Robots also provide means for independent exploration and participation in learning and play activities. Integration of augmentative manipulation and communication increases interest and participation of children with disabilities. CONCLUSION: Children with disabilities can use augmentative manipulation systems to independently explore and interact with their environment. Children can use robots as tools providing them with opportunities to reveal and further develop their cognitive skills. Alternative access methods can increase access for children with severe motor disabilities.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.964
Threshold uncertainty score0.938

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.202
GPT teacher head0.472
Teacher spread0.270 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations21
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueDevelopmental NeurorehabilitationSame topicAssistive Technology in Communication and MobilityFrench-language works237,207