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Record W2996234880 · doi:10.1177/1362361320908104

Facilitating sensorimotor integration via blocked practice underpins imitation learning of atypical biological kinematics in autism spectrum disorder

2020· article· en· W2996234880 on OpenAlexaff
Nathan C. Foster, Simon J. Bennett, Joe Causer, Digby Elliott, Geoffrey Bird, Spencer J. Hayes

Bibliographic record

VenueAutism · 2020
Typearticle
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsMcMaster University
Fundersnot available
KeywordsNeurotypicalImitationAutismPsychologyCopyingMirror neuronCognitive psychologyBiological motionKinematicsAutism spectrum disorderMirroringDevelopmental psychologySet (abstract data type)Physical medicine and rehabilitationNeuroscienceCommunicationComputer scienceMedicine

Abstract

fetched live from OpenAlex

The reduced efficacy of voluntary imitation in autism is suggested to be underpinned by differences in sensorimotor processing. We examined whether the imitation of novel atypical biological kinematics by autistic adults is enhanced by imitating a model in a predictable blocked practice trial order. This practice structure is expected to facilitate trial-to-trial sensorimotor processing, integration and encoding of biological kinematics. The results showed that neurotypical participants were generally more effective at imitating the biological kinematics across all experimental phases. Importantly, and compared to a pre-test where imitation was performed in a randomised (unpredictable) trial order, the autistic participants learned to imitate the atypical kinematics more effectively following an acquisition phase of repeatedly imitating the same model during blocked practice. Data from the post-test showed that autistic participants remained effective at imitating the atypical biological kinematics when the models were subsequently presented in a randomised trial order. These findings show that the reduced efficacy of voluntary imitation in autism can be enhanced during learning by facilitating trial-to-trial processing and integration of sensorimotor information using blocked practice. Lay Abstract Autistic people sometimes find it difficult to copy another person’s movement accurately, especially if the movement is unfamiliar or novel (e.g. to use chop sticks). In this study, we found that autistic people were generally less accurate at copying a novel movement than non-autistic people. However, by making a small adjustment and asking people to copy this movement for a set number of attempts in a predictable manner, we showed that autistic people did successfully learn to copy a new movement. This is a very important finding for autistic people because rather than thinking they cannot copy new movements, all that needs to be considered is for parents/guardians, teachers and/or support workers to make a small adjustment so that learning occurs in a predictable manner for new skills to be successfully acquired through copying. The implications from this study are wide-ranging as copying (imitation) and motor learning are important developmental processes for autistic infants and children to acquire in order to interact within the world. Therefore, practising these behaviours in the most effective way can certainly help the developmental pathway.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.061
GPT teacher head0.324
Teacher spread0.263 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations5
Published2020
Admission routes1
Has abstractyes

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