Strengthening risk prediction using statistical learning in children with autism spectrum disorder
ISSN: 2056-3868
Article publication date: 12 October 2018
Issue publication date: 16 October 2018
Abstract
Purpose
The purpose of this paper is to investigate the prediction ability in children with ASD in the risk-involving situations and compute the impact of statistical learning (SL) in strengthening their risk knowledge. The learning index and stability with time are also calculated by comparing their performance over three consecutive weekly sessions (session 1, session 2 and session 3).
Design/methodology/approach
Participants were presented with a series of images, showing simple and complex risk-involving situations, using the psychophysical experimental paradigm. The stimuli in the experiment were provided with different levels of difficulty in order to keep the legacy of the prediction and SL-based experiment intact.
Findings
The first phase of experimental work showed that children with ASD accurately discriminated the risk, although performed poorly as compared to neurotypical. The attenuated response in differentiating risk levels indicates that children with ASD have a poor and underdeveloped sense of risk. The second phase investigated their capability to extract the information from repetitive patterns and calculated SL stability value in time. The learning curve shows that SL is intact and stable with time (average session r=0.74) in children with ASD.
Research limitations/implications
The present work concludes that impaired action prediction could possibly be one of the factors underlying underdeveloped sense of risk in children with ASD. Their SL capability shows that risk knowledge can be strengthened in them. In future, the studies should investigate the impact of age and individual differences, by using knowledge from repetitive trials, on the learning rate and trajectories.
Practical implications
SL, being an integral part of different therapies, rehabilitation schemes and intervention systems, has the potential to enhance the cognitive and functional abilities of children with ASD.
Originality/value
Past studies have provided evidence regarding the work on the prediction ability in individuals with ASD. However, it is unclear whether the risk-involving/dangerous situations play any certain role to enhance the prediction ability in children with ASD. Also, there are limited studies predicting risk knowledge in them. Based on this, the current work has investigated the risk prediction in children with ASD.
Keywords
Acknowledgements
The authors are highly grateful to the commitment of the participants and parents for being a part of this study. The authors thank the NGO’s (PRAYAAS (22 children), SPARSH (15 children)) owners and supervisors (Joginder Singh) for their throughout help in recruiting the participants and supervising during the data acquisition. Both the authors agree with the contents of the manuscript. The authors also thank Dr Madhvi Oberoi (Medical Officer, Jalandhar Cantonment Hospital) and Dr Pallavi Khanna (Psychologists at SPARSH and Dr B R Ambedkar National Institute of Technology, Jalandhar) for providing their full cooperation and devoting their valuable time willingly in framing the results which might be generalized to unselected children. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Citation
Tanu, T. and Kakkar, D. (2018), "Strengthening risk prediction using statistical learning in children with autism spectrum disorder", Advances in Autism, Vol. 4 No. 3, pp. 141-152. https://doi.org/10.1108/AIA-06-2018-0022
Publisher
:Emerald Publishing Limited
Copyright © 2018, Emerald Publishing Limited