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Strengthening risk prediction using statistical learning in children with autism spectrum disorder

Strengthening risk prediction using statistical learning in children with autism spectrum disorder 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/approachParticipants 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.FindingsThe 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/implicationsThe 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 implicationsSL, 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/valuePast 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Advances in Autism Emerald Publishing

Strengthening risk prediction using statistical learning in children with autism spectrum disorder

, Tanu; Kakkar, Deepti
Advances in Autism , Volume 4 (3): 12 – Oct 16, 2018
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Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
2056-3868
DOI
10.1108/aia-06-2018-0022
Publisher site
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Abstract

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/approachParticipants 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.FindingsThe 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/implicationsThe 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 implicationsSL, 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/valuePast 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.

Journal

Advances in Autism – Emerald Publishing

Published: Oct 16, 2018

Keywords: Stability; Autism spectrum disorder; Risk factor; Action prediction; Learning rate; Statistical learning

References

  • Action understanding as inverse planning
    Baker, C.L.; Saxe, R.; Tenenbaum, J.B.
  • Individuals with autism spectrum disorder show normal responses to a fear potential startle paradigm
    Bernier, R.; Dawson, G.; Panagiotides, H.; Webb, S.
  • Tactile sensitivity in Asperger syndrome
    Blakemore, S.J.; Tavassoli, T.; Calò, S.; Thomas, R.M.; Catmur, C.; Frith, U.; Haggard, P.
  • The psychophysics toolbox
    Brainard, D.H.; Vision, S.
  • Action prediction in 10-month-old infants at high and low familial risk for autism spectrum disorder
    Braukmann, R.; Ward, E.; Hessels, R.S.; Bekkering, H.; Buitelaar, J.K.; Hunnius, S.
  • Intact implicit learning in autism spectrum conditions
    Brown, J.; Aczel, B.; Jiménez, L.; Kaufman, S.B.; Grant, K.P.
  • Reduced sensitivity to social priors during action prediction in adults with autism spectrum disorders
    Chambon, V.; Farrer, C.; Pacherie, E.; Jacquet, P.O.; Leboyer, M.; Zalla, T.
  • Action perception is intact in autism spectrum disorder
    Cusack, J.P.; Williams, J.H.; Neri, P.
  • Behavioral characterization of prediction and internal models in adolescents with autistic spectrum disorders
    Ego, C.; Bonhomme, L.; de Xivry, J.J.O.; Da Fonseca, D.; Lefèvre, P.; Masson, G.S.; Deruelle, C.
  • Risk factors for autism: an Egyptian study
    El-Baz, F.; Ismael, N.A.; El-Din, S.M.N.
  • Young children with autism spectrum disorder use predictive eye movements in action observation
    Falck-Ytter, T.
  • Visual search for basic emotional expressions in autism; impaired processing of anger, fear and sadness, but a typical happy face advantage
    Farran, E.K.; Branson, A.; King, B.J.
  • Statistical learning of higher-order temporal structure from visual shape sequences
    Fiser, J.; Aslin, R.N.
  • Narratives published on the internet by parents of children with autism: what do they reveal and why is it important?
    Fleischmann, A.
  • Implicit learning in individuals with autism spectrum disorders: a meta-analysis
    Foti, F.; De Crescenzo, F.; Vivanti, G.; Menghini, D.; Vicari, S.
  • Differential fear conditioning in Asperger’s syndrome: implications for an amygdala theory of autism
    Gaigg, S.B.; Bowler, D.M.
  • Teleological reasoning in infancy: the naıve theory of rational action
    Gergely, G.; Csibra, G.
  • Statistical word learning in children with autism spectrum disorder and specific language impairment
    Haebig, E.; Saffran, J.R.; Ellis Weismer, S.
  • Implicit learning as an ability
    Kaufman, S.B.; DeYoung, C.G.; Gray, J.R.; Jiménez, L.; Brown, J.; Mackintosh, N.
  • Emotion recognition in children and adolescents with autism spectrum disorders
    Kuusikko, S.; Haapsamo, H.; Jansson-Verkasalo, E.; Hurtig, T.; Mattila, M.L.; Ebeling, H.; Moilanen, I.
  • Applying signal‐detection theory to the study of observer accuracy and bias in behavioral assessment
    Lerman, D.C.; Tetreault, A.; Hovanetz, A.; Bellaci, E.; Miller, J.; Karp, H.; Mahmood, A.; Strobel, M.; Mullen, S.; Keyl, A.; Toupard, A.
  • Accounting for speed – accuracy tradeoff in perceptual learning
    Liu, C.C.; Watanabe, T.
  • Procedural learning is impaired in dyslexia: evidence from a meta-analysis of serial reaction time studies
    Lum, J.A.; Ullman, M.T.; Conti-Ramsden, G.
  • Visual integration of fear and anger emotional cues by children on the autism spectrum and neurotypical peers: an EEG study
    Malaia, E.; Cockerham, D.; Rublein, K.
  • Children with autism do not show sequence effects with auditory stimuli
    Molesworth, C.; Chevallier, C.; Happé, F.; Hampton, J.A.
  • Atypical rapid audio‐visual temporal recalibration in autism spectrum disorders
    Noel, J.P.; De Niear, M.A.; Stevenson, R.; Alais, D.; Wallace, M.T.
  • Statistical learning in specific language impairment and autism spectrum disorder: a meta-analysis
    Obeid, R.; Brooks, P.J.; Powers, K.L.; Gillespie-Lynch, K.; Lum, J.A.
  • Difficulties with multi-sensory fear conditioning in individuals with autism spectrum disorder
    Powell, P.S.; Travers, B.G.; Klinger, L.G.; Klinger, M.R.
  • Aging, practice, and perceptual tasks: a diffusion model analysis
    Ratcliff, R.; Thapar, A.; McKoon, G.
  • Modeling regularities in response time and accuracy data with the diffusion model
    Ratcliff, R.; Smith, P.L.; McKoon, G.
  • The developmental trajectory of children’s auditory and visual statistical learning abilities: modality-based differences in the effect of age
    Raviv, L.; Arnon, I.
  • Statistical learning as a basis for social understanding in children
    Ruffman, T.; Taumoepeau, M.; Perkins, C.
  • Cognitive mechanisms underlying action prediction in children and adults with autism spectrum condition
    Schuwerk, T.; Sodian, B.; Paulus, M.
  • Measuring individual differences in statistical learning: current pitfalls and possible solutions
    Siegelman, N.; Bogaerts, L.; Frost, R.
  • Is implicit sequence learning impaired in schizophrenia? A meta-analysis
    Siegert, R.J.; Weatherall, M.; Bell, E.M.
  • Autism as a disorder of prediction
    Sinha, P.; Kjelgaard, M.M.; Gandhi, T.K.; Tsourides, K.; Cardinaux, A.L.; Pantazis, D.; Diamond, S.P.; Held, R.M.
  • Better fear conditioning is associated with reduced symptom severity in autism spectrum disorders
    South, M.; Larson, M.J.; White, S.E.; Dana, J.; Crowley, M.J.
  • Assessment of fear in children with an autism spectrum disorder
    Turner, L.B.; Romanczyk, R.G.
  • Recognition of emotions in autism: a formal meta-analysis
    Uljarevic, M.; Hamilton, A.
  • The profile of memory function in children with autism
    Williams, D.L.; Goldstein, G.; Minshew, N.J.
  • Functions of Learning Rate in Adaptive Reward Learning
    Wu, X.; Wang, T.; Liu, C.; Wu, T.; Jiang, J.; Zhou, D.; Zhou, J.