title: Children on the autism spectrum update their behaviour in response to a volatile environment.
abstract: Typical adults can track reward probabilities across trials to estimate the volatility of the environment and use this information to modify their learning rate (Behrens et al., 2007). In a stable environment, it is advantageous to take account of outcomes over many trials, whereas in a volatile environment, recent experience should be more strongly weighted than distant experience. Recent predictive coding accounts of autism propose that autistic individuals will demonstrate atypical updating of their behaviour in response to the statistics of the reward environment. To rigorously test this hypothesis, we administered a developmentally appropriate version of Behrens et al.'s (2007) task to 34 cognitively able children on the autism spectrum aged between 6 and 14 years, 32 age- and ability-matched typically developing children and 19 typical adults. Participants were required to choose between a green and a blue pirate chest, each associated with a randomly determined reward value between 0 and 100 points, with a combined total of 100 points. On each trial, the reward was given for one stimulus only. In the stable condition, the ratio of the blue or green response being rewarded was fixed at 75:25. In the volatile condition, the ratio alternated between 80:20 and 20:80 every 20 trials. We estimated the learning rate for each participant by fitting a delta rule model and compared this rate across conditions and groups. All groups increased their learning rate in the volatile condition compared to the stable condition. Unexpectedly, there was no effect of group and no interaction between group and condition. Thus, autistic children used information about the statistics of the reward environment to guide their decisions to a similar extent as typically developing children and adults. These results help constrain predictive coding accounts of autism by demonstrating that autism is not characterized by uniform differences in the weighting of prediction error.
authors: Department of Experimental Psychology, University of Oxford, UK.
authors: Catherine
authors: 0000-0001-6862-2525
authors: C
authors: Manning
authors: Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London, UK.
authors: James
authors: J
authors: Kilner
authors: Centre for Research in Autism and Education (CRAE), UCL Institute of Education, University College London, UK.
authors: Louise
authors: L
authors: Neil
authors: Centre for Research in Autism and Education (CRAE), UCL Institute of Education, University College London, UK.
authors: Themelis
authors: T
authors: Karaminis
authors: Centre for Research in Autism and Education (CRAE), UCL Institute of Education, University College London, UK.|School of Psychology, University of Western Australia, Australia.
authors: Elizabeth
authors: E
authors: Pellicano
mesh_terms: D000293:Adolescent; D000328:Adult; D000704:Analysis of Variance; D001245:Association Learning; D001321:Autistic Disorder / Q000503:physiopathology* / Q000523:psychology*; D016022:Case-Control Studies; D002648:Child; D002755:Choice Behavior / Q000502:physiology; D004777:Environment; D005260:Female; D006801:Humans; D007858:Learning; D008297:Male; D011337:Probability Learning; D021641:Recognition, Psychology / Q000502:physiology; D012201:Reward; D012919:Social Behavior