Weak Priors versus Overfitting of Predictions in Autism: Reply to Pellicano and Burr (<i>TICS</i>, 2012)

Cruys, Sander Van de; de-Wit, Lee; Evers, Kris; Boets, Bart; Wagemans, Johan

doi:10.1068/i0580ic

Cited by 65 publications

(59 citation statements)

References 12 publications

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“…We suggested that this process may be atypical in autism, in that the internal priors are underweighted and less used than in typical individuals. Our theory has been followed by several others along similar lines (8)(9)(10)(11).…”

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confidence: 71%

“…These mechanisms may be more readily explained by the recent Bayesian models of autism (4,(8)(9)(10)(11), which clearly predict that individuals with autism should give less weighting to prior or predictive information, such as the consequences of previous stimulation. Critically, in this study, number perception of autistic children postadaptation was more accurate, in that the target patch of dots corresponded better to physical reality than to expectations.…”

Section: Discussionmentioning

confidence: 99%

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Children with autism spectrum disorder show reduced adaptation to number

Turi

Burr

Igliozzi

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

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Autism is known to be associated with major perceptual atypicalities. We have recently proposed a general model to account for these atypicalities in Bayesian terms, suggesting that autistic individuals underuse predictive information or priors. We tested this idea by measuring adaptation to numerosity stimuli in children diagnosed with autism spectrum disorder (ASD). After exposure to large numbers of items, stimuli with fewer items appear to be less numerous (and vice versa). We found that children with ASD adapted much less to numerosity than typically developing children, although their precision for numerosity discrimination was similar to that of the typical group. This result reinforces recent findings showing reduced adaptation to facial identity in ASD and goes on to show that reduced adaptation is not unique to faces (social stimuli with special significance in autism), but occurs more generally, for both parietal and temporal functions, probably reflecting inefficiencies in the adaptive interpretation of sensory signals. These results provide strong support for the Bayesian theories of autism.utism is a heritable, lifelong neurodevelopmental condition with striking effects on social communication. The condition, however, is also associated with a range of nonsocial symptoms, including both hypersensitivity and hyposensitivity to perceptual stimuli, and sensory-seeking behaviors such as attraction to light, intense looking at objects, and fascination with brightly colored objects. These sensory atypicalities, which now form part of the diagnostic criteria for autism (1), can have debilitating effects on the lives of autistic people (2) and their families (3).We have recently proposed a Bayesian account of autism (4), suggesting that it is not sensory processing itself that is disrupted in individuals with autism, but the interpretation of the sensory input. The Bayesian class of theories, including predictive coding and other generative models (5-7), assumes that perception is an optimized combination of external sensory data (the likelihood) and an internal model (the prior). We suggested that this process may be atypical in autism, in that the internal priors are underweighted and less used than in typical individuals. Our theory has been followed by several others along similar lines (8-11).The suggestion of underutilization of priors leads to several specific predictions. One strong prediction is that autistic individuals should show reduced adaptation aftereffects. Adaptation, which occurs throughout sensory systems, represents a form of experience-dependent plasticity in which our current sensory experience is intimately affected by how we viewed the world only moments before. It is widely held to pose numerous functional advantages (12-16), including serving to autocalibrate perceptual systems to their environment by dynamically tuning its responses to match the distribution of stimuli to make maximal use of the limited working range of the system (12-16). It achieves this by reducing the transm...

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confidence: 71%

Section: Discussionmentioning

confidence: 99%

Children with autism spectrum disorder show reduced adaptation to number

Turi

Burr

Igliozzi

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

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“…As outlined in Section 2.2, this relates to the idea that the perceptual system makes predictions or hypotheses about the nature of upcoming stimuli, which then become matched with incoming sensory information (Friston, 2005(Friston, , 2008. It has been argued that in autism these prior predictions may be less precise (Pellicano & Burr, 2012) or deployed in an inflexible manner possibly even resulting in hyper-precision Van de Cruys et al, 2013), meaning overall that perception becomes more sensitive to incoming stimuli but can be less influenced by context. This lack of top-down prediction in ASD has been suggested to underlie problems during the perception of visually ambiguous stimuli like illusory figures and could even extend to social stimuli such as facial expressions and biological movement .…”

Section: Deficient Predictive Codingmentioning

confidence: 99%

“…An optimal balance in SNR including top-down regulation in form of selection and filtering allows for optimal predictive coding of the environment (see also Pellicano & Burr, 2012;Van de Cruys et al, 2013). With intact top-down regulation (filtering, selection, integration) local encoding can be predictive for "what" should happen and "when" (Arnal & Giraud, 2012), thus mainly processing deviations from expectations, resulting in a system that is highly efficient and proactive to respond (see Fig.…”

Section: Core Features Of the Proposed Frameworkmentioning

confidence: 99%

Brain oscillations and connectivity in autism spectrum disorders (ASD): new approaches to methodology, measurement and modelling

Kessler

Seymour

Rippon

2016

Neuroscience & Biobehavioral Reviews

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Please cite this article as: Kessler, K., Seymour, R.A., Rippon, G., Brain oscillations and connectivity in autism spectrum disorders (ASD): new approaches to methodology, measurement and modelling.Neuroscience and Biobehavioral Reviews http://dx.doi.org/10. 1016/j.neubiorev.2016.10.002 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Abstract:Although atypical social behaviour remains a key characterisation of ASD, the presence of sensory and perceptual abnormalities has been given a more central role in recent classification changes. An understanding of the origins of such aberrations could thus prove a fruitful focus for ASD research. Early neurocognitive models of ASD suggested that the study of high frequency activity in the brain as a measure of cortical connectivity might provide the key to understanding the neural correlates of sensory and perceptual deviations in ASD. As our review shows, the findings from subsequent research have been inconsistent, with a lack of agreement about the nature of any high frequency disturbances in ASD brains.Based on the application of new techniques using more sophisticated measures of brain synchronisation, direction of information flow, and invoking the coupling between high and low frequency bands, we propose a framework which could reconcile apparently conflicting findings in this area and would be consistent both with emerging neurocognitive models of autism and with the heterogeneity of the condition.

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“…In many of the recent proposals for ASD, deficient precision estimation is assumed to be key (Lawson et al, 2014;Palmer, Paton, Kirkovski, Enticott, & Hohwy, 2015;Pellicano & Burr, 2012;Van de Cruys, de-Wit, Evers, Boets, & Wagemans, 2013;Van de Cruys et al, 2014). Our own account, termed ''HIPPEA" (for High, Inflexible Precision of Prediction Errors in Autism), assumes that bottomup prediction errors are assigned a precision that is too high and not adapted (inflexible) to the uncertainty in the context (Van de Cruys et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Disentangling signal and noise in autism spectrum disorder

Cruys

Hallen

Wagemans

2017

Brain and Cognition

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a b s t r a c tPredictive coding has recently been welcomed as a fruitful framework to understand autism spectrum disorder. Starting from an account centered on deficient differential weighting of prediction errors (based in so-called precision estimation), we illustrate that individuals with autism have particular difficulties with separating signal from noise, across different tasks. Specifically, we discuss how deficient precision-setting is detrimental for learning in unstable environments, for context-dependent assignment of salience to inputs, and for robustness in perception, as illustrated in coherent motion paradigms.

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Weak Priors versus Overfitting of Predictions in Autism: Reply to Pellicano and Burr (TICS, 2012)

Cited by 65 publications

References 12 publications

Children with autism spectrum disorder show reduced adaptation to number

Children with autism spectrum disorder show reduced adaptation to number

Brain oscillations and connectivity in autism spectrum disorders (ASD): new approaches to methodology, measurement and modelling

Disentangling signal and noise in autism spectrum disorder

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