Visual statistical learning and integration of perceptual priors are intact in attention deficit hyperactivity disorder.

Katie L. Richards,Peggy Seriès,Stephen M. Lawrie,Povilas Karvelis,Thilo Kellermann

doi:10.1371/journal.pone.0243100

Katie L. Richards, Peggy Seriès + Show 3 more

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https://doi.org/10.1371/journal.pone.0243100

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Abstract

Deficits in visual statistical learning and predictive processing could in principle explain the key characteristics of inattention and distractibility in attention deficit hyperactivity disorder (ADHD). Specifically, from a Bayesian perspective, ADHD may be associated with flatter likelihoods (increased sensory processing noise), and/or difficulties in generating or using predictions. To our knowledge, such hypotheses have never been directly tested. We here test these hypotheses by evaluating whether adults diagnosed with ADHD (n = 17) differed from a control group (n = 30) in implicitly learning and using low-level perceptual priors to guide sensory processing. We used a visual statistical learning task in which participants had to estimate the direction of a cloud of coherently moving dots. Unbeknown to the participants, two of the directions were more frequently presented than the others, creating an implicit bias (prior) towards those directions. This task had previously revealed differences in other neurodevelopmental disorders, such as autistic spectrum disorder and schizophrenia. We found that both groups acquired the prior expectation for the most frequent directions and that these expectations substantially influenced task performance. Overall, there were no group differences in how much the priors influenced performance. However, subtle group differences were found in the influence of the prior over time. Our findings suggest that the symptoms of inattention and hyperactivity in ADHD do not stem from broad difficulties in developing and/or using low-level perceptual priors.

Highlights

Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental disorder characterized by age-inappropriate levels of inattention, hyperactivity, and/or impulsivity that substantially impact psychosocial functioning [1, 2]
Deficits in visual statistical learning and predictive processing could in principle explain the key characteristics of inattention and distractibility in attention deficit hyperactivity disorder (ADHD)
To our knowledge, such hypotheses have never been directly tested. We here test these hypotheses by evaluating whether adults diagnosed with ADHD (n = 17) differed from a control group (n = 30) in implicitly learning and using low-level perceptual priors to guide sensory processing

Summary

Introduction

Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental disorder characterized by age-inappropriate levels of inattention, hyperactivity, and/or impulsivity that substantially impact psychosocial functioning [1, 2]. It has been proposed that disruptions in the development of frontostriatal and frontocerebellar neural loops result in difficulties in using temporal and contextual structure to guide cognition and behavior This hypothesis of ADHD is in keeping with recent Bayesian predictive coding theories of neuropsychiatric disorders [4, 5]. Bayesian theories assume that cognition, from low-level sensory processing all the way through to higher-level beliefs, are governed by inferential processes [6,7,8,9] In this view, perception is an active process, where percepts are generated by integrating noisy incoming sensory signals (likelihood distribution) with implicit beliefs or expectations about the state of the world (prior distribution). To our knowledge, such hypotheses have never been directly tested

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