Auditory and Visual Statistical Learning Are Not Related to ADHD Symptomatology: Evidence From a Research Domain Criteria (RDoC) Approach.

Kaitlyn M. A. Parks,Ryan A. Stevenson

doi:10.3389/fpsyg.2018.02502

Abstract

Statistical learning is an implicit process that allows individuals to track and predict incoming events from their environment. Given that information is highly structured over time, events become predictable, allowing these individuals to make better sense of their environment. Among the studies that have examined statistical learning in attention deficit/hyperactivity disorder (ADHD), findings have been mixed. Our goal was to examine whether increased ADHD symptomatology related to decreased auditory and visual statistical learning abilities. To investigate this, we examined the entire range of ADHD symptomatology using a Research Domain Criteria approach with a clinically reliable questionnaire in addition to well-established auditory and visual statistical learning paradigms. Total ADHD symptomatology was not related to auditory and visual statistical learning. An identical pattern emerged when inattention and hyperactivity components were separated, indicating that neither of these distinct behavioral symptoms of ADHD are related to statistical learning abilities. Findings from the current study converge with other studies but go beyond finding a lack of a significant relationship – through Bayesian analyses, these data provide novel evidence directly supporting the hypothesis that ADHD symptomatology and statistical learning are decoupled. This finding held for overall levels of ADHD symptomatology as well as the subdomains of inattention and hyperactivity, suggesting that the ability to pick up on patterns in both auditory and visual domains is intact in ADHD. Future work should consider investigating statistical learning in ADHD across ages and beyond auditory and visual domains.

Highlights

Similar events and pieces of information tend to co-occur reliably within the environment
Auditory and visual statistical learning were not significantly related to overall attention deficit/hyperactivity disorder (ADHD)
A second multiple regression was performed to determine if inattention and hyperactivity uniquely contributed to visual statistical learning and the overall model was not significant, F(2,95) = 0.181, p = 0.835, R2 = 0.004, BF10 = 0.078

Summary

Introduction

Similar events and pieces of information tend to co-occur reliably within the environment. The ability to identify and predict these statistical relationships in the environment is a process referred to as statistical learning. Extracting such probabilistic information is important for individuals to make sense of their world and has been shown to play a role in both language proficiency. Statistical Learning and ADHD (Kidd, 2012) and predicting behavioral patterns (Baldwin et al, 2008). The ability to track and predict statistical patterns has been observed across many types of tasks and stimuli, including visual stimuli (Kirkham et al, 2002), tactile stimuli (Conway and Christiansen, 2005), non-linguistic sounds (Gebhart et al, 2009), auditory syllables (Saffran et al, 1996, 1999), and within scenes and in response to body movements (Baldwin et al, 2008)

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