Brief Research Report ARTICLE
Auditory and Visual Statistical Learning are not Related to ADHD Symptomatology: Evidence from a Research Domain Criteria (RDoC) Approach
- 1University of Western Ontario, Canada
- 2York University, Canada
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 behavioural 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.
Keywords: hyperactivity, inattention, statistical learning, auditory, visual
Received: 15 Aug 2018;
Accepted: 26 Nov 2018.
Edited by:George Kachergis, Stanford University, United States
Reviewed by:Christopher I. Petkov, Newcastle University, United Kingdom
Joseph A. King, Technische Universität Dresden, Germany
Copyright: © 2018 Parks and Stevenson. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Miss. Kaitlyn M. Parks, University of Western Ontario, London, Canada, email@example.com