Premium
Annual Research Review: Reaction time variability in ADHD and autism spectrum disorders: measurement and mechanisms of a proposed trans‐diagnostic phenotype
Author(s) -
Karalunas Sarah L.,
Geurts Hilde M.,
Konrad Kerstin,
Bender Stephan,
Nigg Joel T.
Publication year - 2014
Publication title -
journal of child psychology and psychiatry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.652
H-Index - 211
eISSN - 1469-7610
pISSN - 0021-9630
DOI - 10.1111/jcpp.12217
Subject(s) - attention deficit hyperactivity disorder , autism spectrum disorder , autism , psychology , arousal , cognition , neuroimaging , attention deficit , audiology , neuroscience , clinical psychology , developmental psychology , medicine
Background Intraindividual variability in reaction time ( RT ) has received extensive discussion as an indicator of cognitive performance, a putative intermediate phenotype of many clinical disorders, and a possible trans‐diagnostic phenotype that may elucidate shared risk factors for mechanisms of psychiatric illnesses. Scope and Methodology Using the examples of attention deficit hyperactivity disorder ( ADHD ) and autism spectrum disorders ( ASD ), we discuss RT variability. We first present a new meta‐analysis of RT variability in ASD with and without comorbid ADHD . We then discuss potential mechanisms that may account for RT variability and statistical models that disentangle the cognitive processes affecting RT s. We then report a second meta‐analysis comparing ADHD and non‐ ADHD children on diffusion model parameters. We consider how findings inform the search for neural correlates of RT variability. Findings Results suggest that RT variability is increased in ASD only when children with comorbid ADHD are included in the sample. Furthermore, RT variability in ADHD is explained by moderate to large increases ( d = 0.63–0.99) in the ex‐Gaussian parameter τ and the diffusion parameter drift rate, as well as by smaller differences ( d = 0.32) in the diffusion parameter of nondecision time. The former may suggest problems in state regulation or arousal and difficulty detecting signal from noise, whereas the latter may reflect contributions from deficits in motor organization or output. The neuroimaging literature converges with this multicomponent interpretation and also highlights the role of top‐down control circuits. Conclusion We underscore the importance of considering the interactions between top‐down control, state regulation (e.g. arousal), and motor preparation when interpreting RT variability and conclude that decomposition of the RT signal provides superior interpretive power and suggests mechanisms convergent with those implicated using other cognitive paradigms. We conclude with specific recommendations for the field for next steps in the study of RT variability in neurodevelopmental disorders.