Intra-Subject Variability, Intelligence, and ADHD Traits in a Community-Based Sample

Abstract

Objective: The present study investigates the predictive validity of intra-subject variability (ISV) for ADHD traits in a community-based sample and the stability of the relationship between ISV and fluid intelligence (gf) across the continuum of ADHD traits. Method: Age-residualized data from 426 participants (8–18 years, 6% ADHD) was used to investigate whether ex-Gaussian and DDM parameters derived from simple choice-reaction-time tasks can predict continuously assessed ADHD traits. Multiple-Group-Analyses and Latent-Moderated-Structural-Equations were used to test whether ADHD traits moderate the relationship between ISV and gf. Results: σ and μ of the ex-Gaussian model as well as DDM parameters drift rate ( v) and boundary separation ( a) significantly predicted general ADHD traits, while τ predicted attention difficulties specifically. Across the ADHD continuum, σ and v were significant predictors of gf. Conclusion: The results confirm the link between ISV and ADHD. The relationship between ISV and gf appears stable across the ADHD continuum.

Effect of transcranial static magnetic stimulation over unilateral or bilateral motor association cortex on performance of simple and choice reaction time tasks

“…RT reflects speed of information processing, while SD of RT reflects consistency in processing speed ( Jensen, 1992 ), suggesting that alertness and sensory processing were inconsistent across trials after triple tSMS over the bilateral MAC. Also, SD of RT has been reported to be more sensitive than mean RT as a marker of cognitive impairment ( Klein et al, 2006 ; Schulz-Zhecheva et al, 2023 ). Moreover, Gonzalez-Rosa et al (2015) demonstrated that visual search RT was prolonged after tSMS over the occipital cortex only when the task was difficult.…”

ADHD Adults Show Lower Interindividual Similarity in Ex-Gaussian Reaction Time Vectors for Congruent Stimuli Compared to Control Peers

“…The intraindividual variability in individuals with ADHD has been widely analyzed with or without the ex-Gaussian distributions of RT (Hohle, 1965; Ratcliff, 1979), which has proven to be a valuable tool in characterizing differences in RT between individuals with ADHD and controls (Bella-Fernández et al, 2023; Bluschke et al, 2021; Buzy et al, 2009; Castellanos et al, 2009; Flehmig et al, 2007; Fredriksen et al, 2021; Galloway-Long et al, 2022; Gmehlin et al, 2014; Gu et al, 2013; Guo et al, 2022; Hervey et al, 2006; Horowitz et al, 2020; Kofler et al, 2013; Kuntsi & Klein, 2012; Lin et al, 2015; Leth-Steensen et al, 2000; Schmid et al, 2020; Schulz-Zhecheva et al, 2023; Vainieri et al, 2022; Vaurio et al, 2009). Computational models using ex-Gaussian distributions used in conjunction with clinical assessments refine our understanding of cognitive concepts as: slow processing speed, and inhibition failures, which are commonly found in Individuals with ADHD (Ging-Jehli et al, 2021; Nikolas et al, 2019).…”