Andrew Crow ’16, Carhart Fellow in Clinical Psychology
This week was rather busy, as I have begun brainstorming research designs for what will hopefully be an honors thesis, in addition to the normal routine of data collection, data scoring, and data entry. On Wednesday, Dr. Nikolas and I met to discuss four empirical works. And finally, I had a lab visit on Thursday morning, which went pretty smoothly.
In their paper exploring diffusion modeling of reaction time variability, Karalunas & Huang-Pollock (2013) intended to examine the relationship between reaction time distributions and response inhibition and working memory in a sample of children with attention-deficit/hyperactivity disorder (ADHD). Perhaps as a result of ADHD, children and adults with ADHD often prioritize speed of response over accuracy of response, spurring what we know to be errors of commission (i.e. responding when one isn’t supposed to), an index of hyperactivity-impulsivity. Errors of omission (e.g. failing to respond when one is supposed to), on the other hand, are a hallmark of inattention. Despite these rather straightforward concepts, decision-making processes involved in responding (e.g. stimulus coding, motor preparation) confound interpretations of reaction time data. As demonstrated in Figure 1, the diffusion model accounts for this by producing three distinct variables: drift rate (rate of information processing), boundary separation (speed-accuracy trade-offs), and non-decision time (completion of preparatory processes prior to decision-making).
What Karalunas & Huang-Pollock found was such that children with ADHD had slower drift rates and faster non-decision times, yet there were no significant differences in boundary separation. This would indicate an overall slower rate of processing consistent with previous findings, while explaining faster rates of responding. Moreover, children with ADHD and controls performed significantly different on measures of working memory and response inhibition. Figure 2 and 3 extrapolate on findings from mediation analyses between ADHD status and reaction time and working memory.
Similar to Karalunas & Huang-Pollock (2013), Ghemlin et al. (2014) examined reaction time data using ex-Gaussian models (Figure 4), which focus more on the variability (or distribution) in reaction time, as opposed to the correlates of information processing and executive functioning. In their study, Ghemlin and colleagues compared mean reaction time (RT), standard deviations of reaction time (SDRT), and exponential functions of reaction time data (indicating extreme slow responses) in adults with ADHD versus controls. What is often found is that adults with ADHD have longer mean reaction times versus children with ADHD, which may suggest adaptive mechanisms or increased variability in response inhibition over development. They predicted that adults with ADHD would have increased frequencies of abnormally slow response comparatively, resulting in differences in variability of responses. When it comes to commission errors, the authors predicted non-significant differences. However, there was a predicted relationship between RT variability and omission errors. Figure 5 describes graphically what Ghemlin and colleagues found.
Ghemlin et al. (2014) found that there were group differences in slow responses and non-significant differences in mean reaction time and commission errors. In addition, there was a significant relationship between RT variability and omission errors. These findings may suggest adults with ADHD do not preform significantly worse on response inhibition, though they respond less consistently or not at all at greater frequency. Following this, it may be the increased RT variability is related to the greater number of slow responses, indicating more of an attentional dysfunction within ADHD as opposed to a deficit in response inhibition.
To investigate the role of delay aversion and impulsive drive for reward, Marco et al. (2009) studied children and adolescents with ADHD and their unaffected siblings and their choices on measures of delay aversion. Considering children with ADHD tend to prefer smaller rewards sooner (SS) over longer rewards later (LL), Marco et al. hypothesized children with ADHD and their siblings would prefer SS over LL compared to controls. As predicted, children with ADHD and their siblings chose SS over LL more frequently than controls with moderate effects (Figure 6). There was an interaction between group and condition, indicating delay aversion and immediate drive for reward may be contributing mutually to children with ADHD and their siblings’ choices of SS over LL.
Wilcutt et al. (2010) exploring etiological and neuropsychological contributions to comorbidity between reading disorder (RD) and ADHD, given there are often shared deficits between RD and ADHD in the areas of processing speed, verbal working memory, response variability, and response inhibition. Wilcutt and colleagues studied performance on a range of neurocognitive tasks within a sample of twin pairs from the Colorado Learning Disabilities Research Center (CLDRC) twin study, and predicted performance would be heritable among twin pairs and processing speed may mediate phenotypic covariance between RD and ADHD. Wilcutt and colleagues found that performance on measures of single-word reading, inattention, and hyperactivity-impulsivity was highly heritable, and shared significant environmental influences with reading difficulty. These findings indicate comorbidity may be due to shared genetic influence among RD and ADHD, where processing speed may be mediating this comorbidity. Figure 7 demonstrates genetic influences upon various constructs, with solid lines indicating significant contributions of one genetic phenotype to a certain construct. Note A1, which contributes to constructs of ADHD, reading difficulty, and processing speed.
Ghemlin, D., Fuermaier, A. B. M., Walther, S., Debelak, R., Rentrop, M., Westermann, C. Sharma, A., Tucha, L., Koerts, J., Tucha, O., Weisbrod, M., & Aschenbrenner, S. (2014). Intraindividual variability in inhibitory function in adults with ADHD: An ex-Gaussian approach. PLoS ONE, 9(12), e112298. doi:10.1371/journal.pone.0112298
Karalunas, S. J. & Huang-Pollock, C. L. (2013). Integrating impairments in reaction time and executive function using a diffusion model framework. Journal of Abnormal Child Psychology, 41(5), 837-850. doi:10.1007/s10802-013-9715-2
Marco, R., Miranda, A., Schlotz, W., Melia, A., Mulligan, A., Müller, U., Andreou, P., Butler, L., Christiansen, H., Gabriels, I., Medad, S., Albrecht, B., Uebel, H., Asherson, P., Banaschewski, T., Gill, M., Kuntsi, J., Mulas, F., Oades, R., Roeyers, H., Steinhausen, H., Rothenberger, A., Faraone, S. V., & Sonuga-Barke, E. J. S. (2009). Delay and reward choice in ADHD: An experimental test of the role of delay aversion. Neuropsychology, 23(3), 367-380. doi:10.1037/a0014914
Wilcutt, E. G., Betjemann, R. S., McGarth, L. M., Chhabildas, N. A., Olson, R. K., DeFries, J. C., & Pennington, B. F. (2010). Etiology and neuropsychology of comorbidity between RD and ADHD: The case for multiple-deficit models. Cortex, 46(10), 1345-1361. doi:10.1016/j.cortex.2010.06