Response selection codes in neurophysiological data predict conjoint effects of controlled and automatic processes during response inhibition

Abstract

The inhibition of prepotent responses is a requirement for goal-directed behavior and several factors determine corresponding successful response inhibition processes. One factor relates to the degree of automaticity of pre-potent response tendencies and another factor relates to the degree of cognitive control that is exerted during response inhibition. However, both factors can conjointly modulate inhibitory control. Cognitive theoretical concepts suggest that codings of stimulus-response translations may underlie such conjoint effects. Yet, it is unclear in how far such specific codes, as assumed in cognitive psychological concepts, are evident in neurophysiological processes and whether there are specific functional neuroanatomical structures associated with the processing of such codes. Applying a temporal decomposition method of EEG data in combination with source localization methods we show that there are different, intermingled codes (i.e., "stimulus codes" and "response selection codes") at the neurophysiological level during conjoint effects of "automatic" and "controlled" processes in response inhibition. Importantly, only "response selection codes" predict behavioral performance, and are subject to conjoint modulations by "automatic" and "controlled" processes. These modulations are associated with inferior and superior parietal areas (BA40/BA7), possibly reflecting an updating of internal representations when information is complex and probably difficult to categorize, but essential for behavioral control. Codes proposed by cognitive, psychological concepts seem to have a neurophysiological analogue that fits into current views on functions of inferior and superior parietal regions.

The neurophysiological basis of developmental changes during sequential cognitive flexibility between adolescents and adults

“…This process occurred approximately 100 ms later than processes reflected by the P1 in the S-cluster. The C-cluster has previously been found to be associated with response selection and control processes in the N2 time window (Chmielewski et al, 2018;Mückschel et al, 2017), which is in line with theoretical considerations suggesting that the N2 reflects intermingled perceptual and response-related (selection) processes (Folstein & Van Petten, 2008). The effect observed for the C-cluster was associated with the anterior cingulate cortex (BA24, BA32).…”

“…Previous findings have shown that the N2 reflects intermingled perceptual and response-related (selection) processes (Folstein & Van Petten, 2008). Using RIDE it was recently shown that these intermingled coding levels in the N2 are reflected by the S-cluster and the C-cluster (Chmielewski, Mückschel, & Beste, 2018;Mückschel et al, 2017). If the modulation of response selection processes is the most important factor underlying processes to overcome backward inhibition in adolescents compared with adults, it is likely that the above hypothesized effects for the N2 will be evident in the C-cluster data in the N2 time window.…”

“…RIDE has been shown to overcome these limitations (Ouyang et al, 2011(Ouyang et al, , 2015bVerleger et al, 2014). Using RIDE it was recently shown that these intermingled coding levels in the N2 are reflected by the S-cluster and the C-cluster (Chmielewski, Mückschel, & Beste, 2018;Mückschel et al, 2017). Utilizing RIDE analysis, recent examinations show that N2-related processes are captured by the S-cluster and the C-cluster in response selection and inhibition paradigms .…”

The system‐neurophysiological basis for how methylphenidate modulates perceptual–attentional conflicts during auditory processing

“…As pointed out in previous studies, exogenous components (like the P1 or N1) are more closely time locked to stimulus onsets, whereas later endogenous components, are more variable in latency (Ouyang, Schacht, Zhou, & Sommer, 2013). In fact, it has been shown several times that processes occurring later in time-processes that reflected by the C-cluster-show considerable differences to original ERP data (Bluschke, Chmielewski et al, 2017;Chmielewski et al, 2018;Mückschel, Chmielewski et al, 2017;Ouyang, Hildebrandt, Sommer, & Zhou, 2017;Ouyang, Sommer, & Zhou, 2015b). In fact, it has been shown several times that processes occurring later in time-processes that reflected by the C-cluster-show considerable differences to original ERP data (Bluschke, Chmielewski et al, 2017;Chmielewski et al, 2018;Mückschel, Chmielewski et al, 2017;Ouyang, Hildebrandt, Sommer, & Zhou, 2017;Ouyang, Sommer, & Zhou, 2015b).…”

“…As outlined in Section 1, we perform RIDE according to established protocols (Verleger, Metzner, Ouyang, Śmigasiewicz, & Zhou, 2014) and the RIDE toolbox (manual available on http://cns.hkbu.edu.hk/RIDE.htm) to account for intraindividual variability in the data (Ouyang et al, 2011) and to dissociate between different coding levels that are otherwise intermingled in classical ERP components (Chmielewski, Mückschel, & Beste, 2018;Mückschel, Chmielewski et al, 2017). Mathematical details on the RIDE method can be found in Ouyang et al (2011).…”