Passive perceptual learning modulates motor inhibitory control in superior frontal regions

Abstract

Response inhibition is of vital importance in the context of controlling inappropriate responses. The role of perceptual processes during inhibitory control has attracted increased interest. Yet, we are far from an understanding of the mechanisms. One candidate mechanism by which perceptual processes may affect response inhibition refers to "gain control" that is closely linked to the signal-to-noise ratio of incoming information. A means to modulate the signal-to-noise ratio and gain control mechanisms is perceptual learning. In the current study, we examine the impact of perceptual learning (i.e., passive repetitive sensory stimulation) on response inhibition combining EEG signal decomposition with source localization analyses. A tactile GO/NOGO paradigm was conducted to measure action restraint as one subcomponent of response inhibition. We show that passive perceptual learning modulates response inhibition processes. In particular, perceptual learning attenuates the detrimental effect of response automation during inhibitory control. Temporally decomposed EEG data show that stimulus-related and not response selection processes during conflict monitoring are linked to these effects. The superior and middle frontal gyrus (BA6), as well as the motor cortex (BA4), are associated with the effects of perceptual learning on response inhibition. Reliable neurophysiological effects were not evident on the basis of standard ERPs, which has important methodological implications for perceptual learning research. The results detail how lower level sensory plasticity protocols affect higher-order cognitive control functions in frontal cortical structures.

Perception‐action integration during inhibitory control is reflected in a concomitant multi‐region processing of specific codes in the neurophysiological signal

“…C-and R-cluster are estimated in the same way. However, because there will be no response in Nogo trials, only the S-and C-clusters were estimated by subtracting C from the trial to estimate S and vice versa in an iterative way, which is in line with previous studies (Chmielewski et al, 2018;Friedrich & Beste, 2020;Ouyang et al, 2013;Prochnow et al, 2021). The details of the RIDE procedure in a Go/Nogo task are given in Ouyang et al (2013).…”

Motor Imagery EEG neurofeedback skill acquisition in the context of declarative interference and sleep

“…This break block followed MI blocks in all three groups. Watching nature documentaries is a common control task in (motor) skill acquisition associated studies (Bassolino, Campanella, Bove, Pozzo, & Fadiga, 2014;Friedrich & Beste, 2020;Ruffino, Bourrelier, Papaxanthis, Mourey, & Lebon, 2019;Ruffino et al, 2017) but it is usually not integrated in the experimental group procedure as it is in this study. Thus, it cannot be excluded that the break blocks interfered with the formation of MI NF practice effects.…”

Neurophysiological correlates of perception–action binding in the somatosensory system

“…However, stimulus-related processes as reflected by the S-cluster have also been found to be modulated in this context 34 . For example, the P2 component has already been found to be evident in the S-cluster time window 34 and has repeatedly been linked to the allocation of processing resources 35,36 .…”

“…However, stimulus-related processes as reflected by the S-cluster have also been found to be modulated in this context 34 . For example, the P2 component has already been found to be evident in the S-cluster time window 34 and has repeatedly been linked to the allocation of processing resources 35,36 . Based on these findings, it seems reasonable to investigate whether effects are modulated by differences in attentional processing of different compatibility conditions (i.e., repetition or alternation of stimulus features as well as the response).…”