EEG tensor decomposition delineates neurophysiological principles underlying conflict-modulated action restraint and action cancellation.

Executive functions are essential for adaptive behavior. One executive function is the so-called 'interference control' or conflict monitoring another one is inhibitory control (i.e., action restraint and action cancelation). Recent evidence suggests an interplay of these processes, which is conceptually relevant given that newer conceptual frameworks imply that nominally different action/response control processes are explainable by a small set of cognitive and neurophysiological processes. The existence of such overarching neural principles has as yet not directly been examined. In the current study, we therefore use EEG tensor decomposition methods, to look into possible common neurophysiological signatures underlying conflict-modulated action restraint and action cancelation as mechanism underlying response inhibition. We show how conflicts differentially modulate action restraint and action cancelation processes and delineate common and distinct neural processes underlying this interplay. Concerning the spatial information modulations are similar in terms of an importance of processes reflected by parieto-occipital electrodes, suggesting that attentional selection processes play a role. Especially theta and alpha activity seem to play important roles. The data also show that tensor decomposition is sensitive to the manner of task implementation, thereby suggesting that switch probability/transitional probabilities should be taken into consideration when choosing tensor decomposition as analysis method. The study provides a blueprint of how to use tensor decomposition methods to delineate common and distinct neural mechanisms underlying action control functions using EEG data.

Response stopping under conflict: The integrative role of representational dynamics associated with the insular cortex.

Coping with distracting inputs during goal-directed behavior is a common challenge, especially when stopping ongoing responses. The neural basis for this remains debated. Our study explores this using a conflict-modulation Stop Signal task, integrating group independent component analysis (group-ICA), multivariate pattern analysis (MVPA), and EEG source localization analysis. Consistent with previous findings, we show that stopping performance is better in congruent (nonconflicting) trials than in incongruent (conflicting) trials. Conflict effects in incongruent trials compromise stopping more due to the need for the reconfiguration of stimulus-response (S-R) mappings. These cognitive dynamics are reflected by four independent neural activity patterns (ICA), each coding representational content (MVPA). It is shown that each component was equally important in predicting behavioral outcomes. The data support an emerging idea that perception-action integration in action-stopping involves multiple independent neural activity patterns. One pattern relates to the precuneus (BA 7) and is involved in attention and early S-R processes. Of note, three other independent neural activity patterns were associated with the insular cortex (BA13) in distinct time windows. These patterns reflect a role in early attentional selection but also show the reiterated processing of representational content relevant for stopping in different S-R mapping contexts. Moreover, the insular cortex's role in automatic versus complex response selection in relation to stopping processes is shown. Overall, the insular cortex is depicted as a brain hub, crucial for response selection and cancellation across both straightforward (automatic) and complex (conditional) S-R mappings, providing a neural basis for general cognitive accounts on action control.

Evidence for independent representational contents in inhibitory control subprocesses associated with frontoparietal cortices.

Inhibitory control processes have intensively been studied in cognitive science for the past decades. Even though the neural dynamics underlying these processes are increasingly better understood, a critical open question is how the representational dynamics of the inhibitory control processes are modulated when engaging in response inhibition in a relatively automatic or a controlled mode. Against the background of an overarching theory of perception-action integration, we combine temporal and spatial EEG signal decomposition methods with multivariate pattern analysis and source localization to obtain fine-grained insights into the neural dynamics of the representational content of response inhibition. For this purpose, we used a sample of N = 40 healthy adult participants. The behavioural data suggest that response inhibition was better in a more controlled than a more automated response execution mode. Regarding neural dynamics, effects of response inhibition modes relied on a concomitant coding of stimulus-related information and rules of how stimulus information is related to the appropriate motor programme. Crucially, these fractions of information, which are encoded at the same time in the neurophysiological signal, are based on two independent spatial neurophysiological activity patterns, also showing differences in the temporal stability of the representational content. Source localizations revealed that the precuneus and inferior parietal cortex regions are more relevant than prefrontal areas for the representation of stimulus-response selection codes. We provide a blueprint how a concatenation of EEG signal analysis methods, capturing distinct aspects of neural dynamics, can be connected to cognitive science theory on the importance of representations in action control.

Deep learning on independent spatial EEG activity patterns delineates time windows relevant for response inhibition.

Inhibitory control processes are an important aspect of executive functions and goal-directed behavior. However, the mostly correlative nature of neurophysiological studies was not able to provide insights which aspects of neural dynamics can best predict whether an individual is confronted with a situation requiring the inhibition of a response. This is particularly the case when considering the complex spatio-temporal nature of neural processes captured by EEG data. In the current study, we ask whether independent spatial activity profiles in the EEG data are useful to predict whether an individual is confronted with a situation requiring response inhibition. We combine independent component analysis (ICA) with explainable artificial intelligence approaches (EEG-based deep learning) using data from a Go/Nogo task (N = 255 participants). We show that there are four dissociable spatial activity profiles important to classify Go and Nogo trials as revealed by deep learning. Of note, for all of these four independent activity profiles, neural activity in the time period between 300 and 550 ms after stimulus presentation was most informative. Source localization analyses further revealed regions in the pre-central gyrus (BA6), the middle frontal gyrus (BA10), the inferior frontal gyrus (BA46), and the insular cortex (BA13) were associated with the isolated spatial activity profiles. The data suggest concomitant processes being reflected in the identified time window. This has implications for the ongoing debate on the functional significance of event-related potential correlates of inhibitory control.

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

The integration of perception and action has long been studied in psychological science using overarching cognitive frameworks. Despite these being very successful in explaining perception-action integration, little is known about its neurophysiological and especially the functional neuroanatomical foundations. It is unknown whether distinct brain structures are simultaneously involved in the processing of perception-action integration codes and also to what extent demands on perception-action integration modulate activities in these structures. We investigate these questions in an EEG study integrating temporal and ICA-based EEG signal decomposition with source localization. For this purpose, we used data from 32 healthy participants who performed a 'TEC Go/Nogo' task. We show that the EEG signal can be decomposed into components carrying different informational aspects or processing codes relevant for perception-action integration. Importantly, these specific codes are processed independently in different brain structures, and their specific roles during the processing of perception-action integration differ. Some regions (i.e., the anterior cingulate and insular cortex) take a 'default role' because these are not modulated in their activity by demands or the complexity of event file coding processes. In contrast, regions in the motor cortex, middle frontal, temporal, and superior parietal cortices were not activated by 'default' but revealed modulations depending on the complexity of perception-action integration (i.e., whether an event file has to be reconfigured). Perception-action integration thus reflects a multi-region processing of specific fractions of information in the neurophysiological signal. This needs to be taken into account when further developing a cognitive science framework detailing perception-action integration.

A ventral stream-prefrontal cortex processing cascade enables working memory gating dynamics.

The representation of incoming information, goals and the flexible processing of these are required for cognitive control. Efficient mechanisms are needed to decide when it is important that novel information enters working memory (WM) and when these WM 'gates' have to be closed. Compared to neural foundations of maintaining information in WM, considerably less is known about what neural mechanisms underlie the representational dynamics during WM gating. Using different EEG analysis methods, we trace the path of mental representations along the human cortex during WM gate opening and closing. We show temporally nested representational dynamics during WM gate opening and closing depending on multiple independent neural activity profiles. These activity profiles are attributable to a ventral stream-prefrontal cortex processing cascade. The representational dynamics start in the ventral stream during WM gate opening and WM gate closing before prefrontal cortical regions are modulated. A regional specific activity profile is shown within the prefrontal cortex depending on whether WM gates are opened or closed, matching overarching concepts of prefrontal cortex functions. The study closes an essential conceptual gap detailing the neural dynamics underlying how mental representations drive the WM gate to open or close to enable WM functions such as updating and maintenance.