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

Abstract

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.

Keywords: EEG; MVPA; action control; group ICA; insular cortex; stopping; superior parietal cortex.

FIGURE 1

Schematic illustration of the implemented task with all possible stimulus configurations. Congruent and incongruent trials in the Go condition are shown above, while the congruent and incongruent trials in the Stop condition are shown below.

FIGURE 2

Stop signal reaction time (SSRT) data for congruent and incongruent Stop trials. For each condition, a boxplot, and the individual data points as well as the probability distribution are given.

FIGURE 3

CORRMAP and multi‐variate pattern analysis (MVPA) results for selected independent component (IC) pairs. (a–d) The binary classification performances, the scalp topographies of the selected of the congruent and incongruent conditions (left), and the temporal generalizations (right) of the IC pairs 1–4, respectively. For the binary classification, the shaded error bars represent standard deviation. The scalp topographies reveal the weighting matrices of each IC.

FIGURE 4

Standardized low‐resolution brain electromagnetic tomography (sLORETA)‐derived maps for selected independent component (IC) pairs (1–4) indicate the sources of maximal differences between congruent and incongruent Nogo trials in time windows showing above‐chance level classification performance and temporal stability.

FIGURE 5

Root mean square error (RMSE) values (with 95% confidence interval) of the nonlinear regression for all independent component (IC) pairs used to find the association between area under the curve (AUC) values and the difference between stop signal reaction times in two congruent and incongruent conditions. Positive values indicate larger activation in congruent Nogo trials, while negative values indicate larger activation for incongruent Nogo trials.