Protocol to decode representations from EEG data with intermixed signals using temporal signal decomposition and multivariate pattern-analysis

Sequence-dependent predictive coding during the learning and rewiring of skills

In the constantly changing environment that characterizes our daily lives, the ability to predict and adapt to new circumstances is crucial. This study examines the influence of sequence and knowledge adaptiveness on predictive coding in skill learning and rewiring. Participants were exposed to two different visuomotor sequences with overlapping probabilities. By applying temporal decomposition and multivariate pattern analysis, we dissected the neural underpinnings across different levels of signal coding. The study provides neurophysiological evidence for the influence of knowledge adaptiveness on shaping predictive coding, revealing that these are intricately linked and predominantly manifest at the abstract and motor coding levels. These findings challenge the traditional view of a competitive relationship between learning context and knowledge, suggesting instead a hierarchical integration where their properties are processed simultaneously. This integration facilitates the adaptive reuse of existing knowledge in the face of new learning. By shedding light on the mechanisms of predictive coding in visuomotor sequences, this research contributes to a deeper understanding of how the brain navigates and adapts to environmental changes, offering insights into the foundational processes that underlie learning and adaptation in dynamic contexts.

Revealing the different levels of action monitoring in visuomotor transformation task: Evidence from decomposition of cortical potentials

This study investigates the cortical correlates of motor response control and monitoring, using the Theory of Event Coding (TEC) as a framework to investigate signals related to low-level sensory processing of motor reafference and high-level response monitoring, including verification of response outcomes with the internal model. We used a visuomotor paradigm with two targets at different distances from the participant. For the recorded movement-related cortical potentials (MRCPs), we analyzed their different components and assessed the movement phases during which they are active. Residual iteration decomposition (RIDE) and multivariate pattern analysis (MVPA) were used for this analysis. Using RIDE, we separated MRCPs into signals related to different parallel processes of visuomotor transformation: stimulus processing (S-cluster), motor response preparation and execution (R-cluster), and intermediate processes (C-cluster). We revealed sequential activation in the R-cluster, with execution-related negative components and positive contralateral peaks reflecting reafference processing. We also identified the motor post-imperative negative variation within the R-cluster, highlighting the response outcome evaluation process included in the action file. Our findings extend the understanding of C-cluster signals, typically associated with stimulus-response mapping, by demonstrating C-activation from the preparatory stages through to response termination, highlighting its participation in action monitoring. In addition, we highlighted the ability of MVPA to identify movement-related attribute encoding: where statistical analysis showed independence of stimulus processing activity from movement distance, MVPA revealed distance-related differences in the S-cluster within a time window aligned with the lateralized readiness potential (LRP). This highlights the importance of integrating RIDE and MVPA to uncover the intricate neural dynamics of motor control, sensory integration, and response monitoring.

Neural representations of statistical and rule-based predictions in Gilles de la Tourette syndrome

Gilles de la Tourette syndrome (GTS) is a disorder characterised by motor and vocal tics, which may represent habitual actions as a result of enhanced learning of associations between stimuli and responses (S-R). In this study, we investigated how adults with GTS and healthy controls (HC) learn two types of regularities in a sequence: statistics (non-adjacent probabilities) and rules (predefined order). Participants completed a visuomotor sequence learning task while EEG was recorded. To understand the neurophysiological underpinnings of these regularities in GTS, multivariate pattern analyses on the temporally decomposed EEG signal as well as sLORETA source localisation method were conducted. We found that people with GTS showed superior statistical learning but comparable rule-based learning compared to HC participants. Adults with GTS had different neural representations for both statistics and rules than HC adults; specifically, adults with GTS maintained the regularity representations longer and had more overlap between them than HCs. Moreover, over different time scales, distinct fronto-parietal structures contribute to statistical learning in the GTS and HC groups. We propose that hyper-learning in GTS is a consequence of the altered sensitivity to encode complex statistics, which might lead to habitual actions.

Error-based Implicit Learning in Language: The Effect of Sentence Context and Constraint in a Repetition Paradigm

Prediction errors drive implicit learning in language, but the specific mechanisms underlying these effects remain debated. This issue was addressed in an EEG study manipulating the context of a repeated unpredictable word (repetition of the complete sentence or repetition of the word in a new sentence context) and sentence constraint. For the manipulation of sentence constraint, unexpected words were presented either in high-constraint (eliciting a precise prediction) or low-constraint sentences (not eliciting any specific prediction). Repetition-induced reduction of N400 amplitudes and of power in the alpha/beta frequency band was larger for words repeated with their sentence context as compared with words repeated in a new low-constraint context, suggesting that implicit learning happens not only at the level of individual items but additionally improves sentence-based predictions. These processing benefits for repeated sentences did not differ between constraint conditions, suggesting that sentence-based prediction update might be proportional to the amount of unpredicted semantic information, rather than to the precision of the prediction that was violated. In addition, the consequences of high-constraint prediction violations, as reflected in a frontal positivity and increased theta band power, were reduced with repetition. Overall, our findings suggest a powerful and specific adaptation mechanism that allows the language system to quickly adapt its predictions when unexpected semantic information is processed, irrespective of sentence constraint, and to reduce potential costs of strong predictions that were violated.

On the Role of Stimulus-Response Context in Inhibitory Control in Alcohol Use Disorder

The behavioral and neural dynamics of response inhibition deficits in alcohol use disorder (AUD) are still largely unclear, despite them possibly being key to the mechanistic understanding of the disorder. Our study investigated the effect of automatic vs. controlled processing during response inhibition in participants with mild-to-moderate AUD and matched healthy controls. For this, a Simon Nogo task was combined with EEG signal decomposition, multivariate pattern analysis (MVPA), and source localization methods. The final sample comprised n = 59 (32♂) AUD participants and n = 64 (28♂) control participants. Compared with the control group, AUD participants showed overall better response inhibition performance. Furthermore, the AUD group was less influenced by the modulatory effect of automatic vs. controlled processes during response inhibition (i.e., had a smaller Simon Nogo effect). The neurophysiological data revealed that the reduced Simon Nogo effect in the AUD group was associated with reduced activation differences between congruent and incongruent Nogo trials in the inferior and middle frontal gyrus. Notably, the drinking frequency (but not the number of AUD criteria we had used to distinguish groups) predicted the extent of the Simon Nogo effect. We suggest that the counterintuitive advantage of participants with mild-to-moderate AUD over those in the control group could be explained by the allostatic model of drinking effects.