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That younger individuals perceive the world as moving slower than adults is a familiar phenomenon. Yet, it remains an open question why that is. Using event segmentation theory, electroencephalogram (EEG) beamforming and nonlinear causal relationship estimation using artificial neural network methods, we studied neural activity while adolescent and adult participants segmented a movie. We show when participants were instructed to segment a movie into meaningful units, adolescents partitioned incoming information into fewer encapsulated segments or episodes of longer duration than adults. Importantly, directed communication between medial frontal and lower-level perceptual areas and between occipito-temporal regions in specific neural oscillation spectrums explained behavioral differences between groups. Overall, the study reveals that a different organization of directed communication between brain regions and inefficient transmission of information between brain regions are key to understand why younger people perceive the world as moving slow. An EEG study using event segmentation theory found adolescents segment movies into fewer, longer episodes than adults. Differences in neural communication patterns between brain regions explain why younger people perceive the world as moving slower. [ABSTRACT FROM AUTHOR]

• Neural oscillatory dynamics of metacontrol is examined. • Especially theta band activity is relevant for metacontrol. • Alpha and beta band activity show more general modulations in resetting the neural system. • Directed functional connectivity in prefrontal-parietal networks is modulated by metacontrol. Understanding the neural mechanisms underlying metacontrol and conflict regulation is crucial for insights into cognitive flexibility and persistence. This study employed electroencephalography (EEG), EEG-beamforming and directed connectivity analyses to explore how varying metacontrol states influence conflict regulation at a neurophysiological level. Metacontrol states were manipulated by altering the frequency of congruent and incongruent trials across experimental blocks in a modified flanker task, and both behavioral and electrophysiological measures were analyzed. Behavioral data confirmed the experimental manipulation's efficacy, showing an increase in persistence bias and a reduction in flexibility bias during increased conflict regulation. Electrophysiologically, theta band activity paralleled the behavioral data, suggesting that theta oscillations reflect the mismatch between expected metacontrol bias and actual task demands. Alpha and beta band dynamics differed across experimental blocks, though these changes did not directly mirror behavioral effects. Post-response alpha and beta activity were more pronounced in persistence-biased states, indicating a neural reset mechanism preparing for future cognitive demands. By using a novel artificial neural networks method, directed connectivity analyses revealed enhanced inter-regional communication during persistence states, suggesting stronger top-down control and sensorimotor integration. Overall, theta band activity was closely tied to metacontrol processes, while alpha and beta bands played a role in resetting the neural system for upcoming tasks. These findings provide a deeper understanding of the neural substrates involved in metacontrol and conflict monitoring, emphasizing the distinct roles of different frequency bands in these cognitive processes. [ABSTRACT FROM AUTHOR]

Quantifying causal (effective) interactions between different brain regions are very important in neuroscience research. Many conventional methods estimate effective connectivity based on linear models. However, using linear connectivity models may oversimplify the functions and dynamics of the brain. In this paper, we propose a causal relationship estimator called nonlinear Causal Relationship Estimation by Artificial Neural Network (nCREANN) that identifies both linear and nonlinear components of effective connectivity in the brain. Furthermore, it can distinguish between these two types of connectivity components by calculating the linear and nonlinear parts of the network input-output mapping. The nCREANN performance has been verified using synthesized data and then it has been applied on EEG data collected during rest in children with autism spectrum disorder (ASD) and typically developing (TD) children. The results show that overall linear connectivity in TD subjects is higher, while the nonlinear connectivity component is more dominant in ASDs. We suggest that our findings may represent different underlying neural activation dynamics in ASD and TD subjects. The results of nCREANN may provide new insight into the connectivity between the interactive brain regions. [ABSTRACT FROM AUTHOR]