Spatiotemporal Phase Synchronization in Adaptive Reconfiguration from Action Observation Network to Mentalizing Network for Understanding Other's Action Intention

Three-stage Dynamic Brain-cognitive Model of Understanding Action Intention Displayed by Human Body Movements

The ability to comprehend the intention conveyed through human body movements is crucial for effective interpersonal interactions. If people can't understand the intention behind other individuals' isolated or interactive actions, their actions will become meaningless. Psychologists have investigated the cognitive processes and neural representations involved in understanding action intention, yet a cohesive theoretical explanation remains elusive. Hence, we mainly review existing literature related to neural correlates of action intention, and primarily propose a putative Three-stage Dynamic Brain-cognitive Model of understanding action intention, which involves body perception, action identification and intention understanding. Specifically, at the first stage, body parts/shapes are processed by those brain regions such as extrastriate and fusiform body areas; During the second stage, differentiating observed actions relies on configuring relationships between body parts, facilitated by the activation of the Mirror Neuron System; The last stage involves identifying various intention categories, utilizing the Mentalizing System for recruitment, and different activation patterns concerning the nature of the intentions participants dealing with. Finally, we delves into the clinical practice, like intervention training based on a theoretical model for individuals with autism spectrum disorders who encounter difficulties in interpersonal communication.

Attachment, Mentalizing and Trauma: Then (1992) and Now (2022)

This article reviews the current status of research on the relationship between attachment and trauma in developmental psychopathology. Beginning with a review of the major issues and the state-of-the-art in relation to current thinking in the field of attachment about the impact of trauma and the inter-generational transmission of trauma, the review then considers recent neurobiological work on mentalizing and trauma and suggests areas of new development and implications for clinical practice.

Electrocorticographic evidence of a common neurocognitive sequence for mentalizing about the self and others

Neuroimaging studies of mentalizing (i.e., theory of mind) consistently implicate the default mode network (DMN). Nevertheless, the social cognitive functions of individual DMN regions remain unclear, perhaps due to limited spatiotemporal resolution in neuroimaging. Here we use electrocorticography (ECoG) to directly record neuronal population activity while 16 human participants judge the psychological traits of themselves and others. Self- and other-mentalizing recruit near-identical cortical sites in a common spatiotemporal sequence. Activations begin in the visual cortex, followed by temporoparietal DMN regions, then finally in medial prefrontal regions. Moreover, regions with later activations exhibit stronger functional specificity for mentalizing, stronger associations with behavioral responses, and stronger self/other differentiation. Specifically, other-mentalizing evokes slower and longer activations than self-mentalizing across successive DMN regions, implying lengthier processing at higher levels of representation. Our results suggest a common neurocognitive pathway for self- and other-mentalizing that follows a complex spatiotemporal gradient of functional specialization across DMN and beyond.

Multi-class classification of action intention understanding brain signals based on thresholding graph metric

BACKGROUND: Classification of action intention understanding is extremely important for human computer interaction. Many studies on the action intention understanding classification mainly focus on binary classification, while the classification accuracy is often unsatisfactory, not to mention multi-class classification. METHOD: To complete the multi-class classification task of action intention understanding brain signals effectively, we propose a novel feature extraction procedure based on thresholding graph metric. RESULTS: Both the alpha frequency band and full-band obtained considerable classification accuracies. Compared with other methods, the novel method has better classification accuracy. CONCLUSIONS: Brain activity of action intention understanding is closely related to the alpha band. The new feature extraction procedure is an effective method for the multi-class classification of action intention understanding brain signals.

Action Intention Understanding EEG Signal Classification Based on Improved Discriminative Spatial Patterns

Objective

Action intention understanding EEG signal classification is indispensable for investigating human-computer interactions and intention understanding mechanisms. Numerous investigations on classification tasks extract classification features by using graph theory metrics; however, the classification results are usually not good.

Method

To effectively implement the task of action intention understanding EEG signal classification, we proposed a new feature extraction method by improving discriminative spatial patterns.

Results

The whole frequency band and fusion band achieved satisfactory classification accuracies. Compared with other authors' methods for action intention understanding EEG signal classification, the new method performs more satisfactorily in some aspects.

Conclusions

The new feature extraction method not only effectively avoids complex values when solving the generalized eigenvalue problem but also perfectly realizes appreciable classification accuracies. Fusing the classification features of different frequency bands is a useful strategy for the classification task.

Weighted Brain Network Metrics for Decoding Action Intention Understanding Based on EEG

Background: Understanding the action intentions of others is important for social and human-robot interactions. Recently, many state-of-the-art approaches have been proposed for decoding action intention understanding. Although these methods have some advantages, it is still necessary to design other tools that can more efficiently classify the action intention understanding signals.

New Method: Based on EEG, we first applied phase lag index (PLI) and weighted phase lag index (WPLI) to construct functional connectivity matrices in five frequency bands and 63 micro-time windows, then calculated nine graph metrics from these matrices and subsequently used the network metrics as features to classify different brain signals related to action intention understanding.

Results: Compared with the single methods (PLI or WPLI), the combination method (PLI+WPLI) demonstrates some overwhelming victories. Most of the average classification accuracies exceed 70%, and some of them approach 80%. In statistical tests of brain network, many significantly different edges appear in the frontal, occipital, parietal, and temporal regions.

Conclusions: Weighted brain networks can effectively retain data information. The integrated method proposed in this study is extremely effective for investigating action intention understanding. Both the mirror neuron and mentalizing systems participate as collaborators in the process of action intention understanding.

Neural Activity and Decoding of Action Observation Using Combined EEG and fNIRS Measurement

In a social world, observing the actions of others is fundamental to understanding what they are doing, as well as their intentions and feelings. Studies of the neural basis and decoding of action observation are important for understanding action-related processes and have implications for cognitive, social neuroscience, and human-machine interaction (HMI). In the current study, we first investigated temporal-spatial dynamics during action observation using a combined 64-channel electroencephalography (EEG) and 48-channel functional near-infrared spectroscopy (fNIRS) system. We measured brain activation while 16 healthy participants observed three action tasks: (1) grasping a cup with the intention of drinking; (2) grasping a cup with the intention of moving it; and (3) touching a cup with an unclear intention. The EEG and fNIRS source analysis results revealed the dynamic involvement of both the mirror neuron system (MNS) and the theory of mind (ToM)/mentalizing network during action observation. The source analysis results suggested that the extent to which these two systems were engaged was determined by the clarity of the intention of the observed action. Based on the difference in neural activity observed among different action-observation tasks in the first experiment, we conducted a second experiment to classify the neural processes underlying action observation using a feature classification method. We constructed complex brain networks based on the EEG and fNIRS data. Fusing features from both EEG and fNIRS complex brain networks resulted in a classification accuracy of 72.7% for the three action observation tasks. This study provides a theoretical and empirical basis for elucidating the neural mechanisms of action observation and intention understanding, and a feasible method for decoding the underlying neural processes.