Everyday we vicariously experience a range of states that we observe in other people: we may “feel” embarrassed when witnessing another making a social faux pas, or we may feel sadness when we see a loved one upset. In some cases this process appears to be implicit. For instance, observing pain in others may activate pain-related neural processes but without generating an overt feeling of pain. In other cases, people report a more literal, conscious sharing of affective or somatic states and this has sometimes been described as representing an extreme form of empathy. By contrast, there appear to be some people who are limited in their ability to vicariously experience the states of others. This may be the case in several psychiatric, neurodevelopmental, and personality disorders where deficits in interpersonal understanding are observed, such as schizophrenia, autism, and psychopathy. In recent decades, neuroscientists have paid significant attention to the understanding of the “social brain,” and the way in which neural processes govern our understanding of other people. In this Research Topic, we wish to contribute towards this understanding and ask for the submission of manuscripts focusing broadly on the neural underpinnings of vicarious experience. This may include theoretical discussion, case studies, and empirical investigation using behavioural techniques, electrophysiology, brain stimulation, and neuroimaging in both healthy and clinical populations. Of specific interest will be the neural correlates of individual differences in traits such as empathy, how we distinguish between ourselves and other people, and the sensorimotor resonant mechanisms that may allow us to put ourselves in another’s shoes.
Release on 2019-02-05 | by Felix Putze,Christian Mühl,Fabien Lotte,Stephen Fairclough,Christian Herff
Author: Felix Putze,Christian Mühl,Fabien Lotte,Stephen Fairclough,Christian Herff
Pubpsher: Frontiers Media SA
Executive cognitive functions like working memory determine the success or failure of a wide variety of different cognitive tasks, such as problem solving, navigation, or planning. Estimation of constructs like working memory load or memory capacity from neurophysiological or psychophysiological signals would enable adaptive systems to respond to cognitive states experienced by an operator and trigger responses designed to support task performance (e.g. by simplifying the exercises of a tutor system when the subject is overloaded, or by shutting down distractions from the mobile phone). The determination of cognitive states like working memory load is also useful for automated testing/assessment or for usability evaluation. While there exists a large body of research work on neural and physiological correlates of cognitive functions like working memory activity, fewer publications deal witt the application of this research with respect to single-trial detection and real-time estimation of cognitive functions in complex, realistic scenarios. Single-trial classifiers based on brain activity measurements such as electroencephalography, functional near-infrared spectroscopy, physiological signals or eye tracking have the potential to classify affective or cognitive states based upon short segments of data. For this purpose, signal processing and machine learning techniques need to be developed and transferred to real-world user interfaces. The goal of this Frontiers Research Topic was to advance the State-of-the-Art in signal-based modeling of cognitive processes. We were especially interested in research towards more complex and realistic study designs, for example collecting data in the wild or investigating the interaction between different cognitive processes or signal modalities. Bringing together many contributions in one format allowed us to look at the state of convergence or diversity regarding concepts, methods, and paradigms.