Brain-Computer Interface

Brain-computer interfacing (BCI) with the use of advanced artificial intelligence identification is a rapidly growing new technology that allows a silently commanding brain to manipulate devices ranging from smartphones to advanced articulated robotic arms when physical control is not possible. BCI can be viewed as a collaboration between the brain and a device via the direct passage of electrical signals from neurons to an external system. The book provides a comprehensive summary of conventional and novel methods for processing brain signals. The chapters cover a range of topics including noninvasive and invasive signal acquisition, signal processing methods, deep learning approaches, and implementation of BCI in experimental problems

Modeling the distributional dynamics of attention and semantic interference in word production

In recent years, it has become clear that attention plays an important role in spoken word production. Some of this evidence comes from distributional analyses of reaction time (RT) in regular picture naming and picture-word interference. Yet we lack a mechanistic account of how the properties of RT distributions come to reflect attentional processes and how these processes may in turn modulate the amount of conflict between lexical representations. Here, we present a computational account according to which attentional lapses allow for existing conflict to build up unsupervised on a subset of trials, thus modulating the shape of the resulting RT distribution. Our process model resolves discrepancies between outcomes of previous studies on semantic interference. Moreover, the model's predictions were confirmed in a new experiment where participants' motivation to remain attentive determined the size and distributional locus of semantic interference in picture naming. We conclude that process modeling of RT distributions importantly improves our understanding of the interplay between attention and conflict in word production. Our model thus provides a framework for interpreting distributional analyses of RT data in picture naming tasks

Knowledge Modelling and Learning through Cognitive Networks

One of the most promising developments in modelling knowledge is cognitive network science, which aims to investigate cognitive phenomena driven by the networked, associative organization of knowledge. For example, investigating the structure of semantic memory via semantic networks has illuminated how memory recall patterns influence phenomena such as creativity, memory search, learning, and more generally, knowledge acquisition, exploration, and exploitation. In parallel, neural network models for artificial intelligence (AI) are also becoming more widespread as inferential models for understanding which features drive language-related phenomena such as meaning reconstruction, stance detection, and emotional profiling. Whereas cognitive networks map explicitly which entities engage in associative relationships, neural networks perform an implicit mapping of correlations in cognitive data as weights, obtained after training over labelled data and whose interpretation is not immediately evident to the experimenter. This book aims to bring together quantitative, innovative research that focuses on modelling knowledge through cognitive and neural networks to gain insight into mechanisms driving cognitive processes related to knowledge structuring, exploration, and learning. The book comprises a variety of publication types, including reviews and theoretical papers, empirical research, computational modelling, and big data analysis. All papers here share a commonality: they demonstrate how the application of network science and AI can extend and broaden cognitive science in ways that traditional approaches cannot