![]() Finally, we will have a panel discussion to suggest how models could be challenged by experimental data, and provide new explanatory mechanisms. The symposium has four talks, by Edith Kaan (associate professor, specializing in psycholinguistics of bilingualism), Yung Han Khoe (PhD student, working on models of bilingual sentence production), Lin Chen (research associate with an expertise in reading processes), and a joint talk by Irene Winther (PhD student working on bilingual sentence processing) and Yevgen Matusevych (research associate in computational cognitive science of language). Our symposium aims to bring together researchers from different labs and with different research traditions, working on the intersection of models and experiments in bilingual sentence processing. Moreover, better understanding of bilingual processing will give more insights into more general mechanisms such as cognitive control processes involved while switching languages (Luk et al., 2012). This lack of computational specifications can hamper further progress in bilingualism research. There are currently only very few sentence-level computational models of second-language (L2) or bilingual processing (Frank, 2021). The role of reflexive collaborative inquiry and active public participation in emergent research is considered as a way to offer socially responsible scientific tools to the cognitive science community.Īlthough sentence comprehension and production are increasingly often studied by combining computational modeling and human experiments, this approach remains mostly restricted to studies of monolingual or first-language (L1) processing. This contributed symposia will stimulate debate and questions arising across the intersections of art, neurology, cognitive science and public participation to leverage understanding of reality and the self through interdisciplinary considerations of cognitive difference. ![]() The exhibition, public events and recent inbuilt psychological study, embrace the subjective nature of perception and highlight a role for augmented reality art experiences as cognitive science experiments in public settings. This exhibit draws on Visual Snow experienced as experienced by the artist and informed by interdisciplinary research, including cognitive science. ![]() A commonly experienced visual symptom is described as the 'persistent effect of television “snow”', and was first described in the literature in 1995.ĭistorted Constellations is an immersive, sensory, labyrinthine environment and playful experience of an augmented reality interpretation of artist, Nwando Ebizie's unique perception of the world. Whilst Visual Snow produces a collection of different symptoms, it is clinically recognised. Visual Snow is a neurological condition that is experienced as an ‘augmented’ reality of auras, glowing lines, depression, anxiety and depersonalisation. The tutorial will provide a working knowledge of the state of the art methods for encoding and decoding, a thorough understanding of the literature, and a better understanding of the benefits and limitations of encoding/decoding with deep learning. In this tutorial, we plan to discuss different kinds of stimulus representations, and popular encoding and decoding architectures in detail. Recently, inspired by the effectiveness of deep learning models for natural language processing and computer vision, such models have been applied for neuroscience as well. Both the brain encoding and decoding problems have been studied in detail in the past two decades and the foremost attraction of studying these solutions is that they serve as additional tools for basic research in cognitive science and cognitive neuroscience. The brain decoding problem is the inverse problem of reconstructing the stimuli given the fMRI brain representation. The brain encoding problem aims to automatically generate fMRI brain representations given a stimulus. How does the brain represent different modes of information? Can we design a system that can automatically understand what the user is thinking? We can make progress towards answering such questions by studying brain recordings from devices such as functional magnetic resonance imaging (fMRI).
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