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49 results

Page 1 of 3

Intercultural Communication and Identity

Intercultural Communication and Identity
  • Ron Darvin, Tongle Sun
  • Published online:
    17 January 2024
    Print publication:
    15 February 2024
  • This Element asserts how identity as a construct enables a critical awareness of how speakers position themselves and are positioned by others in intercultural encounters. It discusses how identity vis-à-vis culture has been theorized through social psychological, poststructuralist, and critical lenses, and how identity is discursively constructed and mediated. Rejecting essentialist notions of language and culture, this Element demonstrates how inscriptions of identity such as race, ethnicity, nationality, and class can be used to critically examine the dynamics of situated intercultural encounters and to understand how such interactions can index competing and colluding ideologies. By examining identity research from different parts of the world, it casts a light on how identities are performed in diverse intercultural contexts and discusses research methodologies that have been employed to examine identity in intercultural communication.

  • 1 - An Overview of Computational Cognitive Sciences

  • from Part I - Introduction
  • Edited by Ron Sun, Rensselaer Polytechnic Institute, New York
  • Book:
    The Cambridge Handbook of Computational Cognitive Sciences
    Published online:
    21 April 2023
    Print publication:
    11 May 2023, pp 3-26

  • Summary

    This chapter provides an introduction and an overview of computational cognitive sciences. Computational cognitive sciences explore the essence of cognition and various cognitive functionalities through developing mechanistic, process-based understanding by specifying corresponding computational models. These models impute computational processes onto cognitive functions and thereby produce runnable programs. Detailed simulations and other operations can then be conducted. Understanding the human mind strictly from observations of, and experiments with, human behavior is ultimately untenable. Computational modeling is therefore both useful and necessary. Computational cognitive models are theoretically important because they represent detailed cognitive theories in a unique, indispensable way. Computational cognitive modeling has thus far deepened the understanding of the processes and the mechanisms of the mind in a variety of ways.

  • 32 - Cognitive Modeling in Social Simulation

  • from Part IV - Computational Modeling in Various Cognitive Fields
  • Edited by Ron Sun, Rensselaer Polytechnic Institute, New York
  • Book:
    The Cambridge Handbook of Computational Cognitive Sciences
    Published online:
    21 April 2023
    Print publication:
    11 May 2023, pp 1064-1087

  • Summary

    This chapter discusses cognitive social simulation, which lies at the intersection of cognitive modeling and social simulation – two forms of computational modeling. Cognitive modeling focuses on producing precise computational models of individual mental processes, while social simulation centers on models of social processes (such as interaction of individuals or collective decision making). By combining cognitive and social models, cognitive social simulation is poised to address issues concerning both individuals and society. Detailed simulation enables precise analysis of possible scenarios and outcomes (social or individual). A number of examples of cognitive social simulation are sketched in this chapter. Issues involved are discussed. Some promising directions are outlined.

The Cambridge Handbook of Computational Cognitive Sciences
  • 2nd edition
  • Edited by Ron Sun
  • Published online:
    21 April 2023
    Print publication:
    11 May 2023
  • The Cambridge Handbook of Computational Cognitive Sciences is a comprehensive reference for this rapidly developing and highly interdisciplinary field. Written with both newcomers and experts in mind, it provides an accessible introduction of paradigms, methodologies, approaches, and models, with ample detail and illustrated by examples. It should appeal to researchers and students working within the computational cognitive sciences, as well as those working in adjacent fields including philosophy, psychology, linguistics, anthropology, education, neuroscience, artificial intelligence, computer science, and more.

  • 5 - Connectionism and neural networks

    • By Ron Sun, Rensselaer Polytechnic Institute
  • Edited by Keith Frankish, The Open University, Milton Keynes, William M. Ramsey, University of Nevada, Las Vegas
  • Book:
    The Cambridge Handbook of Artificial Intelligence
    Published online:
    05 July 2014
    Print publication:
    12 June 2014, pp 108-127

  • Summary

    This chapter reviews briefly the history of connectionist models, identifying major ideas and major areas of applications. It gives a brief review of major paradigms of learning in neural networks. The chapter addresses the issue of symbolic processing in connectionist models. It looks in particular at the following types of learning: supervised learning, unsupervised learning, and reinforcement learning. The connectionist revolution has spurred vigorous theoretical debates about the nature of cognition and the various approaches toward understanding. The chapter discusses the hybrid connectionist models, which incorporate both connectionist and symbolic processing methods. In contrast to connectionist implementationism, hybrid connectionist models may be considered a synthesis of connectionist models and traditional symbolic models. Hybrid models, such as CLARION, have been used to address a wide variety of issues in cognitive science and artificial intelligence, including human learning, reasoning, problem solving, creativity, motivational dynamics, metacognitive processes, and above all, human consciousness.

The Cambridge Handbook of Computational Psychology
  • Edited by Ron Sun
  • Published online:
    05 June 2012
    Print publication:
    28 April 2008
  • This book is a definitive reference source for the growing, increasingly more important, and interdisciplinary field of computational cognitive modeling, that is, computational psychology. It combines breadth of coverage with definitive statements by leading scientists in this field. Research in computational cognitive modeling explores the essence of cognition and various cognitive functionalities through developing detailed, process-based understanding by specifying computational mechanisms, structures, and processes. Given the complexity of the human mind and its manifestation in behavioral flexibility, process-based computational models may be necessary to explicate and elucidate the intricate details of the mind. The key to understanding cognitive processes is often in fine details. Computational models provide algorithmic specificity: detailed, exactly specified, and carefully thought-out steps, arranged in precise yet flexible sequences. These models provide both conceptual clarity and precision at the same time. This book substantiates this approach through overviews and many examples.

Cognition and Multi-Agent Interaction
  • From Cognitive Modeling to Social Simulation
  • Edited by Ron Sun
  • Published online:
    15 December 2009
    Print publication:
    05 December 2005
  • This book explores the intersection between cognitive sciences and social sciences. In particular, it explores the intersection between individual cognitive modeling and modeling of multi-agent interaction (social stimulation). The two contributing fields - individual cognitive modeling (especially cognitive architectures) and modeling of multi-agent interaction (including social simulation and, to some extent, multi-agent systems) - have seen phenomenal growth in recent years. However, the interaction of these two fields has not been sufficiently developed. We believe that the interaction of the two may be more significant than either alone. They bring with them enormous intellectual capitals. These intellectual capitals can be profitably leveraged in creating true synergy between the two fields, leading to more in-depth studies and better understanding of both individual cognition and sociocultural processes. It is possible that an integrative field of study in cognitive and social sciences is emerging and we are laying the foundation for it.

  • 1 - Introduction to Computational Cognitive Modeling

  • from Part I - Introduction
  • Edited by Ron Sun
  • Book:
    The Cambridge Handbook of Computational Psychology
    Published online:
    05 June 2012
    Print publication:
    28 April 2008, pp 3-20

  • Summary

    This chapter provides an overview of the book on Introduction to Computational Cognitive Modeling. The first part of the book provides a general introduction to the field of computational cognitive modeling. The second part, Cognitive Modeling Paradigms, introduces the reader to broadly influential approaches in cognitive modeling. The third part, Computational Modeling of Various Cognitive Functionalities and Domains, describes a range of computational modeling efforts that researchers in this field have undertaken regarding major cognitive functionalities and domains. This part surveys and explains computational modeling research, in terms of detailed computational mechanisms and processes, on memory, concepts, learning, reasoning, decision making, skills, vision, motor control, language, development, scientific explanation, social interaction, and so on. The final part, Concluding Remarks, explores a range of issues associated with computational cognitive modeling and cognitive architectures, and provides some perspectives, evaluations, and assessments.

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