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  1. Home
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  3. The Cambridge Handbook of Computational Psychology
  • Cited by 48
Publisher:
Cambridge University Press
Online publication date:
June 2012
Print publication year:
2008
Online ISBN:
9780511816772
DOI:
https://doi.org/10.1017/CBO9780511816772
Subjects:
Life Sciences, Cognitive Psychology, Psychology, Psychology: General Interest, Cognition
Series:
Cambridge Handbooks in Psychology
Collection:
Cambridge Handbooks of Psychology
Information

Book description

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.

Reviews

"[...] This edited volume by Sun (Rensselaer Polytechnic Institute) comprises two sections:[...] The first section will attract broader interest, especially from students, because of its juxtaposition of distinct approaches including connectionist, Bayesian, and logical modeling. The second section covers a range of topics, from memory and learning to decision making and cognitive control. [...] Given that the application chapters are largely independent of the methodological chapters, a dedicated instructor could cover more extensive ground by selecting primary papers on a desired topic. However, researchers who use computational approaches, or who want to become better consumers of computational psychology literature, may find this to be a valuable compilation of major ideas in this area. Recommended.
--S.A. Huettel, Duke University CHOICE

"--With the publication of The Cambridge Handbook of Computational Psychology, the newly emerging, interdisciplinary field of computational cognitive modeling has come of age...a cutting-edge overview of classic and currentwork in computational psychology. This handbook stakes out this important and promising area of cognitive science...a definitive reference source for therapidly growing, increasingly important, and strongly interdisciplinary field ofcomputational cognitive modeling...The Cambridge Handbook of Computational Psychology represents a milestone, marking a number of important contributions to the larger field of cognitive science."
--Howard T. Everson, PsycCRITIQUES [May 20, 2009, Vol. 54, Release 20, Article 5]

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Contents

Page 2 of 2

Contents
  • 22 - Models of Animal Learning and Their Relations to Human Learning
    pp 589-611
  • View abstract

    Summary

    This chapter outlines the historical origins and the state of art of computational models of psycholinguistic processes. It considers interrelationships between the different theoretical traditions in reaction to the Chomskyan revolution. The chapter focuses attention on topics that have the widest general theoretical implications, both for fields of computational cognitive modeling and for the project of cognitive science more broadly. The chapter outlines and contrasts symbolic, connectionist, and probabilistic approaches to the computational modeling of psycholinguistic phenomena. The chapter considers word segmentation and recognition, and single word reading. The chapter focuses primarily on parsing, relating connectionist and probabilistic models to the symbolic models of grammar and processing associated with Chomsky's program. The chapter reviews formal and computational models of language learning and re-evaluates, in the light of current computational work, Chomsky's early theoretical arguments for a strong nativist view of the computational mechanisms involved.
  • 23 - Computational Modeling of Visual Information Processing
    pp 612-634
  • View abstract

    Summary

    The most frequently used computational models in social psychology are probably various kinds of connectionist models, such as constraint satisfaction networks, feedforward pattern associators with delta-rule learning, and multilayer recurrent networks with learning. The chapter begins with work on causal learning, causal reasoning, and impression formation. A large number of central phenomena in social psychology can be captured by a fairly simple feedback or recurrent network with learning. Important findings on causal learning, causal reasoning, individual and group impression formation, and attitude change can all be captured within the same basic architecture. This suggests that we might be close to being able to provide an integrated theory or account of a wide range of social psychological phenomena. It also suggests that underlying the apparent high degree of complexity of social and personality phenomena may be more fundamental simplicity.
  • 24 - Models of Motor Control
    pp 635-664
  • View abstract

    Summary

    Social simulation focuses on processes to provide some forms of historical perspectives in explaining social phenomena. This chapter presents three representative examples of cognitive social simulation. It looks into a few representative examples of the kind of social simulation that takes cognition of individual agents into consideration seriously. Game-theoretical interaction is an excellent domain for researching multiagent interactions. The chapter discusses types, issues, and directions of cognitive social simulation and looks into some possible dimensions for categorizing cognitive social simulation. A variety of modeling works has been done on group and/or organizational dynamics on the basis of cognitive models. By combining cognitive models and social simulation models, cognitive social simulation is poised to address issues of the interaction of cognition and sociality, in addition to advancing the state of the art in understanding cognitive and social processes.
  • Part IV - Concluding Remarks
    pp 665-710
  • View abstract

    Summary

    In addition to providing a concise review of computational models of explanation. This chapter describes a new neural network model that shows how explanations can be performed by multimodal distributed representations. A more psychologically elegant way of performing inference to the best explanation, the model ECHO, is described in the section on neural networks. This chapter provides an over view about Bayesian networks providing an excellent tool for computational and normative philosophical applications. All of the computational models described in this chapter are mechanistic, although they differ in what they take to be the parts and interactions that are central to explaining human thinking; for the neural network approaches, the computational mechanisms are also biological ones. This chapter provides a review about four major computational approaches to understanding scientific explanations: deductive, schematic, probabilistic, and neural network.
  • 25 - AnEvaluation of Computational Modeling in Cognitive Science
    pp 667-683
  • View abstract

    Summary

    Cognitive engineering is the application of cognitive science theories to human factors practice. Attempts to apply computational and mathematical modeling techniques to human factor issues have a long and detailed history. This chapter reviews the seminal work of Card, Moran, and Newell from the modern perspective. It discusses the issues and applications of cognitive engineering, first for the broad category of complex systems and then for the classic area of human-computer interaction, with a focus on human interaction with quantitative information, that is, visual analytics. Not only is the control of integrated cognitive systems a challenging basic research question, the importance of understanding the control of integrated cognitive systems for cognitive engineering purposes suggests that research on control issues should become a high priority among basic researchers as well as those agencies that fund basic research.
  • 26 - Putting the Pieces Together Again
    pp 684-710
  • View abstract

    Summary

    This chapter reviews a contingency learning against the background of recent formal models of animal learning. It reviews a very substantial amount of research including not only human causal and predictive learning but also category learning and multiple-cue probability learning. The development of theoretical models of predictive learning has been stimulated to an enormous extent by demonstrations that cues compete with each other to gain control over behavior (so-called cue interaction effects). In causal learning scenarios, the cue and outcome are provided, via the instructions, with particular causal roles. In most cases, then, the cues are not only potentially predictive of the outcome but also cause it. Despite the challenging nature of the evidence against an associative perspective as a unique account of human predictive learning, there is also evidence that the influence of causal knowledge or rule learning is not necessarily pervasive.

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