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11919 results in Cambridge Handbooks

Page 568 of 596

  • 21 - Cognitive Modeling for Cognitive Engineering

  • from Part III - Computational Modeling of Various Cognitive Functionalities and Domains
  • Edited by Ron Sun
  • Book:
    The Cambridge Handbook of Computational Psychology
    Published online:
    05 June 2012
    Print publication:
    28 April 2008, pp 565-588

  • Summary

    This chapter provides a comparative survey of computational models of psychological development. To understand how computational modeling can contribute to the study of psychological development, it is important to appreciate the enduring issues in developmental psychology. The most common computational techniques applied to psychological development are production systems, connectionist networks, dynamic systems, robotics, and Bayesian inference. The chapter discusses modeling in the areas of the balance scale, past tense, object permanence, artificial syntax, similarity-to-correlation shifts in category learning, discrimination-shift learning, concept and word learning, and abnormal development. Some of the models reviewed in this chapter simulated development with programmer designed parameter changes. Variations in such parameter settings were used to implement age-related changes in both connectionist and dynamic-systems models of the A-not-B error, the Cascade-Correlation (CC) model of discrimination-shift learning, all three models of the similarity-to-correlation shift, and the autism model.

  • 6 - Constraints in Cognitive Architectures

  • from Part II - Cognitive Modeling Paradigms
  • Edited by Ron Sun
  • Book:
    The Cambridge Handbook of Computational Psychology
    Published online:
    05 June 2012
    Print publication:
    28 April 2008, pp 170-186

  • Summary

    Cognitive architectures are on the one hand echoes of the original goal of creating an intelligent machine faithful to human intelligence and on the other hand attempts at theoretical unification in the field of cognitive psychology. This chapter discusses the current state of cognitive architectures to characterize four prime examples: The States, Operators, And Reasoning (SOAR) architecture, the Adaptive Control of Thought, Rational (ACT-R) theory, Executive-Process Interactive Control (EPIC) architecture, and Connectionist Learning with Adaptive Rule Induction Online (CLARION) architecture. The chapter examines a number of topics that can serve as constraints on modeling and discusses how four architectures offer solutions to help modeling in that topic area. The viewpoint of cognitive constraint is different from the perspective of how much functionality an architecture can provide, as expressed by, for example, Anderson and Lebiere.

  • 2 - Connectionist Models of Cognition

  • from Part II - Cognitive Modeling Paradigms
  • Edited by Ron Sun
  • Book:
    The Cambridge Handbook of Computational Psychology
    Published online:
    05 June 2012
    Print publication:
    28 April 2008, pp 23-58

  • Summary

    In this chapter, computer models of cognition focusing on the use of neural networks are reviewed. This chapter begins by placing connectionism in its historical context, leading up to its formalization in Rumelhart and Mc-Clelland's two-volume Parallel Distributed Processing. Three important early models illustrating some of the key properties of connectionist systems are discussed, as well as how the novel theoretical contributions of these models arose from their key computational properties. Connectionism offers an explanation of human cognition because instances of behavior in particular cognitive domains can be explained with respect to a set of general principles and the conditions of the specific domains. Connectionist theory has had a widespread influence on cognitive theorizing, and this influence was illustrated by considering connectionist contributions to our understanding of memory, cognitive development, acquired cognitive impairments, and developmental deficit.

  • 26 - Putting the Pieces Together Again

  • from Part IV - Concluding Remarks
  • Edited by Ron Sun
  • Book:
    The Cambridge Handbook of Computational Psychology
    Published online:
    05 June 2012
    Print publication:
    28 April 2008, pp 684-710

  • 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.

Page 568 of 596