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12267 results in Cambridge Handbooks

Page 585 of 614

  • 15 - Computational Models of Attention and Cognitive Control

  • 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 422-450

  • Summary

    Computational models are like the new kids in town for the field of decision making. This field is dominated by axiomatic utility theories or simple heuristic rule models. Decision theory has a long history, starting as early as the seventeenth century with probabilistic theories of gambling by Blaise Pascal and Pierre Fermat. In an attempt to retain the basic utility framework, constraints on utility theories are being relaxed, and the formulas are becoming more deformed. Recently, many researchers have responded to the growing corpus of phenomena that challenge traditional utility models by applying wholly different approaches. This chapter provides concrete illustration of how the computational approach can account for all of the behavioral paradoxes that have contested utility theories. The extent to which the other computational models have been successful in accounting for the results is also discussed.

  • 5 - Declarative/Logic-Based Cognitive Modeling

  • 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 127-169

  • 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.

  • 4 - Dynamical Systems Approaches to 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 101-126

  • Summary

    The dynamical systems approach to cognition is the theoretical framework within which this embodied view of cognition can be formalized. This chapter reviews the core concepts of the dynamical systems approach and illustrates them through a set of experimentally accessible examples. Particular attention is given to how cognition can be understood in terms that are compatible with principles of neural function, most prominently, with the space-time continuity of neural processes. The chapter reviews efforts to form concepts based on the mathematical theory of dynamical systems into a rigorous scientific approach toward cognition that embraces the embodied and situated stance. The chapter explains how behavioral signatures of the neural field dynamics may provide evidence for the Dynamical Field Theory (DFT) account of cognition. The theoretical concept of stability, at the core of dynamical systems thinking, is the key to understanding autonomy.

  • 20 - Models of Scientific Explanation

  • 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 549-564

  • Summary

    The study of attention is central to understanding how information is processed in cognitive systems. Modern cognitive research interprets attention as the capacity to select and enhance limited aspects of currently processed information. This chapter reviews key computational models and theoretical directions pursued by researchers trying to understand the multifaceted phenomenon of attention. A broad division is drawn between theories and models addressing the mechanisms by which attention modulates specific aspects of perception (primarily visual) and those that have focused on goal-driven and task-oriented components of attention. An area of recent activity in elaborating on the computational mechanisms of goal-driven attention concerns mechanisms by which attentional biases arise or are modulated during the course of task performance. Finally, the chapter focuses on the contrast or continuum between attentional control and automaticity, an issue that becomes crystallized when examining the distinctions between, or transitions from, novice to expert cognitive task performance.

  • 19 - Cognitive Social Simulation

  • 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 530-548

  • Summary

    Learning is implicit when an individual acquires new information without intending to do so. The distinction between implicit and explicit knowledge may hinge on whether a person is conscious of the regularity with a conscious rather than unconscious mental state. Computational modeling has played a central role in deconstructing early verbal theories of the nature of what is learned in implicit learning paradigms. On the theoretical and conceptual applications of implicit learning, this chapter addresses three central issues: whether performance in implicit learning situations result in abstract knowledge; whether the data and the modeling suggest the involvement of single or multiple systems; and whether modeling is relevant to addressing the conscious versus unconscious nature of the acquired knowledge. Implicit learning has proven to be a rich domain for exploration of the differences between information processing with and without consciousness.

  • 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 665-710

  • 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.

  • 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 585 of 614