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12267 results in Cambridge Handbooks

Page 584 of 614

  • 17 - Computational Models of Psycholinguistics

  • 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 477-504

  • Summary

    Individuals, who know no logic, are able to make deductive inferences. For many years, psychologists argued that deduction depends on an unconscious system of formal rules of inference akin to those in proof-theoretic logic. The first mental model theory is for simple inferences based on quantifiers, and programs have simulated various versions of this theory, and the probabilistic theory often makes unsatisfactory predictions. The theory of mental models posits that the engine of human reasoning relies on content. The simulation of model theory concerns sentential reasoning, and it shows how an apparently unexceptional assumption leads to a striking prediction of systematic fallacies in reasoning - a case that yields crucial predictions about the nature of human deductive reasoning. The chapter concludes with an attempt to weigh up the nature of human rationality in the light of other simulation programs.

  • 16 - Computational Models of Developmental Psychology

  • 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 451-476

  • Summary

    This chapter addresses one important aspect of inductive reasoning, namely, psychological research on category-based induction, or how people use categories to make likely inferences. It describes similarity effects, typicality effects, diversity effects, and other phenomena, including background knowledge effects, setting the stage for the presentation of computational models of inductive reasoning. One consideration to keep in mind as computational models are presented is whether they have any facility for addressing not only similarity, typicality, and diversity effects, but also background knowledge effects and indeed whether they show any capacity for causal reasoning. The chapter discusses two general issues that arise in modeling inductive reasoning and also in computational modeling of other cognitive activities. The first issue is that cognitive activities do not fall neatly into pigeonholes. The second is that putting background knowledge into models is the necessary next step.

  • 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.

  • 7 - Computational Models of Episodic Memory

  • 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 189-225

  • Summary

    The chapter focuses on problems in higher-level cognition: inferring causal structure from patterns of statistical correlation, learning about categories and hidden properties of objects, and learning the meanings of words. This chapter discusses the basic principles that underlie Bayesian models of cognition and several advanced techniques for probabilistic modeling and inference coming out of recent work in computer science and statistics. The first step is to summarize the logic of Bayesian inference based on probabilistic models. A discussion is then provided of three recent innovations that make it easier to define and use probabilistic models of complex domains: graphical models, hierarchical Bayesian models, and Markov chain Monte Carlo. The central ideas behind each of these techniques is illustrated by considering a detailed cognitive modeling application, drawn from causal learning, property induction, and language modeling, respectively.

  • 22 - Models of Animal Learning and Their Relations to Human Learning

  • 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 589-611

  • 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.

  • 24 - Models of Motor 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 635-664

  • 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.

  • 8 - Computational Models of Semantic Memory

  • 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 226-266

  • 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.

  • 18 - Computational Models in Personality and Social Psychology

  • 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 505-529

  • Summary

    This chapter focuses on cognitive as opposed to sensori-motor skills and on models that create or alter symbolic knowledge representations. It deals briefly with models that learn by adjusting quantitative properties of knowledge structures. Although occasionally referring to empirical studies, the chapter is primarily a review of theoretical concepts. It proceeds on the assumption that each hypothesis contains some grain of truth to be extracted and incorporated into future models. The learning mechanism is a more finegrained unit than the model or the cognitive architecture. Cognitive descriptions of processes in the mind are functional descriptions of what this or that piece of wetware is doing, what function it carries out. This perspective points to the need to understand the relation between learning mechanisms and modes of neural plasticity.

  • 12 - Mental Logic, Mental Models, and Simulations of Human Deductive Reasoning

  • 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 339-358

  • Summary

    The term episodic memory refers to the ability to recall previously experienced events and to recognize things as having been encountered previously. Research on the neural basis of episodic memory has increasingly come to focus on three structures: The hippocampus, Perirhinal cortex and Prefrontal cortex. This chapter reviews the Complementary Learning Systems (CLS) model and how it has been applied to understanding hippocampal and neocortical contributions to episodic memory. In addition to the biologically based models, there is a rich tradition of researchers building more abstract computational models of episodic memory. The chapter describes an abstract modeling framework, the Temporal Context Model (TCM) that has proved to be very useful in understanding how to selectively retrieve memories from a particular temporal context in free recall experiments. Episodic memory modeling has a long tradition of trying to build comprehensive models that can simultaneously account for multiple recall and recognition findings.

  • 11 - Models of Inductive Reasoning

  • 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 322-338

  • Summary

    This chapter addresses one important aspect of inductive reasoning, namely, psychological research on category-based induction, or how people use categories to make likely inferences. It describes similarity effects, typicality effects, diversity effects, and other phenomena, including background knowledge effects, setting the stage for the presentation of computational models of inductive reasoning. One consideration to keep in mind as computational models are presented is whether they have any facility for addressing not only similarity, typicality, and diversity effects, but also background knowledge effects and indeed whether they show any capacity for causal reasoning. The chapter discusses two general issues that arise in modeling inductive reasoning and also in computational modeling of other cognitive activities. The first issue is that cognitive activities do not fall neatly into pigeonholes. The second is that putting background knowledge into models is the necessary next step.

  • 23 - Computational Modeling of Visual Information Processing

  • 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 612-634

  • 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.

  • 10 - Micro-Process Models of Decision Making

  • 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 302-321

  • 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.

  • 25 - AnEvaluation of Computational Modeling in Cognitive Science

  • 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 667-683

  • 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.

  • 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 187-664

  • 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.

  • 14 - Computational Models of Implicit Learning

  • 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 396-421

  • Summary

    This chapter surveys a variety of formal models of categorization, with emphasis on exemplar models. The chapter reviews exemplar models' similarity functions, learning algorithms, mechanisms for exemplar recruitment, formalizations of response probability, and response dynamics. A mutual goal of different formal models is to account for detailed quantitative data from laboratory experiments in categorization. Although a variety of representational formats have been formalized, exemplar models have been especially richly explored by researchers. The chapter discriminates numerous exemplar models to excise their functional components and to examine those components side by side. The main functional components include the computation of similarity, the learning of associations and attention, the recruitment of exemplars, the determination of response probability, and the generation of response times. This dissection revealed a variety of formalizations available for expressing any given psychological process.

  • 9 - Models of Categorization

  • 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 267-301

  • Summary

    This chapter talks about systematization of a particular approach to modeling the mind: declarative computational cognitive modeling. The goal of computational cognitive modeling and the goal of declarative computational cognitive modeling and systematization in logic-based computational cognitive modeling (LCCM) are to understand the kind of cognition distinctive of human persons by modeling this cognition in information processing systems. LCCM is made based on a generalized form of the concept of logical system as defined rather narrowly in mathematical logic. This chapter shows how the problems can be solved in LCCM in a manner that matches the human normatively incorrect and normatively correct responses returned after the relevant stimuli are presented. This chapter explains LCCM as a formal rationalization of declarative computational cognitive modeling. It also presents the attempt to build computational simulations of all, or large portions of, human cognition, on the basis of logic alone.

  • 3 - Bayesian 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 59-100

  • Summary

    The chapter focuses on problems in higher-level cognition: inferring causal structure from patterns of statistical correlation, learning about categories and hidden properties of objects, and learning the meanings of words. This chapter discusses the basic principles that underlie Bayesian models of cognition and several advanced techniques for probabilistic modeling and inference coming out of recent work in computer science and statistics. The first step is to summarize the logic of Bayesian inference based on probabilistic models. A discussion is then provided of three recent innovations that make it easier to define and use probabilistic models of complex domains: graphical models, hierarchical Bayesian models, and Markov chain Monte Carlo. The central ideas behind each of these techniques is illustrated by considering a detailed cognitive modeling application, drawn from causal learning, property induction, and language modeling, respectively.

Page 584 of 614