Cognitive Modeling

Overview

Computational modeling plays a central role in cognitive science. This book provides a comprehensive introduction to computational models of human cognition. It covers major approaches and architectures, both neural network and symbolic; major theoretical issues; and specific computational models of a variety of cognitive processes, ranging from low-level (e.g., attention and memory) to higher-level (e.g., language and reasoning). The articles included in the book provide original descriptions of developments in the field. The emphasis is on implemented computational models rather than on mathematical or nonformal approaches, and on modeling empirical data from human subjects.

Table of Contents

  1. Preface
  2. Sources
  3. I. Architectures and Approaches
  4. 1. The Role of Knowledge in Discourse Comprehension: A Construction-Integration Model

    Walter A. Kintssch

  5. 2. Act: A Simple Theory of Complex Cognition

    John R. Anderson

  6. 3. A Preliminary Analysis of the Soar Architecture as a Basis for General Intelligence

    Paul S. Rosenbloom, John E. Laird, Allen Newell, and Robert McCarl

  7. 4. Adaptive Executive Control: Flexible Multiple-Task Performance without Pervasive Immutable Response-Selection Bottlenecks

    David E. Meyer, David E. Kieras, Erick Lauber, Eric H. Schumacher, Jennifer Glass, Eileen Zurbriggen, Leon Gmeidel, and Dana Apfelblat

  8. 5. A Capacity Theory of Comprehension: Individual Differences in Working Memory

    Marcel A. Just and Patricia A. Carpenter

  9. 6. How Neural Networks Learn from Experience

    Geoffrey E. Hinton

  10. 7. The Hopfield Model

    John Hertz, Anders Krogh, and Richard G. Palmer

  11. 8. Learning Representations by back-Propagating Errors

    David E. Rumelhart, Geoffrey E. Hinton, and Ronald J. Williams

  12. 9. Forward Models: Supervised Learning with a Distal Teacher

    Michael I. Jordan and David E. Rumelhart

  13. 10. Finding Structure in Time

    Jeffrey L. Elman

  14. 11. A self-Organizeing Neural Network for Supervised Learning, Recognition, and Prediction

    Gail A. Carpenter and Stephen Grossberg

  15. 12. Optimality: From Neural Networks to Universal Grammar

    Alan Prince and Paul Smolensky

  16. Part II
  17. 13. Dynamic Binding in a Neural Network for Shape Recognition

    John E. Hummel and Irving Biederman

  18. 14. Dynamic Binding in a Neural Network for Shape Recognition

    John E. Hummel and Irving Biederman

  19. 15. The End of the LIne for a Brain-Damaged Model of Unilateral Neglect

    Michael C. Mozer, Peter W. Halligan, and John C. Marshall

  20. 16. An Integrated Theory of List Memory

    John R. Anderson, Dan Bothell, Christian Lebiere, and Michael Matessa

  21. 17. Why There Are Complementary Learning Systems in Hippocampus and Neocortex: Insights from the Successes and Failures of COnnectionist Models of Learning and Memory

    James L. McClelland, Bruce L. McNaughton, and Randall C. O'Reilly

  22. 18. ALCOVE: An Exemplar-Based Connectionist Model of Category Learning

    John K. Kruschke

  23. 19. How People Learn to Skip Steps

    Stephen B. Blessing and John R. Anderson

  24. 20. Acquisition of Children's Addition Strategies: A Model of Impasse-Free, Knowledge-Level Learning

    Randolph M. Jones and Kurt Van Lehn

  25. 21. Learning from a Connectionist Model of the Acquisition of the English Past Tense

    Kim Plunkett and Virginia A. Marchman

  26. 22. Acquiring the Mapping from Meaning to Sounds

    Garrison W. Cottrell and Kim plunkett

  27. 23. Understanding Normal and Impaired Word Reading: Computational Principles in Quasi-Regular Domains

    David C. Plaut, James L. McClelland, Mark S. Seidenberg, and Karalyn Patterson

  28. 24. Language Production and Serial Order: A Functional Analysis and a Model

    Gary S. Dell, Lisa K. Burger, and William R. Svec

  29. 25. Interference in Short-Term Memory: The Magical Number Two (or Three) in Sentence Processing

    Richard L. Lewis

  30. 26. Similarity, Interactive Activation, and Mapping: An Overview

    Robert L. Goldstone and Douglas L. Medin

  31. 27. Analogical Mapping by Constraint Satisfaction

    Keith J. Holyoak and Paul Thagard

  32. 28. MAC/FAC: A Model of Similarity-based Retrieval

    Kenneth D. Forbus, Dedre Gentner, and Keith Law

  33. 29. Distributed Representations of Structure: A Theory of Analogical Access and Mapping

    John E. Hummel and Keith J. Holyoak

  34. 30. Case-Based Learning: Predictive Features in Indexing

    Colleen M. Seifert, Kristian J. Hammond, Hollyn M. Johnson, Timothy M. Converse, Thomas F. McDougal, and Scott W. Vanderstoep

  35. 31. Feature-Based Induction

    Steven A. Sloman

  36. 32. Deduction as Verbal Reasoning

    Thad A. Polk and Allen Newell

  37. 33. Project Ernestine: Validating a GOMS Analysis for Predicting and Explaining Real-World Task Performance

    Wayne D. Gray, Bonnie E. John, and Michael E. Atwood

  38. III. Issues in Cognitive Modeling
  39. 34. Connectionism and the Problem of Systematicity (Continued): Why Smolensky's Solution Still Doesn't Work

    Jerry Fodor

  40. 35. Networks and Theories: The Place of Connectionism in Cognitive Science

    Michael C. McCloskey

  41. 36. Neuropsychological Inference with an Interactive Brain: A Critique of the "Locality" Assumption

    Martha J. Farah

  42. 37. Is Human Cognition Adaptive?

    John R. Anderson

  43. 38. Précis of Unified Theories of Cognition

    Allen Newell

  44. Contributors
  45. Index