Formal Approaches in Categorization (eBook, PDF)

Redaktion: Pothos, Emmanuel M.

• Format: PDF

Produktbeschreibung

The process of constructing concepts underpins our capacity to encode information in an efficient and competent manner and also, ultimately, our ability to think in terms of abstract ideas such as justice, love and happiness. But what are the mechanisms which correspond to psychological categorization processes? This book unites many prominent approaches in modelling categorization. Each chapter focuses on a particular formal approach to categorization, presented by the proponent(s) or advocate(s) of that approach, and the authors consider the relation of this approach to other models and the ultimate objectives in their research programmes. The volume evaluates progress that has been made in the field and where it goes from here. This is an essential companion to any scientist interested in the formal description of categorization and, more generally, in formal approaches to cognition. It will be the definitive guide to formal approaches in categorization research for years to come.

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Produktdetails

• Verlag: Cambridge University Press
• Erscheinungstermin: 27. Januar 2011
• Englisch
• ISBN-13: 9780511855627
• Artikelnr.: 38210515

Inhaltsangabe

1. Introduction Emmanuel M. Pothos and Andy J. Wills; 2. The generalized
context model: an exemplar model of classification Robert M. Nosofsky; 3.
Prototype models of categorization: basic formulation, predictions, and
limitations John Paul Minda and J. David Smith; 4. COVIS F. Gregory Ashby,
Erick J. Paul and W. Todd Maddox; 5. Semantics without categorization
Timothy T. Rogers and James L. McClelland; 6. Models of attentional
learning John K. Kruschke; 7. An elemental model of associative learning
and memory Evan Livesey and Ian McLaren; 8. Nonparametric Bayesian models
of categorization Thomas L. Griffiths, Adam N. Sanborn, Kevin R. Canini,
Daniel J. Navarro and Joshua B. Tenenbaum; 9. The simplicity model of
unsupervised categorization Emmanuel M. Pothos, Nick Chater and Peter
Hines; 10. Adaptive clustering models of categorization John V. McDonnell
and Todd M. Gureckis; 11. COBWEB models of categorization and probabilistic
concept formation Wayne Iba and Pat Langley; 12. The knowledge and
resonance (KRES) model of category learning Harlan D. Harris and Bob
Rehder; 13. The contribution (and drawbacks) of models to the study of
concepts Gregory L. Murphy; 14. Formal models of categorization: insights
from cognitive neuroscience Lukas Strnad, Stefano Anzellotti and Alfonso
Caramazza; 15. Comments on models and categorization theories: the razor's
edge Douglas Medin.