Mathematical Principles of Human Conceptual Behavior

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A01=Ronaldo Vigo
Artificial intelligence
Author_Ronaldo Vigo
Boolean Algebra
Boolean Categories
Boolean Complexity
Boolean concepts
Boolean Expression
Boolean Formulae
Boolean Function
Categorical Invariance
Categorical Stimulus
categorization mechanisms
Category=GTK
Category=JMR
Choice Difficulty
Cognition
cognitive modeling
Complexity
Concept Learning
concept learning theory
Conceptual Behavior
DNF
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eq_nobargain
eq_non-fiction
eq_society-politics
Human Concept Learning
Human Conceptual Behavior
information complexity
Invariance
invariance detection
mathematical models of cognition
Pe Rc
Perception
Probability Theory
QM Method
Relational Homogeneity
Representational Information
Scope
Sensitivity Weights
similarity assessment
Single Scaling Parameter
Stimulus Set
Structural Manifolds
Toy Model Car

Product details

  • ISBN 9780415714365
  • Weight: 498g
  • Dimensions: 152 x 229mm
  • Publication Date: 28 Oct 2014
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Hardback
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The ability to learn concepts lies at the very core of human cognition, enabling us to efficiently classify, organize, identify, and store complex information. In view of the basic role that concepts play in our everyday physical and mental lives, the fields of cognitive science and psychology face three long standing challenges: discovering the laws that govern concept learning and categorization behavior in organisms, showing how they inform other areas of cognitive research, and describing them with the mathematical systematicity and precision found in the physical sciences. In light of these theoretical and methodological shortcomings, this volume will introduce a set of general mathematical principles for predicting and explaining conceptual behavior.

The author’s theory is based on seven fundamental constructs of universal science: invariance, complexity, information, similarity, dissimilarity, pattern, and representation. These constructs are joined by a novel mathematical framework that does not depend on probability theory, and derives key results from conceptual behavior research with other key areas of cognitive research such as pattern perception, similarity assessment, and contextual choice. The result is a unique and systematic unifying foundation for cognitive science in the tradition of classical physics.

Ronaldo Vigo is Associate Professor of Mathematical & Computational Cognitive Science at Ohio University, USA, where he is also director of the Center for the Advancement of Cognitive Science. He received his doctoral degree on Mathematical & Computational Cognitive Science from Indiana University at Bloomington.

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