Generalization of Knowledge

Multidisciplinary Perspectives

• Edited by Marie T. Banich, and Donna Caccamise.

While the notion of generalization fits prominently into cognitive theories of learning, there is surprisingly little research literature that takes an overview of the issue from a broad multifaceted perspective. This volume remedies this by taking a multidisciplinary perspective on generalization of knowledge from several fields associated with Cognitive Science, including Cognitive Neuroscience, Computer Science, Education, Linguistics, Developmental Science, and Speech, Language and Hearing Sciences.
Researchers from each perspective explain how their field defines generalization - and what practices, representations, processes, and systems in their field support generalization. They also examine when generalization is detrimental or not needed. A principal aim is the identification of general principles about generalization that can be derived from triangulation across different disciplines and approaches.
Collectively, the contributors’ multidisciplinary approaches to generalization provide new insights into this concept that will, in turn, inform future research into theory and application, including tutoring, assistive technology, and endeavors involving collaboration and distributed cognition.

Table of Contents

M. Banich, D. Caccamise, Generalization of Knowledge: An Introduction. Part 1. Biological Perspectives on Generalization. K.S. LaBar, N.L. Huff, Generalization and Specification of Emotion. J. Tanaka, The Training and Transfer of Perceptual Expertise. R.A. Poldrack, Neural Bases of Inflexibility in Memory. Part 2. Developmental Perspectives on Generalization. L. Gerken, Three Questions About Linguistic Generalization in Infants. A. Fisher, V. Sloutsky, Generalization from Known to the Unknown: Flexible yet Non-Strategic. R. Gómez, The Role Of Memory in Generalization. Part 3. Representations that Support Generalization. T. Griffiths, M. Kalish, S. Lewandowsky, Bayesian Models as a Tool For Revealing Inductive Biases. M. Huenerfauth, Representing American Sign Language in a Machine Language System. D. Gentner, Alignment and Abstraction in Learning. Part 4. Educational and Training Methods to Support Generalization. A. Graesser, Computer Learning Environments that Support Inquiry, Deep Comprehension and Collaborative Reasoning. R. Hall, How Does Cognition Get Distributed? Case Studies of Making Concepts General in Technical and Scientific Work. C. Thompson, Complexity in Language Learning and Relearning. Part 5. Applications to Promote Generalization. G. Stahl, The Generalization Of Group Knowledge by a Virtual Math Team. J. McGrenere, Generalization as it Relates to Human-Computer Interaction. J. Hollan, Generalization in Computer Assisted Environments. M. Banich, D. Caccamise, Some Generalizations about Generalization.

Reviews

"This book is an ambitious interdisciplinary undertaking to shed light on an important cognitive process. Never before have biological, developmental, and educational perspectives on knowledge generalization been brought together under one cover. This effort is a model for future interdisciplinary approaches to studying cognition and learning."
- Tamara Sumner, Ph.D., Executive Director of Digital Learning Sciences and Associate Professor at the University of Colorado at Boulder, USA
"This volume addresses a fundamental question: How do individuals extend what they have learned to novel situations? The scope of the volume is striking, with contributions from cognitive and developmental psychology, cognitive neuroscience, education, and computer science. It is sure to be of interest to scholars across all of the cognitive sciences."
- Carol Seger, Ph.D., Colorado State University, USA