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.
"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
Preface. Part 1. Cognitive Neuroscience Approaches to Generalization. N.C. Huff, K. LaBar, Generalization and Specialization of Conditioned Learning. R.W. McGugin, J. Tanaka, Transfer and Interference in Perceptual Expertise: When Expertise Helps and When it Hurts. R. Poldrack, V. Carr, K. Foerde, Flexibility and Generalization in Memory Systems. Part 2. Developmental Perspectives on Generalization. L. Gerken, F.K. Balcomb, Three Observations About Infant Generalization and Their Implications for Generalization Mechanisms. A.V. Fisher, Mechanisms of Induction Early in Development. J. Lany, R.L. Gomez, Prior Experience Shapes Abstraction and Generalization in Language Acquisition. Part 3. Representations that Support Generalization. T.L. Griffiths, Bayesian Models as Tools for Exploring Inductive Biases. M. Huenerfauth, Representing American Sign Language Classifier Predicates Using Spatially Parameterized Planning Templates. K. Levering, K.J. Kurtz, Generalization in Higher-order Cognition: Categorization and Analogy as Bridges to Stored Knowledge. Part 4. Educational, Training Approaches to Generalization. A.C. Graesser, D. Lin, S. D’Mello, Computer Learning Environments with Agents that Support Deep Comprehension and Collaborative Learning. R. Hall, K. Wieckert, K. Wright, How Does Cognition Get Distributed? Case Studies of Making Concepts General in Technical and Scientific Work. C.K. Thompson, Generalization in Language Learning: the Role of Structural Complexity. Part 5. Technological Approaches to Generalization. J. McGrenere, A. Bunt, L. Findlater, K. Moffatt, Generalization in Human-Computer Interaction Research. K.R. Butcher, S. de la Chica, Supporting Student Learning with Adaptive Technology: Personalized Conceptual Assessment and Remediation. S.P. Carmien, G. Fischer, Beyond Human-Computer Interaction: Meta-Design in Support of Human Problem-Domain Interaction. M.T. Banich, D.J. Caccamise, In Summary. Index.