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Learning to do and learning to understand : a lesson and a challenge for cognitive modeling

Abstract : Book's abstract : The book discusses the analysis, comparison and integration of computational approaches to learning and research on human learning. Learning has for some time been an issue of minor importance in the cognitive sciences. It has, however, now become one of the most active research fields in psychology, the neurosciences, and computer science (machine learning). The aim of this book is to provide the reader with an overview of the prolific research on learning throughout the disciplines. The book will not only provide a general overview for those who are new to the field but will also provide specialist knowledge for those who want to learn more about alternative approaches and conceptualizations of learning in other disciplines. The contributing authors are all considered as leading experts in their field and come from the fields of cognitive, computer and educational science. They provide an assessment of the state-of-the-art of research, links between the disciplines, and they highlight the critically important research issues and methodologies, thus providing a basis for future research. (
Keywords : epistemic feedback
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Contributor : Jerome Zeiliger <>
Submitted on : Monday, May 21, 2012 - 3:54:11 PM
Last modification on : Tuesday, September 17, 2019 - 10:39:23 AM


  • HAL Id : hal-00699807, version 1



Stellan Ohlsson. Learning to do and learning to understand : a lesson and a challenge for cognitive modeling. P. Reiman and H. Spade. Learning in humans and machines towards an interdisciplinary learning science, Pergamon, pp.37-62, 1996. ⟨hal-00699807⟩



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