Situated Cognition: On Human Knowledge and Computer Representations (Learning in Doing: Social, Cognitive and Computational Perspectives)

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Publisher ‏ : ‎ Cambridge University Press (August 28, 1997)
Language ‏ : ‎ English
Paperback ‏ : ‎ 428 pages
ISBN-10 ‏ : ‎ 0521448719
ISBN-13 ‏ : ‎ 978-0521448710
Item Weight ‏ : ‎ 1.3 pounds
Dimensions ‏ : ‎ 6 x 1.07 x 9 inches

Situated Cognition: On Human Knowledge and Computer Representations (Learning in Doing: Social, Cognitive and Computational Perspectives)

Situated cognition is a theory that suggests that knowledge is embedded in the context in which it is learned and used. This means that our understanding of the world is shaped by our interactions with our environment and the people around us. In the realm of computer science, this theory has important implications for how we design and use computer representations.

In the book “Learning in Doing: Social, Cognitive and Computational Perspectives,” authors explore the intersection of situated cognition and computer representations. They argue that traditional approaches to representing knowledge in computers, such as symbolic logic and rule-based systems, are limited in their ability to capture the complexities of human cognition.

Instead, the authors propose a more dynamic and interactive approach to representing knowledge in computers. This approach involves creating computer systems that can adapt and learn from their interactions with users and their environment, much like humans do.

By integrating insights from cognitive science, social psychology, and computer science, the authors offer a new framework for understanding how humans and computers can work together to solve complex problems. This framework has the potential to revolutionize the way we think about knowledge and computer representations, leading to more intelligent and adaptive systems.

Overall, “Learning in Doing” offers a compelling argument for the importance of situated cognition in the field of computer science. It challenges us to rethink traditional approaches to representing knowledge in computers and to embrace a more dynamic and interactive model of cognition.
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