This course studies the relations of affect to cognition and behavior, feeling to thinking and acting, and values to beliefs and practices. These connections will be considered at the psychological level of organization and in terms of their neurobiological and sociocultural counterparts.
In this guide you will find eleven terms and definitions for Computational Thinking (CT) concepts. These concepts can be incorporated into existing lesson plans, projects, and demonstrations in order to infuse CT into any disciplinary subject.
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This course is a survey of the scientific study of human nature, including how the mind works, and how the brain supports the mind. Topics include the mental and neural bases of perception, emotion, learning, memory, cognition, child development, personality, psychopathology, and social interaction. Students will consider how such knowledge relates to debates about nature and nurture, free will, consciousness, human differences, self, and society.
The sensing, thinking, moving body is the basis of our experience in the world; it is the very foundation on which cognitive intelligence is built. Physical Intelligence, then, is the inherent ability of the human organism to function in extraordinary accord with its physical environment. This class--a joint DAPER/ME offering for both PE and academic credit--uses the MIT gymnastics gym as a laboratory to explore Physical Intelligence as applied to ME and design. Readings, discussions and experiential learning introduce various dimensions of Physical Intelligence which students then apply to the design of innovative exercise equipment.
This course is an introduction to the theory that tries to explain how minds are made from collections of simpler processes. It treats such aspects of thinking as vision, language, learning, reasoning, memory, consciousness, ideals, emotions, and personality. It incorporates ideas from psychology, artificial intelligence, and computer science to resolve theoretical issues such as wholes vs. parts, structural vs. functional descriptions, declarative vs. procedural representations, symbolic vs. connectionist models, and logical vs. common-sense theories of learning.