An introduction to human information processing and learning; topics include the nature of mental representation and processing; the architecture of memory; pattern recognition; attention; imagery and mental codes; concepts and prototypes; reasoning and problem solving.

Covers the basics of fMRI, the strengths and limitations of fMRI compared to other techniques, and the design and analysis of fMRI experiments, focusing primarily on experiments on high-level vision. Upon completion, students should be able to understand and critique published fMRI papers, have a good grasp on what is known about high-level vision from fMRI, and design their own fMRI experiments.

" We are now at an unprecedented point in the field of neuroscience: We can watch the human brain in action as it sees, thinks, decides, reads, and remembers. Functional magnetic resonance imaging (fMRI) is the only method that enables us to monitor local neural activity in the normal human brain in a noninvasive fashion and with good spatial resolution. A large number of far-reaching and fundamental questions about the human mind and brain can now be answered using straightforward applications of this technology. This is particularly true in the area of high-level vision, the study of how we interpret and use visual information including object recognition, mental imagery, visual attention, perceptual awareness, visually guided action, and visual memory. The goals of this course are to help students become savvy and critical readers of the current neuroimaging literature, to understand the strengths and weaknesses of the technique, and to design their own cutting-edge, theoretically motivated studies. Students will read, present to the class, and critique recently published neuroimaging articles, as well as write detailed proposals for experiments of their own. Lectures will cover the theoretical background on some of the major areas in high-level vision, as well as an overview of what fMRI has taught us and can in future teach us about each of these topics. Lectures and discussions will also cover fMRI methods and experimental design. A prior course in statistics and at least one course in perception or cognition are required."

Analyzes seminal work directed at the development of a computational understanding of human intelligence, such as work on object tracking, object recognition, change representation, language evolution, and the role of symbols in learning and communication. Reviews visionary ideas of Turing, Minsky, and other influential thinkers. Examines the role of brain scanning, systems neuroscience, and cognitive psychology. Emphasis on discussion and analysis of original papers.This course is designed to help students learn about progress toward the scientific goal of understanding human intelligence from a computational point of view. This course complements 6.034, because 6.803/6.833 focuses on long-standing scientific questions, whereas 6.034 focuses on existing tools for building applications with reasoning and learning capability. The content of 6.803/6.833 is largely based on papers by representative Artificial Intelligence leaders, which serve as the basis for discussion and assignments for the course.

Analyzes seminal work directed at the development of a computational understanding of human intelligence, such as work on object tracking, object recognition, change representation, language evolution, and the role of symbols in learning and communication. Reviews visionary ideas of Turing, Minsky, and other influential thinkers. Examines the role of brain scanning, systems neuroscience, and cognitive psychology. Emphasis on discussion and analysis of original papers. Meets with graduate subject 6.833 but assignments differ. 6.803/6.833 is a course in the department's "Artificial Intelligence and Applications" concentration.

Understanding the brain's remarkable ability for visual object recognition is one of the greatest challenges of brain research. The goal of this course is to provide an overview of key issues of object representation and to survey data from primate physiology and human fMRI that bear on those issues. Topics include the computational problems of object representation, the nature of object representations in the brain, the tolerance and selectivity of those representations, and the effects of attention and learning.

Provides a comprehensive introduction to key issues and findings in object recognition in experimental, neural, computational and applied domains. Second half focuses on face recognition. An additional project is required for graduate credit. Alternate years.