Online courses directory (423)

How genetics can add to our understanding of cognition, language, emotion, personality, and behavior. Use of gene mapping to estimate risk factors for psychological disorders and variation in behavioral and personality traits. Mendelian genetics, genetic mapping techniques, and statistical analysis of large populations and their application to particular studies in behavioral genetics. Topics also include environmental influence on genetic programs, evolutionary genetics, and the larger scientific, social, ethical, and philosophical implications.

This course explores the cognitive and neural processes that support attention, vision, language, motor control, navigation, and memory. It introduces basic neuroanatomy, functional imaging techniques, and behavioral measures of cognition, and discusses methods by which inferences about the brain bases of cognition are made. We consider evidence from patients with neurological diseases (Alzheimer's disease, Parkinson's disease, Huntington's disease, Balint's syndrome, amnesia, and focal lesions from stroke) and from normal human participants.

This course highlights the interplay between cellular and molecular storage mechanisms and the cognitive neuroscience of memory, with an emphasis on human and animal models of hippocampal mechanisms and function. Class sessions include lectures and discussion of papers.

This course examines the neural bases of sensory perception. The focus is on physiological and anatomical studies of the mammalian nervous system as well as behavioral studies of animals and humans. Topics include visual pattern, color and depth perception, auditory responses and sound localization, and somatosensory perception.

Roles of neural plasticity in learning and memory and in development of invertebrates and mammals. An in-depth critical analysis of current literature of molecular, cellular, genetic, electrophysiological, and behavioral studies. Discussion of original papers supplemented by introductory lectures.

This seminar focuses on the cognitive science of moral reasoning. Philosophers debate how we decide which moral actions are permissible. Is it permissible to take one human life in order to save others? We have powerful and surprisingly rich and subtle intuitions to such questions.

In this class, you will learn how intuitions can be studied using formal analytical paradigms and behavioral experiments. Thursday evening, meet to learn about recent advances in theories of moral reasoning. Overnight, formulate a hypothesis about the structure of moral reasoning and design a questionnaire-based experiment to test this. Friday, present and select 1-2 proposals and collect data; we will then reconvene to analyze and discuss results and implications for the structure of the moral mind.

This course is offered during the Independent Activities Period (IAP), which is a special 4-week term at MIT that runs from the first week of January until the end of the month.

This undergraduate course is designed to introduce students to cognitive processes. The broad range of topics covers each of the areas in the field of cognition, and presents the current thinking in this discipline. As an introduction to human information processing and learning, the 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.

This course is an introduction to computational theories of human cognition. Drawing on formal models from classic and contemporary artificial intelligence, students will explore fundamental issues in human knowledge representation, inductive learning and reasoning. What are the forms that our knowledge of the world takes? What are the inductive principles that allow us to acquire new knowledge from the interaction of prior knowledge with observed data? What kinds of data must be available to human learners, and what kinds of innate knowledge (if any) must they have?

An introduction to computational theories of human cognition. Emphasizes questions of inductive learning and inference, and the representation of knowledge. Project required for graduate credit. This class is suitable for intermediate to advanced undergraduates or graduate students specializing in cognitive science, artificial intelligence, and related fields.

This course is an introduction to computational theories of human cognition. Drawing on formal models from classic and contemporary artificial intelligence, students will explore fundamental issues in human knowledge representation, inductive learning and reasoning. What are the forms that our knowledge of the world takes? What are the inductive principles that allow us to acquire new knowledge from the interaction of prior knowledge with observed data? What kinds of data must be available to human learners, and what kinds of innate knowledge (if any) must they have?

The applications of pattern recognition techniques to problems of machine vision is the main focus for this course. Topics covered include, an overview of problems of machine vision and pattern classification, image formation and processing, feature extraction from images, biological object recognition, bayesian decision theory, and clustering.

We will examine current research and theory regarding the validity and utility of commonly accepted gender differences in many realms. Topics include: gender differences in cognitive abilities; the social construction of gender; developmental, family, educational and medical influences; and political and economic forces.

To provide instruction and dialog on practical ethical issues relating to the responsible conduct of human and animal research in the brain and cognitive sciences. Specific emphasis will be placed on topics relevant to young researchers including data handling, animal and human subjects, misconduct, mentoring, intellectual property, and publication.

This course considers molecular control of neural specification, formation of neuronal connections, construction of neural systems, and the contributions of experience to shaping brain structure and function. Topics include: neural induction and pattern formation, cell lineage and fate determination, neuronal migration, axon guidance, synapse formation and stabilization, activity-dependent development and critical periods, development of behavior.

Students will be exposed to all aspects of a cutting-edge technique in modern electrophysiology, in a highly structured, team oriented environment. The research projects will probe the neural mechanisms of learning and memory through tetrode array recordings coupled with patterned microstimulation. Due to the broad nature of tasks to be completed, coupled with the team oriented approach we will be employing, we are interested in students with a wide variety of laboratory experience and skill levels.

Designed for students without previous experience in techniques of cellular and molecular biology, this class teaches basic experimental techniques in cellular and molecular neurobiology. Experimental approaches covered include tissue culture of neuronal cell lines, dissection and culture of brain cells, DNA manipulation, synaptic protein analysis, immunocytochemistry, and fluorescent microscopy.

Advances in cognitive science have resolved, clarified, and sometimes complicated some of the great questions of Western philosophy: what is the structure of the world and how do we come to know it; does everyone represent the world the same way; what is the best way for us to act in the world. Specific topics include color, objects, number, categories, similarity, inductive inference, space, time, causality, reasoning, decision-making, morality and consciousness. Readings and discussion include a brief philosophical history of each topic and focus on advances in cognitive and developmental psychology, computation, neuroscience, and related fields. At least one subject in cognitive science, psychology, philosophy, linguistics, or artificial intelligence is required. An additional project is required for graduate credit.

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.

This course examines interpersonal and group dynamics, considers how the thoughts, feelings, and actions of individuals are influenced by (and influence) the beliefs, values, and practices of large and small groups. Learning occurs through a combination of lectures, demonstrations and in-class activities complemented by participation in small study groups and completion of homework assignments.

An advanced seminar on issues of current interest in human and machine vision. Topics vary from year to year. This year, the class will involve studying the perception of materials. Participants discuss current literature as well as their ongoing research. Topics are tackled from multiple standpoints, including optics, psychophysics, computer graphics and computer vision.