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 class is the second half of an intensive survey of cognitive science for first-year graduate students. Topics include visual perception, language, memory, cognitive architecture, learning, reasoning, decision-making, and cognitive development. Topics covered are from behavioral, computational, and neural perspectives.

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?

This course is the second half of the intensive survey of brain and behavioral studies for first-year graduate students in the Brain and Cognitive Sciences curriculum. Each module of this core course involves a series of overview lectures by leading researchers in the field. By offering a thorough introduction to the current state of the discipline while emphasizing critical thinking, the course aims to prepare students as cognitive scientists.
Topics include: perception, attention, working memory, recognition and recall, language, and other issues in cognitive science. Topics are covered from the neural, behavioral and computational perspectives.

Teaches principles of experimental methods in human perception and cognition, including design and statistical analysis. Combines lectures and hands-on experimental exercises; requires an independent experimental project. Some experience in programming desirable. To foster improved writing and presentation skills in conducting and critiquing research in cognitive science, students are required to provide reports and give oral presentations of three team experiments; a fourth individually conducted experiment includes a proposal with revision, and concluding written and oral reports.

Cognitive science arose in the 1950s when it became apparent that a number of disciplines, including psychology, computer science, linguistics, and philosophy, were fragmenting. Perhaps owing to the field’s immediate origins in cybernetics, as well as to the foundational assumption that cognition is information processing, cognitive science initially seemed more unified than psychology. However, as a result of differing interpretations of the foundational assumption and dramatically divergent views of the meaning of the term information processing, three separate schools emerged: classical cognitive science, connectionist cognitive science, and embodied cognitive science.

This is a seminar series led by graduate students and postdocs in the MIT Department of Brain and Cognitive Sciences (BCS) from 2015 to the present, featuring tutorials on computational topics relevant to research on intelligence in neuroscience, cognitive science, and artificial intelligence. These tutorials are aimed at participants who have some computational background but are not experts on these topics.
A computational tutorial can consist of any method, tool, or model that is broadly relevant within neuroscience, cognitive science, and artificial intelligence. The goal is to bring researchers in brain and cognitive sciences closer to the researchers creating computational methods. 
Resources posted here include lecture videos, lecture slides, code and datasets for exercises, background references, and other supplementary material. Typically, each tutorial consists of a short lecture, and an interactive part with tutorials or "office hours" to work through practice problems and discuss how the material may be applied to participants’ research. 
This series was organized by Emily Mackevicius, Jenelle Feather, Nhat Le, Fernanda De La Torre Romo, and Greta Tuckute, with financial support from BCS. Videos were filmed, edited, and produced by Kris Brewer, Director of Technology at the Center for Brains, Minds, and Machines (CBMM).

A reading about the GenderMag Cognitive Style Heuristics (CSH) with embedded examples and quizzes.

Memory is not a unitary faculty, but rather consists of multiple forms of learning that differ in their operating characteristics and neurobiological substrates. This seminar will consider current debates regarding the cognitive and neural architectures of memory, specifically focusing on recent efforts to address these controversies through application of functional neuroimaging (primarily fMRI and PET).

Cognitive Psychology is a psychological science which is interested in various mind and brain related subfields such as cognition, the mental processes that underlie behavior, reasoning and decision making.

In this course you will learn about social cognition—the part of psychology that deals with how individuals understand and make sense of the social world. You will learn about research that allows you to better understand how people think about and act upon their social environment and the people who inhabit it. On the one hand, social cognition is a theoretical, fundamental part of psychology. It can give us answers about such fundamental questions as: how do people form opinions? Or: why do people sometimes do good things, and sometimes they behave unfairly or in a morally questionable way? On the other hand, social cognition is also a practical part of psychology because it allows you to make sense of social phenomena, which can in turn be applied to areas such as consumer decisions. The course covers classical psychological research about social cognition, and also discusses current debates in the field.

The aims of the course are to help you gain knowledge and understanding about theoretical and empirical perspectives, and to practice making judgments about the scientific literature we address. Specifically, on successful completion of this course, you will be able to

- explain key ways through which social settings influence cognitive functioning and overt behavior,
- explain the key theoretical concepts applied to explain of classical effects found in the social cognition literature,
- explain the design of classical studies in social cognition,
- interpret the results of classical studies in social cognition,
- compare the results of classical, more recent and replication studies in social cognition,
- illustrate selected cognitive and behavioral findings from the social cognition literature,
- plan your future approach to studying established scientific literature on social cognition while integrating state-of-the-art findings.

The contents are:
- History & Concepts
- Memory
- Heuristics & Biases
- Deliberate Decisions
- Affect, Mood & Emotions
- Automaticity
- Stereotypes
- Social Comparison
- Prosociality & Morality
- Consumer Behavior
- Approach & Avoidance

This is a sort of flowchart describing the psychological process of cognitive appraisal. This could be used during a lecture or as a lesson handout.

This is a free, open-educational textbook and set of course materials for an introductory undergraduate course in cognition.

The suite is developed using open-source software (R, RStudio, Bookdown). The source code for this project is available at https://github.com/CrumpLab/cognition.

The suite includes:

This textbook: https://www.crumplab.com/cognition/textbook
A course website: https://www.crumplab.com/cognition/
Web-based slide decks for a one semester long course (see course website)
An exam test bank (email mcrump@brooklyn.cuny.edu)

We have plans to continually revise, improve, and add to this suite. In particular, a major stretch goal for the textbook is to embed lab activities in the form of web-based experiment demonstrations, and data-analysis demonstrations. Another goal is to develop tutorials showing others how to copy, use, and/or collaborate on content development.

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.

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.

9.63 teaches principles of experimental methods in human perception and cognition, including design and statistical analysis. The course combines lectures and hands-on experimental exercises and requires an independent experimental project. Some experience in programming is desirable. To foster improved writing and presentation skills in conducting and critiquing research in cognitive science, students are required to provide reports and give oral presentations of three team experiments. A fourth individually conducted experiment includes a proposal with revision, and concluding written and oral reports.

Survey and special topics designed for students in Brain and Cognitive Sciences. Emphasizes ethological studies of natural behavior patterns and their analysis in laboratory work, with contributions from field biology (mammology, primatology), sociobiology, and comparative psychology. Stresses human behavior but also includes major contributions from studies of other animals.

Provides students with the basic tools for analyzing experimental data, properly interpreting statistical reports in the literature, and reasoning under uncertain situations. Topics organized around three key theories: Probability, statistical, and the linear model. Probability theory covers axioms of probability, discrete and continuous probability models, law of large numbers, and the Central Limit Theorem. Statistical theory covers estimation, likelihood theory, Bayesian methods, bootstrap and other Monte Carlo methods, as well as hypothesis testing, confidence intervals, elementary design of experiments principles and goodness-of-fit. The linear model theory covers the simple regression model and the analysis of variance. Places equal emphasis on theory, data analyses, and simulation studies.

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.