This training course is an introduction to the use of the AFNI software suites for the analysis of functional MRI (fMRI) data. It is not intended as an introduction to how fMRI works but is aimed at people who are already doing fMRI data analysis, or those who will be in the near future. 
AFNI (Analysis of Functional NeuroImages) is a leading software suite of C, Python, and R programs and shell scripts, primarily developed for the analysis and display of anatomical and fMRI data. It is freely available for research purposes. 
This event was organized by the Center for Brains, Minds, and Machines (CBMM) Trainee Leadership Council.
CBMM is a multi-institutional NSF Science and Technology Center headquartered at MIT that is dedicated to developing a computationally based understanding of human intelligence and establishing an engineering practice based on that understanding. CBMM brings together computer scientists, cognitive scientists, and neuroscientists to create a new field—the science and engineering of intelligence.

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).

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.

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.

A comprehensive collection of multimedia resources and inquiry-based activities tied to the National Science Education Standards help teachers and students learn about the structure, function and cognitive aspects of the human brain. The packet includes a teacher's manual, student manual, DVD of videos, and a CDROM of accompanying materials. (National Institute of Mental Health)

This course explores the problem of intelligence—its nature, how it is produced by the brain and how it could be replicated in machines—using an approach that integrates cognitive science, which studies the mind; neuroscience, which studies the brain; and computer science and artificial intelligence, which study the computations needed to develop intelligent machines. Materials are drawn from the Brains, Minds and Machines Summer Course offered annually at the Marine Biological Laboratory in Woods Hole, MA, taught by faculty affiliated with the Center for Brains, Minds and Machines headquartered at MIT. Elements of the summer course are integrated into the MIT course, 9.523 Aspects of a Computational Theory of Intelligence.
Contributors
This course includes the contributions of many instructors, guest speakers, and a team of iCub researchers. See the complete list of contributors.

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 resource is a video abstract of a research paper created by Research Square on behalf of its authors. It provides a synopsis that's easy to understand, and can be used to introduce the topics it covers to students, researchers, and the general public. The video's transcript is also provided in full, with a portion provided below for preview:

"Looking at these two figures, does one of the solid black circles appear larger than the other? What about these apples? If the apple or the circle on the right looks bigger, your eyes have been fooled by an optical illusion. One that may help fight obesity. Inspired by these illusions, researchers have recently developed a portion-control plate as a means to promote smaller meal sizes. A new study published in the journal, BMC Obesity, evaluates the success of this plate. The World Heath Organization has officially declared obesity to be a global epidemic, with 38% of women and nearly as many men affected world-wide. An important reason for this is an increase in energy intake without the corresponding energy expenditure, and large portion sizes have been implicated as a key player in creating this imbalance. Unfortunately, many people find it a difficult task to learn healthy portion sizes and consistently consume the proper amount of food for every meal..."

The rest of the transcript, along with a link to the research itself, is available on the resource itself.

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 resource is a video abstract of a research paper created by Research Square on behalf of its authors. It provides a synopsis that's easy to understand, and can be used to introduce the topics it covers to students, researchers, and the general public. The video's transcript is also provided in full, with a portion provided below for preview:

"For scientists and other academics hoping to translate their findings into political change, policy experts offer the following advice: Tell a good story. Unfortunately, the academic literature—researchers’ go-to source for evidence that things actually work the way people claim they do—says little on how to go about crafting a compelling narrative. For that, says media expert Brett Davidson, researchers must delve into a lesser known but no less copious store of useful know-how: the grey literature. In a recent paper, Davidson outlines how this overlooked body of work provides researchers a manual for landing on the political agenda. A catch-all term for any research or materials not controlled by commercial publishers, the grey literature is a compendium of lessons learned through trial and error. Long a survival guide for policy advocates and activists working in the non-profit world, now, Davidson argues, it’s time for researchers to take advantage, too..."

The rest of the transcript, along with a link to the research itself, is available on the resource itself.

This class investigates cognitive science and technology as it is applied to the industrial design process. The class introduces prototyping techniques and approaches for objective evaluation as part of the design process. Students practice evaluating products with mechanical and electronic aspects. Evaluation processes are applied to creating functioning smart product prototypes. This is a project oriented subject that draws upon engineering, aesthetic, and creative skills. It is geared toward students interested in creating physical products which encompass electronics and computers, aimed at including them in smart scenarios. Students in the class will present readings, learn prototyping skills, create a product prototype, and complete a publication style paper.

Set of open educational resources related to the field of Cognitive Psychology. Mostly adapted from various other OER sources.

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.

This course provides an overview of the distinctive features which distinguish sound categories of languages of the world. Theories which relate these categories to their acoustic and articulatory correlates, both universally and in particular languages, are covered. Models of word recognition by listeners, features, and phonological structure are also discussed. In addition, the course offers a variety of perspectives on these issues, drawn from Electrical Engineering, Linguistics and Cognitive Science.

This series helps learners understand magnetoencephalography (MEG) signals through the lens of source estimation, decoding, and connectivity: principles, pitfalls, and perspectives.
MEG methodological approaches have grown remarkably during the 50-year history of MEG. A breadth of source estimation tools can localize brain activity even in challenging situations. Pattern analysis of brain activity can perform feats of mind reading by revealing what a person is seeing, perceiving, attending to, or remembering. Functional connectivity approaches can assess the role of large-scale brain networks in cognitive function. The aim of this workshop is to deconstruct these tools, overview the challenges and limitations, and demonstrate MEG data analysis procedures to a novice researcher.
This workshop was sponsored by the Center for Brains, Minds, and Machines (CBMM), a multi-institutional NSF Science and Technology Center headquartered at MIT that is dedicated to the study of intelligence—how the brain produces intelligent behavior and how we may be able to replicate intelligence in machines.

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.

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 course focuses on neural structures and mechanisms mediating the detection, localization and recognition of sounds. Discussions cover how acoustic signals are coded by auditory neurons, the impact of these codes on behavioral performance, and the circuitry and cellular mechanisms underlying signal transformations. Topics include temporal coding, neural maps and feature detectors, learning and plasticity, and feedback control. General principles are conveyed by theme discussions of auditory masking, sound localization, musical pitch, speech coding, and cochlear implants.