This course examines language-related topics in cognitive science from a linguistic perspective. The organizing topic of the Fall 2014 offering will be interaction of linguistic knowledge with other cognitive systems.

Advances in theoretical linguistics have made important contributions to our understanding of what we know when we know a language; but the precise manner in which linguistic knowledge interacts with other aspects of cognition when it is put to use in communication is still not well understood. Questions to be addressed include the following: How is it that we can understand what other people intend to communicate when they use language, given that the intended meaning is almost always grossly underspecified by the linguistic form alone. What role do ‘theory of mind’ and attribution of intentions to the communicator play in this process? How does ‘context’ contribute to the resolution of ambiguities and indeterminacies, and what exactly is context? In addressing these questions, we will also examine the disruption (and non-disruption) of linguistic communication in individuals diagnosed with Autism, Alzheimer’s disease, Aphasia, Schizophrenia, Williams Syndrome and Specific Language Impairment (SLI).

Graduate students interested in language and cognition are invited to register for the course, regardless of disciplinary background. For questions or more information, please contact Jeanette Gundel gunde003@umn.edu

This 3-credit course is designed to give students an overview into the fields of clinical physiology and clinical movement science. It provides a basic understanding of clinical issues related to human motor function and physiological parameters of human performance. It presents the newest research methods to study human movement and physiological function and explains how these methods produce clinically relevant research findings. The course is designed to contrast normal development of human function throughout the lifespan and outlines relevant clinical issues of each life phase, such as childhood obesity or rehabilitation after stroke.

Faculty from Kinesiology, Mechanical Engineering, Neurology, Nursing, Otolaryngology, Physical Therapy, and Public Health will provide the latest insights into the following topics:

• Research methodology in clinical movement science
• Research methodology in clinical physiology
• Typical sensorimotor development in childhood
• Motor problems in pediatric populations
• Pediatric clinical physiology
• Aging of the skeletal-muscle system
• Aging of heart, lung and vascular function
• Aging of the motor system
• Pediatric treatment of childhood obesity
• Robotic rehabilitation
• Neurological rehabilitation after stroke

 

 

Prerequisite: Business administration PhD student or instructor consent

We increasingly perform tasks using knowledge that we individually do not possess. Decisions and the solution to problems are as likely to arise from the interaction among people (and among people and artifacts), as they are to result from the capacity of a single individual. The use of various physical, social and intellectual resources to perform tasks has given us many benefits. It has also given us the ability to act without reflection (the philosopher A. N. Whitehead observed that civilization advances by extending the number of things we can do without thinking about them). An interesting consequence of reliance on the knowledge and thinking of others is that our mental models often become divorced from reality. When this happens individuals as well as organizations sometimes act counter to their best interests. Such actions reflect characteristics of the human mind and how it is (and is not) adapted to the demands of modern twentieth century life and work. In this course we examine research and theory on the nature of the mind and how it functions in the modern world.

Drawing on work in psychology, anthropology, philosophy and computer science we develop a framework for understanding the behavior of cognitive agents in various settings of work and daily life. We will be particularly interested in the role of consciousness (including intentionality and narrative thinking), the nature of representation (including the idea of self organizing systems) and the limits of cognitive capacity (e.g., bounded rationality) as explanations for behavior. Data from the study of research problems in the field settings (health care, manufacturing, financial markets) as well as the laboratory will be critiqued and evaluated. Alternative methodologies for investigating behavior will be explored.

Upon completion of the course students should be able to provide an informed critique of research as well as undertake the formulation of a research problem of modest scope using cognitive science theory and methodology. The course format will be lecture and discussion based on assigned readings from the research literature. Course requirements include a weekly synopsis of one assigned reading and a take-home final exam.

 

Learning often involves the revision of prior knowledge at the level of systems, at the level of individual concepts, and of course at the level of individual beliefs. This type of learning is known as conceptual change. In this seminar, we will focus on the discussion of current research in the area of conceptual change learning with the aim to advance our understanding of the underlying cognitive processes involved in knowledge revision and their implications for pedagogy and assessment. Main topics include current theories of conceptual change learning, processes and mechanisms of change, conceptual change in different domains (e.g., science, math, physics), influences of learner characteristics (e.g., prior knowledge, epistemological beliefs, motivation, engagement), instructional approaches for conceptual change, and challenges in conceptual change research.

Graduate students are invited to register for the course, regardless of disciplinary background. For questions or more information, please contact Dr. Panayiota (Pani) Kendeou (kend0040@umn.edu) .

This course is an introduction to theories and behavioral data of cognitive psychology of greatest relevance to education. It is "advanced" in three senses. First, it emphasizes data, theories, and models. Second, it focuses on higher-level cognition. Third, it combines a conventional textbook with papers from the research literature. The topics covered will include: the cognitive revolution, working memory, executive function and cognitive control, long-term memory, learning and transfer, problem solving, expertise, word and sentence comprehension, discourse comprehension, mathematical thinking, reasoning, and cognitive architecture.

Graduate students interested in cognitive psychology are invited to register for the course, regardless of disciplinary background.

For questions or more information, please contact Dr. Sashank Varma (sashank@umn.edu).

What are adolescents, psychopaths and white-collar fraud artists thinking? Why does emotional trauma for victims of abuse last so long? Whey is eye-witness memory so poor? Do violent video games lead to violent children? How can you get into the heads of the judge and jury? Lawyer and courts, including the U.S. Supreme Court, are already integrating neuroscience research into their arguments and opinions on questions such as these. This class will introduce the exciting new field of "neurolaw" by covering issues such as neuroscience of criminal culpability, brain-based lie detection, cognitive enhancement, emotions, decision making, and much more. Along the way we'll discuss how the legal system can and should respond to new insights on topics as adolescent brain development, addiction, psychopathy, Alzheimer's, effects of combat on soldiers' brains, and concussions from sports injuries. (Note that all scientific material in class will be presented in an accessible manner, so no previous science background is required.)

As humans we routinely have to leam new movements during our life time. Some highly complex movements, like playing a piano, become so engrained that we do not forget them even in advanced age. In contrast, after brain injury one may have to relearn even the simplest motor patterns such a standing and walking. This seminar will provide an overview on human sensorimotor learning. We will examine different aspects of motor learning such as skill learning, the learning of motor sequences or the ability to adapt movements to changes in the environment. The course highlights the neurobiology of motor learning and will present current models of motor representations and how they are formed during learning . In addition, we will explore how motor learning can fail with brain dysfunction and discuss the implications of movement disorders research on our understanding of human motor learning.

This course will introduce students to advanced topics in cognition, brain, and behavior. It will combine lecture, discussion, and student-led presentations of research papers on core topics of attention, memory, thinking, language, social cognition, individual differences, consciousness, and intersections between these areas. The course is taught with additional invited faculty lectures. The class is designed for graduate students relatively early in their career.

PREREQUISITES: Graduate standing in EE or IT, or consent of instructor. Familiarity with computer programming, using software of your choice, for homework assignments. MATLAB or similar environment is recommended but not required. Other prerequisites: working knowledge of probability/ statistics and linear algebra, beginning courses on machine learning/data mining, e.g. EE4389, CSci 5521, CSci 5523 or consent of instructor.

INSTRUCTOR: Prof. Vladimir Cherkassky, KHK 6-111, phone (612) 625-9597, email: cherk001@umn.edu

OFFICE Hours Mo, We 4 - 4:45 pm or by appointment

GRADING:
4 homework assignments 40%

Midterm exam 25% on November 19
(open book/ open notes)

course project final report (20%) due Dec 16
midterm progress report (5%) due Nov 12
oral presentation (10%) - Dec 8 and Dec 10
Note: Students registered as S/N: only need to submit the homework.

Optional: extra credit for class participation (up to 5 %)

NOTE: there is no final exam (instead, the class project report is due during the week of finals).

COURSE MATERIAL:
(1) V. Cherkassky and F. Mulier, Learning from Data. Concepts, Theory and Methods,Second edition, Wiley 2007
The textbook can be ordered from Wiley (the publisher), Amazon.com, and it is also available in digital form:
at Google books http://books.google.com/books/about/Learning_from_Data.html?id=IMGzP-IIaKAC
at the University of Minnesota library
(2) V. Cherkassky, Predictive Learning, 2013 http://vctextbook.com/
Available on Amazon.com and also at the University bookstore
Note: most homework assignments will be problems from this textbook.
WEB PAGE: all course material (including lecture notes) will be available at
http://www.ece.umn.edu/users/cherkass/ee8591/

Description

Methods for estimating dependencies from data have been traditionally explored in such diverse fields as: Statistics (multivariate regression and classification), Engineering (pattern recognition, system identification) and Computer Science (artificial intelligence, machine learning, data mining). Recent interest in learning methods triggered by the widespread use of computers and database technology has resulted in the development of biologically motivated methodologies, such as (artificial) neural networks, fuzzy systems and wavelets. Unfortunately, developments in each field are seldom related to other fields. Many data mining applications attempt to estimate predictive models, when estimated models are used for prediction or decision making with new data. This course will first provide general conceptual framework for learning predictive models from data, and then discuss various methods developed in statistics, pattern recognition and machine learning.
COURSE PROJECTS: Each student is expected to complete a project (of research nature). A list of project topics will be distributed during second week of class. Students will receive close supervision and feedback from the instructor. Students may propose their own project topic, subject to instructor's approval.

Course Outline (tentative)

CONCEPTS and THEORY
Introduction/motivation (Chapter 1) 0.5 week
Formulation of the learning problem & classical methods (Ch. 2) 1 week
Adaptive learning: concepts & inductive principles (Ch. 2) 1 week
Regularization and complexity control (Ch. 3) 1 week
Statistical Learning Theory (Ch. 4 and 8) 1.5 week
Nonlinear optimization (Ch. 5) 0.5 week

LEARNING METHODS
Clustering/ VQ/ Self-organizing networks (Ch. 6) 1 weeks
Methods for regression (Ch. 7) 1.5 weeks
Classification (Ch. 8) 1.5 week
Support Vector Machines (Ch. 9) 2 weeks
Advanced Learning Formulations (Ch. 10) 1 week
(All chapter numbers refer to Cherkassky and Mulier, Learning from Data)

The goal of this class is to provide graduate students with the tools they need to (1) execute good psychophysical experiments, (2) create graphics for papers that communicate results clearly, and (3) create compelling graphics for professional presentations and websites. For each general topic, we will devote approximately 5 weeks of class time to discussion and demos and learning relevant software packages and techniques. Class format will be discussion, lecture/demos and group and/or individual projects completed during class time and outside of class.

Each week, students are expected to complete assigned readings and come to class prepared with any (free) software specified for the week's topic installed on their own laptops. Students will take turns signing up to lead the first 10 minutes of class in critique of the visual presentation of information in a relevant (vision-science related) paper, website or presentation. Following the student-led, informal critique of the graphic du jour, instructors will lead the class in an hour-long discussion of theoretical background and hardware or software mechanics for the topic of the day. Students will then work in class (as a group or individually) to create a road map for solving a graphics problem related to the content of the day. During the course of the semester, students will also work independently or in groups outside of class to implement three more substantial projects: one project of their choosing related to each of the three main topics of the course (experiment execution, publication figures, public face).

The course will be in journal club format, in which participants present and discuss recent original research papers. The topic this semester will be "Categorization".

Categorization is cornerstone of cognition and perception; we constantly make value (good/bad), motor (stay/go), and perceptual decisions (friend/foe) which depend on rapid and accurate categorization. With regard to perception, it has traditionally been thought that categorization reflects signals which represent abstract prototypes in higher order cortical areas, and specifically prefrontal cortex, which then are propagated in a top-down manner to alter representations in early areas of cortex. Recently, a range of studies have called into question this prototype-feedback model. This class will discuss recent physiological, imaging, and theoretical studies of categorization, and how it might reflect the selective reward-based reinforcement of feedfoward pathways.

Each meeting will focus on a single paper chosen by the presenter. A tentative reading list of potential papers for the course is available at http://www.ghoselab.cmrr.umn.edu/Classes/8217/Categorization.html

All interested students, faculty members, and postdocs are encouraged to attend. The course typically attracts participants from a variety of departments and perspectives. Students enrolled in the course will be expected to lead the discussion of at least 2 papers over the course of the semester, but all are encourage to present.