Spring 2015

First day of classes: Pitt January 5, 2015; CMU January 12, 2015.

Core courses:
Advanced Systems Neuroscience, Introduction to Parallel Distributed Processing, Statistical Models of the Brain, Systems Neurobiology

Note: students in the CNBC graduate training program automatically have instructor permission to attend any of these core courses, but cross-registration procedures may apply.


CMU Biological Sciences

03-763 Advanced Systems Neuroscience: 12 units [CNBC Core Course]

    • Instructors: Sandra Kuhlman
    • Date/Time/Location: Tues & Thurs 9:00 AM – 10:20 AM (CMU Scaife Hall 125), Thurs 4:30 PM – 5:50 PM (Mellon Institute 355)

This course is a graduate version of 03-363. Students will attend the same lectures as the students in 03-363, plus an additional once weekly meeting. In this meeting, topics covered in the lectures will be addressed in greater depth, often through discussions of papers from the primary literature. Students will read and be expected to have an in depth understanding of several classic papers from the literature as well as current papers that illustrate cutting edge approaches to systems neuroscience or important new concepts. Use of animals as research model systems will also be discussed. Performance in this portion of the class will be assessed by supplemental exam questions as well as by additional homework assignments.


CMU Biomedical Engineering

42-632 Neural Signal Processing: 12 units
(Cross-listed in Electrical & Computer Engineering as 18-698)

      • Instructor: Byron Yu
      • Date/Time: Tues & Thurs 1:30 PM – 2:50 PM
      • Location: Doherty Hall 1212

The brain is among the most complex systems ever studied. Underlying the brain’s ability to process sensory information and drive motor actions is a network of roughly 10^11 neurons, each making 10^3 connections with other neurons. Modern statistical and machine learning tools are needed to interpret the plethora of neural data being collected, both for (1) furthering our understanding of how the brain works, and (2) designing biomedical devices that interface with the brain. This course will cover a range of statistical methods and their application to neural data analysis. The statistical topics include latent variable models, dynamical systems, point processes, dimensionality reduction, Bayesian inference, and spectral analysis. The neuroscience applications include neural decoding, firing rate estimation, neural system characterization, sensorimotor control, spike sorting, and field potential analysis. Prerequisites: 18-290; 36-217, or equivalent introductory probability theory and random variables course; an introductory linear algebra course; senior or graduate standing. No prior knowledge of neuroscience is needed.


CMU CNBC

86-714 Fundamentals of Functional MRI: 12 units
(Cross-listed in Psychology as 85-709)

  • Instructor: John Pyles
  • Date/Time: Tues 5:00 PM – 8:00 PM
  • Location: Baker Hall 332P

Functional MRI (fMRI) has become one of the core experimental methods in cognitive neuroscience and other fields of psychology. This course will provide graduate student researchers with the fundamental knowledge and training necessary to design, acquire, and analyze their own fMRI experiments. Topics will include: MRI physics, MRI safety, best practices in practical aspects of scanning subjects, scanning parameters, experimental design, data pre-processing, data quality, basic data analysis (GLM), and an introduction to advanced analysis (MVPA, surface analysis). The course will move at a rapid pace utilizing lectures, as well as practical lab experience with fMRI analysis software, and visits to the CMU MRI scanner. Students will work through the entire analysis pipeline of a basic fMRI experiment, and also be introduced to more advanced analysis methods. Students should have a background in experimental design and statistics in psychology, as well as some previous experience working in command line computing environments and programming in Matlab.

86-715 Perception and Consciousness: 9 units
(Cross-listed in Psychology as 85-771)

  • Instructor: Wayne Wu & Michael Tarr
  • Date/Time: Tues & Thurs 10:30 AM – 11:50 AM
  • Location: Baker Hall 235A

We explore the idea of conscious and unconscious perception across different modalities and their interactions. Our approach combines neuroscientific data, psychological phenomena, and philosophical analysis, focusing on foundational behaviors and the theory, concepts and presuppositions that structure our investigation of them. Topics include visual awareness, visual cognition, the role of perception in action, cross modal interactions, hallucinations and illusions, the non-visual senses and “sensation”. This will be an upper level undergraduate/graduate course, with focus on lively discussion and will involve several guest speakers.


CMU Computer Science

15-686 Neural Computation: 12 units

  • Instructor: Tai Sing Lee
  • Date/Time: Mon & Wed 1:30 PM – 2:50 PM
  • Location: Gates Hillman 4102

Computational neuroscience is an interdisciplinary science that seeks to understand how the brain computes to achieve natural intelligence. It seeks to understand the computational principles and mechanisms of intelligent behaviors and mental abilities — such as perception, language, motor control, and learning — by building artificial systems and computational models with the same capabilities. This course explores how neurons encode and process information, adapt and learn, communicate, cooperate, compete and compute at the individual level as well as at the levels of networks and systems. It will introduce basic concepts in computational modeling, information theory, signal processing, system analysis, statistical and probabilistic inference. Concrete examples will be drawn from the visual system and the motor systems, and studied from computational, psychological and biological perspectives. Students will learn to perform computational experiments using Matlab and quantitative studies of neurons and neuronal networks.

15-694 Special Topic: Cognitive Robotics: 12 units

  • Instructor: David Touretzky
  • Date/Time: Mon & Wed 3:30 PM – 4:20 PM (Gates Hillman 4211), Fri 3:00 PM – 4:20 PM (Newell Simon Hall 3206)

Cognitive robotics is a new approach to robot programming based on high level primitives for perception and action. These primitives draw inspiration from ideas in cognitive science combined with state of the art robotics algorithms. Students will experiment with these primitives and help develop new ones using the Tekkotsu software framework on the Calliope robot, which includes a 5 degree-of-freedom arm with gripper, a Kinect camera on a pan/tilt mount, and Ubuntu Linux on a dual-core on-board netbook. Prior robotics experience is not necessary, but strong programming skills are required.


CMU Machine Learning

10-701 Machine Learning: 12 units
(Cross-listed as 15-781 for CS PhD students only.)

  • Instructor: Alexander Smola
  • Date/Time: Mon & Wed 9:00 AM – 10:20 AM
  • Location: Doherty Hall A302

Machine learning studies the question “How can we build computer programs that automatically improve their performance through experience?” This includes learning to perform many types of tasks based on many types of experience. For example, it includes robots learning to better navigate based on experience gained by roaming their environments, medical decision aids that learn to predict which therapies work best for which diseases based on data mining of historical health records, and speech recognition systems that lean to better understand your speech based on experience listening to you. This course is designed to give PhD students a thorough grounding in the methods, mathematics and algorithms needed to do research and applications in machine learning. Students entering the class with a pre-existing working knowledge of probability, statistics and algorithms will be at an advantage, but the class has been designed so that anyone with a strong numerate background can catch up and fully participate. You can evaluate your ability to take the course via a self-assessment exam that will be made available to you after you register. If you are interested in this topic, but are not a PhD student, or are a PhD student not specializing in machine learning, you might consider the master’s level course on Machine Learning, 10-601.” This class may be appropriate for MS and undergrad students who are interested in the theory and algorithms behind ML. You can evaluate your ability to take the course via a self-assessment exam at: http://www.cs.cmu.edu/~aarti/Class/10701_Spring14/Intro_ML_Self_Evaluation.pdf

Registration for 15-781 is restricted to CSD PhD and MS students only. All others wishing to register should use the number 10-701, which is the home number for the Machine Learning course.

10-702 Statistical Machine Learning: 12 units
(Cross-listed in Statistics as 36-702)

  • Instructor: Larry Wasserman
  • Date/Time: Mon & Wed 1:30 PM – 2:50 PM
  • Location: Scaife Hall 125

This course builds on the material presented in 10-701(Machine Learning), introducing new learning methods and going more deeply into their statistical foundations and computational aspects. Applications and case studies from statistics and computing are used to illustrate each topic. Aspects of implementation and practice are also treated. A tentative list of topics to be covered includes (but is not restricted to) the following: Maximum likelihood vs. Bayesian inference; Regression, Classficiation, and Clustering; Graphical Methods, including Causal Inference; The EM Algorithm; Data Augmentation, Gibbs, and Markov Chain Monte Carlo Algorithms; Techniques for Supervised and Unsupervised Learning; Sequential Decision making and Experimental Design.

Prerequisites:     10701 and 36705

 


CMU Psychology

85-712 Cognitive Modeling: 12 units

  • Instructor: John Anderson
  • Date/Time: Tues & Thurs 10:30 AM – 11:50 AM
  • Location: Baker Hall 342F

This course will be concerned with modeling of agent behavior in a range of applications from laboratory experiments on human cognition, high-performance simulations such as flight simulators, and video game environments like Unreal Tournament. The first half of the course will teach a high-level modeling language for simulating human perception, cognition, and action. The second half of the course will be a project in which students develop a simulated agent or agents for the application of their choice.

85-719 Introduction to Parallel Distributed Processing: 12 units [CNBC Core Course]

  • Instructor: David Plaut
  • Date/Time: Tues & Thurs 10:30 AM – 11:50 AM
  • Location: Baker Hall 336B

This course will provide an overview of parallel-distributed processing models of aspects of perception, memory, language, knowledge representation, and learning. The course will consist of lectures describing the theory behind the models as well as their implementation, and students will get hands-on experience running existing simulation models on workstations.

85-726 Learning in Humans and Machines: 12 units

  • Instructor: Charles Kemp
  • Date/Time: Mon & Wed 1:30 PM – 2:50 PM
  • Location: Baker Hall 340A

This course provides an introduction to probabilistic models of cognition. The focus is on principles that can help to explain human learning and to develop intelligent machines. Topics discussed will include categorization, causal learning, language acquisition, and inductive reasoning.

85-729 Cognitive Brain Imaging: 12 units

  • Instructor: Marcel Just
  • Date/Time: Tues 7:00 PM – 9:50 PM
  • Location: Baker Hall 336B
  • Prerequisites: Special permission required, contact instructor.

This seminar will examine how the brain executes higher level cognitive processes, such as problem-solving, language comprehension, and visual thinking. The topic will be addressed by examining what recent brain imaging studies can tell us about these various kinds of thinking. This new scientific approach has the potential of providing important information about how the brain thinks, indicating not only what parts perform what function, but also how the activity of different parts of the brain are organized to perform some thinking task, and how various neurological diseases (e.g. aphasia, Alzheimer’s) affect brain activity. A variety of different types of thinking will be examined, including short-term working memory storage and computation, problem solving, language comprehension, visual thinking. Several different technologies for measuring brain activity (e.g. PET and functional MRI and also some PET imaging) will be considered, attempting to relate brain physiology to cognitive functioning. The course will examine brain imaging in normal subjects and in people with various kinds of brain damage. Graduate Students Only.

85-756 Music and Mind: The Cognitive Neuroscience of Sound: 9 units

  • Instructor: Lori Holt
  • Date/Time: Mon & Wed 1:30 PM – 2:50 PM
  • Location: Baker Hall 336B

This course will take a multidisciplinary approach to understand the neural systems that contribute to auditory perception and cognition, using music and speech as domains of inquiry. Students will master topics in acoustics, psychophysics, cognitive psychology, cognitive development, neurophysiology, and neuropsychology. The early part of the course will provide students with a common foundation in acoustics, signal processing, and auditory neuroscience. Later in the semester, the focus will turn to developing analytical skills through critical evaluation of primary-source experimental literature. Hands-on laboratories and homework sets in sound manipulation and experimentation also will constitute a means of learning about auditory cognitive neuroscience. Throughout, the focus will be upon understanding general cognitive and perceptual challenges in perceiving and producing complex sounds like speech and music. Topics may include biological vs. cultural influences, development in infancy, perception versus production, time perception, effects of experience on perceptual processing, comparative studies of animals, attention, development of expertise, effects of brain damage, and emotional expression. Topics will be addressed from the perspective of cognitive neuroscience, in that we will attempt to understand the neural processes that give rise to auditory perception and cognition.

85-785 Auditory Perception: Sense of Sound: 12 units

  • Instructor: Laurie Heller
  • Date/Time: Mon & Wed 10:30 AM – 11:50 AM
  • Location: Baker Hall 342F

This course explores how our sense of hearing allows us to interact with the world. Students will learn about basic principles of sound, spatial sound, sound quality, hearing impairment, auditory perception, interactions with other modalities, and auditory cognition. Topics may also include musical acoustics, basic auditory physiology, sound-semantic associations, acoustic analysis, and sound-making gestures. We will consider not only simple laboratory-generated signals, but also more complex sounds such as those in our everyday environment, as well music and speech. Students will gain some in-class experience with generating sounds and analytic listening. After students reach a sophisticated level of understanding of the auditory fundamentals, they will apply their knowledge to the study of several current issues in auditory research.

 

85-795 Applications of Cognitive Science: 12 units

  • Instructor: Roberta Klatzky
  • Date/Time: Tue & Thu 9:00 AM – 10:20 AM
  • Location: Baker Hall 336B
  • Special permission required, contact instructor.

The famous psychologist George Miller once said that Psychology should “give itself away.” The goal of this course is to look at cases where we have done so — or at least tried. The course focuses on applications that are sufficiently advanced as to have made an impact outside of the research field per se. That impact can take the form of a product, a change in practice, or a legal statute. The application should have a theoretical base, as contrasted, say, with pure measurement research as in ergonomics. Examples of applications are virtual reality (in vision, hearing, and touch), cognitive tutors based on models of cognitive processing, phonologically based reading programs, latent semantic analysis applications to writing assessment, and measurses of consumers’ implicit attitudes. The course will use a case-study approach that considers a set of applications in detail, while building a general understanding of what it means to move research into the applied setting. The questions to be considered include: What makes a body of theoretically based research applicable? What is the pathway from laboratory to practice? What are the barriers – economic, legal, entrenched belief or practice? The format will emphasize analysis and discussion by students.


CMU Robotics

16-720 Computer Vision: 12 units

  • Instructor: Srinivasa Narasimhan
  • Date/Time: Tues & Thurs 3:00 PM – 4:20 PM
  • Location: Gates Hillman 4307
  • Prerequisites: (15122) and (21259) and (18202 or 21241)

This course introduces the fundamental techniques used in computer vision, that is, the analysis of patterns in visual images to reconstruct and understand the objects and scenes that generated them. Topics covered include image formation and representation, camera geometry, and calibration, computational imaging, multi-view geometry, stereo, 3D reconstruction from images, motion analysis, physics-based vision, image segmentation and object recognition. The material is based on graduate-level texts augmented with research papers, as appropriate. Evaluation is based on homeworks and a final project. The homeworks involve considerable Matlab programming exercises. Texts recommended but not required: Title: “Computer Vision Algorithms and Applications” Author: Richard Szeliski Series: Texts in Computer Science Publisher: Springer ISBN: 978-1-84882-934-3 Title: “Computer Vision: A Modern Approach” Authors: David Forsyth and Jean Ponce Publisher: Prentice Hall ISBN: 0-13-085198-1

16-725 Medical Image Analysis: 12 units

(Cross-listed as Pitt Bioengineering BIOE 2630: Methods in Image Analysis.)

  • Instructor: John Galeotti
  • Days/Times: Tue & Thu 10:30 AM – 11:50 AM
  • Location: Wean Hall 5316

Students will gain theoretical and practical skills in medical image analysis, including skills relevant to general image analysis. The fundamentals of computational medical image analysis will be explored, leading to current research in applying geometry and statistics to segmentation, registration, visualization, and image understanding. Student will develop practical experience through projects using the new v4 of the National Library of Medicine Insight Toolkit ( ITK ), a popular open-source software library developed by a consortium of institutions including Carnegie Mellon University and the University of Pittsburgh. In addition to image analysis, the course will include interaction with clinicians at UPMC. NEW THIS YEAR: ITKv4 includes a new simplified interface and many new features, several of which will be explored in the class. Extensive expertise with C++ and templates is no longer necessary (but still helpful). Some or all of the class lectures may also be videoed for public distribution.


CMU Statistics

36-702 Statistical Machine Learning: 12 units
(Cross-listed in Machine Learning as 10-702)

  • Instructor: Larry Wasserman & Ryan Tibshirani
  • Date/Time: Mon & Wed 1:30 PM – 2:50 PM
  • Location: Doherty Hall 1212

This course builds on the material presented in 10-701(Machine Learning), introducing new learning methods and going more deeply into their statistical foundations and computational aspects. Applications and case studies from statistics and computing are used to illustrate each topic. Aspects of implementation and practice are also treated. A tentative list of topics to be covered includes (but is not restricted to) the following: Maximum likelihood vs. Bayesian inference; Regression, Classficiation, and Clustering; Graphical Methods, including Causal Inference; The EM Algorithm; Data Augmentation, Gibbs, and Markov Chain Monte Carlo Algorithms; Techniques for Supervised and Unsupervised Learning; Sequential Decision making and Experimental Design.

Prerequisites: 10701 and 36705

36-759 Statistical Models of the Brain: 12 units [CNBC Core Course]

  • Instructor: Rob Kass
  • Date/Time: Tues & Thurs 10:30 AM – 11:50 AM
  • Location: Mellon Institute 355

This new course is intended for CNBC students, as an additional option for fulfilling the computational core course requirement, but it will also be open to Statistics and Machine Learning students. It should be of interest to anyone wishing to see the way statistical ideas play out within the brain sciences, and it will provide a series of case studies on the role of stochastic models in scientific investigation. Statistical ideas have been part of neurophysiology and the brainsciences since the first stochastic description of spike trains, and the quantal hypothesis of neurotransmitter release, more than 50 years ago. Many contemporary theories of neural system behavior are built with statistical models. For example, integrate-and-fire neurons are usually assumed to be driven in part by stochastic noise; the role of spike timing involves the distinction between Poisson and non-Poisson neurons; and oscillations are characterized by decomposing variation into frequency-based components. In the visual system, V1 simple cells are often described using linear-nonlinear Poisson models; in the motor system, neural response may involve direction tuning; and CA1 hippocampal receptive field plasticity has been characterized using dynamic place models. It has also been proposed that perceptions, decisions, and actions result from optimal (Bayesian) combination of sensory input with previously-learned regularities; and some investigators report new insights from viewing whole-brain pattern responses as analogous to statistical classifiers. Throughout the field of statistics, models incorporating random “noise” components are used as an effective vehicle for data analysis. In neuroscience, however, the models also help form a conceptual framework for understanding neural function. This course will examine some of the most important methods and claims that have come from applying statistical thinking


Pitt Bioengineering

BIOE 2540 Neural Biomaterials and Tissue Engineering: 3 credits

  • Instructor: Tracy Cui
  • Days/Times: Days TBD 3:00 PM – 4:15 PM
  • Location: TBD

This course is designed to introduce students to an advanced understanding of biomaterials and tissue engineering specialized in neural applications. It will review biomaterials used for neural prosthesis, drug delivery devices, and tissue engineering scaffold. The student will gain a fundamental understanding of the biocompatibility issues relevant to a variety of neural implantable devices and the current strategies to solve thse issues. Topics will include basic material science, neural tissue biocompatibilty with implant, bbb and cns drug delivery, tissue engineering and regenerative medicine for pns, tissue engineering and regenerative medicine for cns, neural electrode/tissue interface (including both simulating and recording electrodes, both peripheral and cortical neural interface). The student should have some exposure to biomaterials and tissue engineering before taking this course.

BIOE 2630 Methods in Image Analysis: 3 credits

(Cross-listed as CMU Robotics 16-725: Medical Image Analysis.)

  • Instructor: John Galeotti
  • Days/Times: Tue & Thu 10:30 AM – 11:50 AM
  • Location: Wean Hall 5316

Students will gain theoretical and practical skills in medical image analysis, including skills relevant to general image analysis. The fundamentals of computational medical image analysis will be explored, leading to current research in applying geometry and statistics to segmentation, registration, visualization, and image understanding. Student will develop practical experience through projects using the new v4 of the National Library of Medicine Insight Toolkit ( ITK ), a popular open-source software library developed by a consortium of institutions including Carnegie Mellon University and the University of Pittsburgh. In addition to image analysis, the course will include interaction with clinicians at UPMC. NEW THIS YEAR: ITKv4 includes a new simplified interface and many new features, several of which will be explored in the class. Extensive expertise with C++ and templates is no longer necessary (but still helpful). Some or all of the class lectures may also be videoed for public distribution.

Prerequisites: Knowledge of vector calculus, basic probability, and C++ or python (most lectures will use C++). Required textbook, “Machine Vision”, ISBN: 052116981X; Optional textbook, “Insight to Images”, ISBN: 9781568812175.


Pitt Neurobiology

MSNBIO 2614 Neuropharmacology: CR HRS: 3.0

  • Instructor: Michael Palladino
  • Days/Times: Mon & Wed 1:30 PM – 2:55 PM
  • Location: BST 1395

This course will examine the molecular mechanism of drug action for different classes of drugs that act on the nervous system, antidepressants, antipsychotics, drugs to relieve pain, drugs for neurological diseases, and drug abuse and addiction.


Pitt Neuroscience

NROSCI/MSNBIO 2102 Systems Neurobiology: CR HRS: 6.0 [CNBC Core Course]

  • Note: to register, sign up for NROSCI 2102 and list MSNBIO 2102 as second choice in case the class fills up.
  • Instructor: Dan Simons
  • Days/Times: Mon & Wed 9:00 – 10:20am (Victoria Hall 230), Fri 9:00 – 11:55am (Victoria Hall 230)
  • Prerequisites: MSNBIO 2100 OR NROSCI 2100 (Cellular and Molecular Neurobiology), or INTBP 2000 (Foundations in Biomedical Science), or permission of the instructor. A background in basic biology is required. If students have not had college biology courses, they must obtain consent of the instructor to enroll.

This course is a component of the introductory graduate sequence designed to provide an overview of neuroscience. This course provides an introduction to the structure of the mammalian nervous system and to the functional organization of sensory systems, motor systems, regulatory systems, and systems involved in higher brain functions. It is taught primarily in a lecture format with some laboratory work. The course covers in detail the major sensory, motor and behavioral regulatory systems of the brain. The course satisfies the CNBC core requirement in neuroanatomy.


Pitt Psychology

PSY 2471 Topics in Psychology: Mapping Human Brain Connectivity: CR HRS 3.0

  • Instructor: Walter Schneider
  • Day/Time: Tues 4:00 PM – 5:50PM, Thurs 4:00 PM – 4:50PM
  • Location: Old Engineering Building 303

This class will cover background and technical methods of mapping High Definition Fiber Tracking of brain connectivity for basic research and clinical imaging. The class is for graduate and advanced undergraduates interested in mapping/quantifying anatomical connections of the human brain. These techniques are used to study of brain: systems, disorders, development, and neurosurgery planning. It will involve a laboratory where students will learn to use advanced computation software executing research projects including: developing technical methods, mapping brain networks, or clinical analysis of data. For more details go to www.lrdc.pitt.edu/schneider/D10.pdf

PSY 2576 Topics Seminar in Health Psychology – Health Neuroscience (course number 28896): CR HRS 3.0

  • Instructor: Peter Gianaros
  • Day/Time: Thurs 1:00 PM – 3:55 PM
  • Location: 621 Old Engineering Hall

This seminar will focus on current research examining health factors that affect adult development from a cognitive, social, and neuroscientific perspective. Topics will include nutrition and diet, physical activity, aerobic exercise, social support, intellectual engagement and education, and hormones and genetics. These factors will be discussed on molecular/cellular levels through cognitive/social levels in the hope of bridging research from a variety of disciplines. Class discussion and presentations will be utilized.

PSY 2576 Topics Seminar in Health Psychology – Health and Aging (course number 29577): CR HRS 3.0

  • Instructor: Kirk Erickson
  • Day/Time: Mon 3:00 PM-6:00 PM
  • Location: 4117 Sennott Square

This seminar will focus on current research examining health factors that
affect
adult development from a cognitive, social, and neuroscientific
perspective.
Topics will include nutrition and diet, physical activity, social support,
intellectual engagement and education, and hormones and genetics. These
factors
will be discussed on molecular/cellular levels through cognitive/social
levels
in the hope of bridging research from a variety of disciplines.

PSY 3205 Graduate Seminar: Introduction to Behavior Genetics: CR HRS 3.0

  • Instructors: Michael Pogue-Geile & Kathryn Roecklein
  • Day/Time: Thurs 2:00 PN – 4:30 PM
  • Location: 4125 Sennott Square

The goals of this course are to present and
discuss behavior genetic strategies for investigating the genetic and
environmental causes of psychological differences among individuals. The
following topics will be covered: Overview of genetic principles,
introduction to behavior genetic concepts, designs and analyses and discussion of
research findings from several designs on psychological phenotypes chosen based on
class interest. The course will involve readings, lectures, class discussions, and student presentations of
research articles and study proposals. There are no prerequisites, although knowledge
of statistics is useful. Graduate students throughout the University are welcome, however, those outside the
Department of Psychology should request permission prior to registering. Course evaluation will be based on class
discussion, presentations of research articles and a study proposal.

PSY Cross-Cutting Issues in Reading Science: CR HRS 3.0

  • Instructors: Charles Perfetti
  • Day/Time: Tues 9:30 AM – 12:30 PM
  • Location: 833 Learning Research and Development Center

This cross-disciplinary seminar examines central issues in the science of reading, linking cognitive neuroscience, behavioral, and instructional research to gain an integrated picture of this most profound of human cultural inventions: communicating with language through conventionalized visual symbols. An important thread is the extent to which reading processes are specifically dependent on language and writing system factors, as opposed to universally engaging basic procedures required by human perception and cognition and the neural bases of these processes. Specific topics include the nature of orthographies, lexical expertise, comprehension, dyslexia, learning to read, models of reading processes. Readings will include recent and not-yet published research and sets of new (and to be written) state of the science chapters in learning to read and dyslexia across different languages, and their biological foundations. In addition to participation in seminar discussions, students will write a short seminar paper (flexibility in topic) and present the conclusions from the paper to a final meeting of the seminar.