Feb. 17, 11:45, Wei Ji Ma. Optimality and Probabilistic Computation in Visual Categorization

Core Curriculum in Perceptual Science

The Interdisciplinary Perceptual Science Core Curriculum supplements the doctoral program in the student’s home department. It consists of (1) four foundational courses that are open to all students regardless of undergraduate major, (2) research components, and (3) professional development components. Depending on your particular interests, there are also many related courses relevant to perceptual science, described following the core curriculum.

typical plan of study
 Foundations (years 1-2)Transition (year 3)Integration (years 4-5)
Computer Science
Computational Thinking
Computational Modeling
Sensation & Perception Computational Perception

IMPS: Integrative Methods in Perceptual Science (1 year)

Advanced electives

Qualifying exam - home dept.

Advanced electives

ResearchIntroductory research project (typically fulfilling Master's)

Identify 2 research advisors in human & computer perception

Lab rotation outside primary discipline

Second research project

Doctoral research in one of 6 cross-cutting initiatives integrating human and computer perception
TrainingResearch Seminar
Annual Forum
Research Seminar
Annual Forum
Summer internship in industry
Research Seminar
Annual Forum
Teaching experience
foundation courses
Computer Science

Computational Thinking 16:198:503. Concepts and methods of computer science preparing students to use computational ideas and tools in their research (Computer Science undergraduate majors may be exempt). course description.

Computational Modeling 16:198:504. Introduction to computational modeling as a methodology for explaining the behavior of complex and intelligent systems. Topics include generative, statistical and knowledge-based techniques with examples from perceptual science. course description


Sensation and Perception 16:830:514:01. Psychophysical approaches and models, and underlying neurophysiological mechanisms. Topics include the perception of color, contrast, contour, motion, and depth, audition, haptic senses, perceptual-motor integration, and attention.

Computational Perception 16:830:515. How humans represent complex information in real-world scenes, considering both perceptual performance and computational models.

Perceptual Science

Integrative Methods in Perceptual Science (IMPS). A project-based course that builds on the foundations of knowledge established in years 1 & 2 to develop in the student the ability to do original interdisciplinary research. IMPS provides students with hands-on experience working in a computer-based teaching laboratory on realistic (but modest scale) projects in small groups composed of students from diffrent disciplines. IMPS extends and develops the students' technical and research skills within and outside the primary discipline, while teaching them how to combine and integrate computer and human-based approaches to realistic research problems.

professional development
Perceptual Science Weekly Research Seminar: Faculty, students and outside speakers present and discuss current research and work in progress.

Annual Perceptual Science Forum: The forum provides an opportunity to share ideas and enhance ties among local scientists and students. It features invited speakers and an extensive poster session.
related courses of interest

Computer Science

(UG) Graphics - CS 428, A. Nealan
(Grad) AI - CS 530, V. Pavlov


Advanced topics in the philosophy of language: Metaphor (16:730:670); Ernest Lepore and Matthew Stone
This course approaches metaphorical discourse from the standpoint of the philosophy of language and that of cognitive science. Metaphor is often understood to show that people use their embodied experience, including their perceptual and spatial abilities, to understand more difficult or abstract ideas, as in this malapropism (attributed to Cher): "I've been up and down so many times that I feel as if I'm in a revolving door". Perceptual scientists may therefore be interested in the kind of critical thinking about metaphor that we will examine.


Psychology of Language - 830:351:01/615:371, Karin Stromswold

The course covers all of the course areas in psycholinguistics. There are no prereq's and the course is appropriate for interested grad students in any department. Grad students will be expected to attend all lectures. In addition, grad students will meet with me for an additional session each week during which we will read and discuss classic and recent articles in psycholinguistics. Grad students will be assessed based on a series of short essays and participation in the extra reading/discussion session. A syllabus for the lecture portion from a previous year may be found at:

Advanced Seminar in Perception II - 26:830:576, Maggie Shiffrar. (Note: Seminar in Perception I is an unrelated course)

Text: Visual perception: Physiology, Psychology, and Ecology by Vicki Bruce et al. Paperback publisher: Psychology Press, 4th ed., Sept. 2003, ISBN-10: 1841692387, ISBN-13: 978-1841692388

Electrical & Computer Engineering
Robust Computer Vision - 16:332:570, Peter Meer
The goal of the course is to provide a set of versatile tools for solving large classes of computer vision problems, as well as, to facilitate critical thinking in choosing the most suitable tool for a given problem. Instead of the traditional approach in which each problem class, e.g., low-level vision, motion, stereo, etc., is treated separately, we will focus also on the mathematical/statistical foundations and emphasize the common principles behind techniques employed to solve very different tasks. Robust estimators, which can reliably recover the model of the data under partially correct assumptions, e.g., severe corruption with outliers, will be discussed in details. It is assumed that the students are familiar with basic concepts of linear algebra and random processes, the rest will be taught. Knowledge of MATLAB is required for the assignments and projects. Previous exposure to computer vision is needed since some of the papers will be read by you before a class, we only discuss the differences in the class.
Note: As it is s an EE course, some mathematics is needed.

ECE 231 - 14:332:231, Digital Logic Design, Kristin Dana

Perception experiments often require constructing apparatus with various sensors and motorized stimuli. Consequently, customized electronic control is an integral part of designing and building flexible experiments. This course provides training in digital logic circuits and provides perceptual science students with the requisite knowledge to build control circuitry. Topics include: Binary arithmetic, Boolean algebra, K-maps, Combinational circuit synthesis, Combinational MSI circuits, Sequential logic, Synchronous state machine design, Sequential MSI circuits. Pre-requisites by topic: electrical concepts from Physics, general computer skills.

core course descriptions

Computational Thinking (CS 503)

Goals: By taking this course, you will learn how to participate in interdisciplinary collaborations that depend on the techniques and results of computer science. Over the course, we will develop three important kinds of programs through hands-on case studies: an interpreter for a programming language; a web interface to a database; and a reinforcement-learning agent capable of perception, deliberation and action. We'll also present some of the theoretical background computer scientists use to understand such programs precisely - including representation, complexity, and computability - and introduce the history and culture of the field.

Requirements: Small in-class exercises, recitations and weekly homeworks will give you practice in understanding problems computationally, solving them, and critiquing solutions. Grades will be assigned based on homeworks, class participation, and take-home midterm and final exams. We'll focus on talking computationally as well as thinking computationally, so you understand how to play your part in computational projects - especially those where not everyone on the team has a hand in the programming. The central place of skills development in the class means that typical students will not be well served by attending as auditors.

Audience: The course is geared to students with some mathematical sophistication. You should be familiar with abstraction and proof from a course such as linear algebra (as 01:640:250), mathematics of probability (as 01:640:477 or 01:198:206), or formal logic (as 01:640:461 or 01:730:407). No background in computer science or programming will be presupposed; however, computer science students with interdisciplinary interests are very welcome in the class, as they are likely to benefit from our reflections on talk and teamwork in computer science. While the course will not count towards graduate requirements in computer science (other than the graduate school's general requirement that PhD students take 48 credits of coursework), students can take it together with its companion class CS 504 in the Spring to satisfy prerequisites for Rutgers's advanced graduate courses in Artificial Intelligence.

Computational Modeling (CS 504)

Goals: In this course, you will learn to discuss, play with, test, understand,and sharpen theories of complex systems as computer programs -- not just theories of perception that we as cognitive scientists argue about, but also theories of the world that agents entertain to help solve their problem of action under uncertainty. Computational modeling clarifies the theories and their underlying assumptions. It also brings to life the tradeoff between bias and variance that us and the agents we study both face as we seek and weigh hypotheses for truth and beauty.

Audience: This course is for students both in and outside of computer science whowant to participate in collaborative, interdisciplinary research. Itrequires the ability to talk and think computationally about programs, data, processes, abstraction, and proofs, such as taught and exercised in 16:198:503, Computational Thinking. While the course will not count towards graduate requirements in computer science (other than the graduate school's general requirement that PhD students take 48 credits of coursework), students who complete it will be ready for Rutgers's advanced graduate courses in artificial intelligence. The central place of skills development in the class means that typical students will not be well served by attending as auditors.