Cognitive Science Doctor of Philosophy (Ph.D.) Degree

RIT’s cognitive science Ph.D. provides an interdisciplinary study of the human mind that combines insights from psychology, computer science, linguistics, neuroscience, augmented reality, and philosophy.

Overview for Cognitive Science Ph.D.

Students in the Ph.D. in cognitive science conduct research on human perception, cognition, action, and language with a focus on the representation and processing of information within biological and computational frameworks. The cognitive science Ph.D. prepares students for careers in academia or industry and develops abilities to analyze data, grasp complex concepts, and interpret and communicate concepts for a wider audience. Faculty advisors come from across the RIT campus to create a thriving, interdisciplinary community that supports students on their path to becoming independent scholars.

Interdisciplinary Curriculum

The cognitive science program is jointly delivered by faculty experts from five RIT colleges, allowing students to develop valuable, career-enhancing interdisciplinary skills and communication competency as part of the program experience.

The five colleges include: College of Liberal Arts; College of Science; Golisano College of Computing and Information Sciences; Kate Gleason College of Engineering; and National Technical Institute for the Deaf.



Cognitive Science Research

Ph.D. in cognitive science faculty, all sharing a passion for understanding the biological and computational foundations of human cognition - including memory, perception, attention, language, learning, decision-making, creativity, and problem solving. While faculty research interests are broad, our program emphasizes:

  • Context, culture, and cognition: Whilst the existence of cognitive universals is likely given the high degree of genetic overlap across animal species, there are significant variations in environments that are likely to lead to individual differences in cognition. Understanding how context and culture drive this variability can both improve our understanding of human cognition and lead to a more inclusive cognitive science.
  • Biologically-inspired computational models: Computational models provide insights into the mechanisms of cognition and how information is represented and processed in cognitive systems. Biologically-inspired models not only help us constrain our cognitive theories, they also allow us to apply the insights of cognitive science to the development of innovative and powerful computational tools.
  • Cognition and human action: Cognition cannot be separated from action. The purpose of cognition is to allow us to navigate the world around us in both goal-driven and stimulus-responsive ways. Therefore it is important to explain how cognition is realized in human behavior, and the role of cognition in guiding human action.

Curriculum for 2023-2024 for Cognitive Science Ph.D.

Current Students: See Curriculum Requirements

Cognitive Science, Ph.D. degree, typical course sequence

Course Sem. Cr. Hrs.
First Year
 Foundations in Research
Cognitive Science Research Colloquium
Laboratory Methods
Philosophical Foundations in Cognitive Science
Graduate Statistics
This course reviews descriptive and inferential statistics. Basic and advanced conceptual material will be presented to assist students in their understanding of diverse data analytic methods, their appropriate application, and how to interpret statistical analyses. Topics include one- and two-sample inferential procedures, interval estimation, correlation, nonparametric tests, linear regression, and analysis of variance. Students will learn to integrate concepts with computer applications. Course content will be taught through lectures, discussion, and applied data analysis exercises. Student mastery of the material will be evaluated through small group discussion of data set analyses, written results of the analyses following APA style, and two exams. Lecture 3 (Fall, Spring, Summer).
Graduate Cognition
This course will survey theoretical and empirical approaches to understanding the nature of the mental processes involved in attention, object recognition, learning and memory, reasoning, problem solving, decision-making, and language. The course presents a balance between historically significant findings and current state of-the-art research. Readings that have structured the nature and direction of scientific debate in these fields will be discussed. The course also includes discussions of methodology and practical applications. Students will have opportunities to develop their research skills and critical thinking by designing research studies in cognitive psychology. Seminar (Spring).
Second Year
Cognitive Science Research Colloquium
Teaching Practicum
Foundations of Scientific Computing
Advanced Graduate Statistics
This course introduces students to more advanced inferential parametric and non-parametric data-analysis techniques commonly used in psychological research, but not covered (or not covered in depth) in the Graduate Statistics course. These techniques may include, but are not limited to: Reliability Analysis, Multiple Regression, Discriminant Analysis, Logistic Regression, Factor Analysis, Analysis of Covariance, Multivariate Analysis of Variance, Contrast Analysis, Mediator and Moderator Variable Analysis, Non-Parametric Tests, and Multi-level Modeling. The focus is on the conceptual understanding of these statistics, how different statistical procedures are applied in different research methods, how to perform analyses, how to interpret the results in the context of the research question, and how to communicate these results. (Prerequisites: PSYC-640 or equivalent course.) Lecture 3 (Biannual).
Third Year
Cognitive Science Research Colloquium
Cognitive Science Dissertation Research
Dissertation and Research
Cognitive Science Dissertation Research
Total Semester Credit Hours


Program Electives

Cognitive Neuroscience
Cognitive neuroscience is concerned with the study of the biological processes that underlie cognition with a specific focus on neural systems in the brain that are involved in mental processes. This course provides the foundation of cognitive neuroscience including neuroanatomy, neural signaling, motor and sensory pathways, experimental methods employed in cognitive neuroscience, and discusses the neural bases of complex cognitive functions such as attention, perception, learning, memory, emotional regulation, executive control, decision making and language. Critical analysis of primary research and research projects employed in the course foster an in-depth understanding of main areas of cognitive neuroscience and its recent advances. (This class is restricted to degree-seeking graduate students or those with permission from instructor.) Lecture 3 (Spring).
Design Thinking and Cognition
Design thinking refers to the cognitive, strategic, and practical processes involved in problem solving and creation of innovative design concepts, which can lead to the development of new products and to aid scientific exploration. Several key concepts and aspects of design thinking have been pinpointed through studies of design cognition, focusing on understanding of how designers think when they are trying to find creative and original solutions for vague, ill-defined problems. Currently, cognitive neuroscientists are becoming increasingly interested in identified brain mechanisms of design thinking. This course focuses on the principles, processes, and scientific underpinnings of design thinking and their applications to real-life innovative problem solving. (Prerequisite: CGNS-601 or equivalent course.) Lecture 3 (Spring).
Modeling Visual Perception
This course presents the transition from the measurement of color matches and differences to the description and measurement of color appearance in complex visual stimuli. This seminar course is based mainly on review and student-led discussion of primary references. Topics include: appearance terminology, appearance phenomena, viewing conditions, chromatic adaptation, color appearance modeling, image appearance, image quality, and material appearance. (Prerequisites: CRLS-601 and CLRS-720 or equivalent courses.) Lecture 3 (Spring).
Machine Intelligence
Machine intelligence teaches devices how to learn a task without explicitly programming them how to do it. Example applications include voice recognition, automatic route planning, recommender systems, medical diagnosis, robot control, and even Web searches. This course covers an overview of machine learning topics with a computer engineering influence. Includes Matlab programming. Course topics include unsupervised and supervised methods, regression vs. classification, principal component analysis vs. manifold learning, feature selection and normalization, and multiple classification methods (logistic regression, regression trees, Bayes nets, support vector machines, artificial neutral networks, sparse representations, and deep learning). (Prerequisites: CMPE-380 and CMPE-480 and MATH-251 or graduate standing in the CMPE-MS, CMPE-BS/MS program.) Lecture 3 (Fall).
Brain Inspired Computing
This course is primarily designed for graduate students and will expose them to theoretical and practical aspects of brain-inspired computing. It will offer students the opportunity to understand how the human brain computes to achieve intelligent behavior and how this understanding guides the development of new neural algorithms. We will identify the key developments and large issues at stake, and study brain inspired systems in the context of pragmatic applications. At the end of the course the students are expected to have expanded their knowledge of how the brain processes information, and how one can develop neuromorphic algorithms to tackle emergent spatio-temporal problems. (Prerequisites: CMPE-260 and MATH-251 or equivalent course or graduate standing in CMPE-MS.) Lecture 3 (Spring).
Foundations of Cognitive Modeling
Graduate Psycholinguistics
Animal Cognition
Psycholinguistics of Signed Languages
Neuroplasticity in Deaf and Blind Individuals
Deaf Vision
Foundations of Artificial Intelligence
An introduction to the theories and algorithms used to create artificial intelligence (AI) systems. Topics include search algorithms, logic, planning, machine learning, and applications from areas such as computer vision, robotics, and natural language processing. Programming assignments and oral/written summaries of research papers are required. (Prerequisites:((CSCI-603 or CSCI-605) &CSCI-661) with grades of B or better or ((CSCI-243 or SWEN-262)&(CSCI-262 or CSCI-263)).If you have earned credit for CSCI-331 or you are currently enrolled in CSCI-331 you won't be permitted to enroll in CSCI-630.) Lecture 3 (Fall, Spring).
Foundations of Computer Vision
An introduction to the underlying concepts of computer vision and image understanding. The course will consider fundamental topics, including image formation, edge detection, texture analysis, color, segmentation, shape analysis, detection of objects in images and high level image representation. Depending on the interest of the class, more advanced topics will be covered, such as image database retrieval or robotic vision. Programming assignments are an integral part of the course. Note: students who complete CSCI-431 may not take CSCI-631 for credit. (Prerequisites:(CSCI-603 and CSCI-605 and CSCI-661 with grades of B or better) or ((CSCI-243 or SWEN-262) and (CSCI-262 or CSCI-263)) or equiv courses. If earned credit for/or currently enrolled in CSCI-431 you will not be permitted to enroll in CSCI-631.Prerequisites:(CSCI-603 and CSCI-605 and CSCI-661 with grades of B or better) or ((CSCI-243 or SWEN-262) and (CSCI-262 or CSCI-263)) or equiv courses. If earned credit for/or currently enrolled in CSCI-431 you will not be permitted to enroll in CSCI-631.) Lecture 3 (Fall, Spring).
Biologically Inspired Intelligent Systems
There have been significant advances in recent years in the areas of neuroscience, cognitive science and physiology related to how humans process information. In this course students will focus on developing computational models that are biologically inspired to solve complex problems. A research paper and programming project on a relevant topic will be required. A background in biology is not required. (CSCI-603,605,661 or CSCI ETC..) Lecture 3 (Fall).
Introduction to Machine Learning
This course offers an introduction to supervised machine learning theories and algorithms, and their application to classification and regression tasks. Topics include: Mathematical background of machine learning (e.g. statistical analysis and visualization of data), neural models (e.g. Convolutional Neural Networks, Recurrent Neural Networks), probabilistic graphical models (e.g. Bayesian networks, Markov models), and reinforcement learning. Programming assignments are required. (Prerequisites: (CSCI-603 or CSCI-605 with a grade of B or better) or ((CSCI-243 or SWEN 262) and (MATH-251 or STAT-205)) or equivalent courses.) Lecture 3 (Fall, Spring).
Neural Networks and Machine Learning
The course will introduce students into the current state of artificial neural networks. It will review different application areas such as intrusion detection and monitoring systems, pattern recognition, access control and biological authentication, and their design. The students will be required to conduct research and analysis of existing applications and tools as well as to implement a course programming project on design of a specified application based on neural networks and/or fuzzy rules systems. (Prerequisites: CSCI-630 or CSCI-331 or equivalent course.) Lec/Lab 3 (Spring).
The Human Visual System
This course describes the underlying structure of the human visual system, the performance of those structures and the system as a whole, and introduces psychophysical techniques used to measure them. The visual system's optical neural systems responsible for collecting and detecting spatial, temporal, and spectral signals from the environment are described. The sources and extent of limitations in the subsystems are described and discussed in terms of the "enabling limitations" that allow practical imaging systems. (This course is restricted to Graduate students.) Lecture 2 (Fall).
Interactive Virtual Env
This course provides experience in the development of real-time interactive three-dimensional environments, and in the use of peripherals, including virtual reality helmets, motion tracking, and eye tracking in virtual reality. Students will develop expertise with a contemporary Game Engine, along with an understanding of the computations that facilitate 3D rendering for interactive environments. Projects will cover topics such as lighting and appearance modelling, mathematics for vertex manipulation, 3D to 2D projection, ray tracing, the integration of peripherals via software development kits, and the spatial and temporal calibration of an eye tracker embedded within a head-worn display. Students will complete homework tutorials on game/application development in a contemporary computer gaming engine. This course involves a substantial programming component, and prior programming experience is required. (This class is restricted to graduate students in the IMGS-MS or IMGS-PHD programs.) Lab 4, Lecture 1 (Fall).
Deep Learning for Vision
This course will review neural networks and related theory in machine learning that is needed to understand how deep learning algorithms work. The course will include the latest algorithms that use deep learning to solve problems in computer vision and machine perception, and students will read recent papers on these systems. Students will implement and evaluate one or more of these systems and apply them to problems that match their interests. Students are expected to have taken multiple computer programming courses and to be comfortable with linear algebra and calculus. No prior background in machine learning or pattern recognition is required. (This course is restricted to students with graduate standing in the College of Science or the Kate Gleason College of Engineering or Graduate Computing and Information Sciences.) Lecture 3 (Fall).
Philosophy of Mind
Philosophy of Action
Natural Language Processing I
This course provides theoretical foundation as well as hands-on (lab-style) practice in computational approaches for processing natural language text, for problems that involve natural language meaning and structure. The course has relevance to cognitive science, artificial intelligence, and science and technology fields. Machine learning, including standard and recent neural network methods, is a central component of this course. Students will develop natural language processing solutions individually or in teams using Python, and explore additional relevant tools. Expected: Programming skills, demonstrated by coursework or instructor approval. (This class is restricted to degree-seeking graduate students or those with permission from instructor.) Lecture 3 (Fall).
Natural Language Processing II
Study of a focus area of increased complexity in natural language processing. The focus varies each semester. Students will develop skills in computational linguistics analysis in a laboratory setting, according to professional standards. A research project plays a central role in the course. Students will engage with relevant research literature, research design and methodology, project development, and reporting in various formats. (Prerequisite: PSYC-681 or (IDAI-610 and IDAI-620) or equivalent courses.) Lecture 3 (Spring).
Graduate Speech Processing 
This course introduces students to speech and spoken language processing with a focus on real-world applications including automatic speech recognition, speech synthesis, and spoken dialog systems, as well as tasks such as emotion detection and speaker identification. Students will learn the fundamentals of signal processing for speech and explore the theoretical foundations of how human speech can be processed by computers. Students will then collect data and use existing toolkits to build their own speech recognition or speech synthesis system. This course provides theoretical foundation as well as hands-on laboratory practice. Expected: Programming skills, demonstrated by coursework or instructor approval. Lecture 3 (Fall).
Graduate Biopsychology
A graduate level introduction to the field of behavioral neuroscience, the study of neurobiological basis of cognition and behavior. Topics include neuroanatomy and physiology, localization of function, brain injury, research methods in behavioral neuroscience, and biological basis of learning, language, memory, emotion, conscious states, sexual behavior, etc. Lecture 3 (Spring).
Graduate Engineering Psychology
In this course the students will learn to recognize the integrated (systems) nature of Engineering Psychology, the centrality of human beings in systems design, and to use the topics covered and the available knowledge base to adapt the environment to people. This course will cover several fundamental models of human information processing in the context of human-system interactions. The models may include but are not limited to Signal Detection Theory, Information Theory, theories of attention, both normative and naturalistic decision-making models, Control Theory, and the Lens Model of Brunswick, as well as models of the human as a physical engine, that is, anthropometry, biomechanics, and work physiology. Most topics include readings in addition to the course text as well as a lab exercise with a detailed lab report. Seminar (Biannual).
Graduate Perception
The course is designed to provide students with a deeper understanding of topics in perception. This course will be organized such that students will work in groups on various projects as well as covering topics through readings and classroom discussion. The topics may include, but are not limited to: spatial frequency perception; aftereffects, visual illusions and their relationship to cortical function and pattern perception; color perception; depth and motion perception; higher order perception such as face and object recognition; and music and speech perception. The goal is to cover current research and theories in perception, looking at current developments and their antecedents. The course will be divided into various modules. Students will be assigned readings relevant to each section of the course, and will be expected to master the major concepts. Group discussion of the readings will complement lectures where the instructor will present relevant background material. There will also be laboratory time for the students, where they will examine empirical findings in perception, and develop their research skills in the field. Lecture 3 (Biannual).
Design of Experiments
How to design and analyze experiments, with an emphasis on applications in engineering and the physical sciences. Topics include the role of statistics in scientific experimentation; general principles of design, including randomization, replication, and blocking; replicated and unreplicated two-level factorial designs; two-level fractional-factorial designs; response surface designs. Lecture 3 (Fall, Spring).

Admissions and Financial Aid

This program is available on-campus only.

Offered Admit Term(s) Application Deadline STEM Designated
Full‑time Fall January 15 priority deadline; rolling thereafter Yes

Full-time study is 9+ semester credit hours. International students requiring a visa to study at the RIT Rochester campus must study full‑time.

Application Details

To be considered for admission to the Cognitive Science Ph.D. program, candidates must fulfill the following requirements:

  • Complete a graduate application.
  • Submit copies of official transcript(s) (in English) of all previously completed undergraduate and graduate course work, including any transfer credit earned.
  • Hold a baccalaureate degree (or US equivalent) from an accredited university or college. Since the program encompasses a wide variety of disciplines, students with diverse backgrounds are encouraged to apply.
  • A recommended minimum cumulative GPA of 3.0 (or equivalent).
  • Submit a current resume or curriculum vitae.
  • Submit a statement of purpose for research which will allow the Admissions Committee to learn the most about you as a prospective researcher.
  • Submit two letters of recommendation.
  • Entrance exam requirements: None
  • Submit one writing sample.
  • Submit English language test scores (TOEFL, IELTS, PTE Academic), if required. Details are below.

English Language Test Scores

International applicants whose native language is not English must submit one of the following official English language test scores. Some international applicants may be considered for an English test requirement waiver.

88 6.5 60

International students below the minimum requirement may be considered for conditional admission. Each program requires balanced sub-scores when determining an applicant’s need for additional English language courses.

How to Apply Start or Manage Your Application

Cost and Financial Aid

An RIT graduate degree is an investment with lifelong returns. Ph.D. students typically receive full tuition and an RIT Graduate Assistantship that will consist of a research assistantship (stipend) or a teaching assistantship (salary).