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 six colleges include: College of Liberal Arts; College of Science; Golisano College of Computing and Information Sciences; Kate Gleason College of Engineering; College of Engineering Technology; and National Technical Institute for the Deaf.
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Apply early for priority consideration for admission and financial aid.
Applications are accepted after the deadline, but are only considered on a space-available basis.
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Research
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.
Featured Work and Profiles
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![Sophia Caruana]()
Student Finds Her Niche in New Cognitive Science Ph.D. Program
Sophia Caruana RIT’s new cognitive science Ph.D. program is attracting students such as Sophia Caruana, who is eager to explore data ethics, AI, and human-centered computing in a collaborative, multifaceted...
Read More about Student Finds Her Niche in New Cognitive Science Ph.D. Program
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November 25, 2024
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A new National Science Foundation grant will allow 18 RIT students to travel to Sweden and conduct artificial intelligence (AI) research that enhances the industrial workplace. The students will take part in a program of AI research, professional development, and mentorship, which includes eight weeks at University West, near Gothenburg, Sweden.
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August 20, 2024
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New Ph.D. programs welcome students this fall
The university’s two newest Ph.D. programs in cognitive science and physics offer diverse research opportunities for students and help them gain the skills and abilities needed to analyze data and grasp complex concepts. The new programs bring RIT’s total doctoral programs to 15.
Curriculum for 2024-2025 for Cognitive Science Ph.D.
Current Students: See Curriculum Requirements
Cognitive Science, Ph.D. degree, typical course sequence
| Course | Sem. Cr. Hrs. | |
|---|---|---|
| First Year | ||
| COGS-600 | Foundations in Research This introduction to research comprises two parts. The first part introduces interdisciplinary cognitive science and its impact in society together with foundational notions about the research process (including responsible conduct of research) and publication practices and grant writing. The second part provides an entry point to later methods courses by establishing shared computational foundations. Lecture 3 (Fall). |
3 |
| COGS-801 | Cognitive Science Research Colloquium The seminar will meet weekly every semester for 12 times (excluding the first and last weeks of the semester). Each week will feature a different presenter. The presenters will include speakers invited to RIT and RIT faculty members who are active in research relevant to Cognitive Science, as well as the students in the Cognitive Science PhD program when they have progressed to the level that they will have worthwhile research to present to their classmates and the program faculty. Seminar 1 (Fall, Spring). |
0 |
| COGS-610 | Laboratory Methods Scientists use a wide range of experimental methods to elucidate the function of the human brain and mind. This course will provide an overview of these methods, in order to allow students to understand a wide range of scientific studies and to be able to select an appropriate method for a specific research topic. Such methods include neuroimaging, psychophysiology, single-cell recordings, computational modeling, and cognitive psychology and behavioral methods that use measures such as response time and decision accuracy to test theories concerning the nature of mental processes and representations. Lecture 3 (Fall). |
3 |
| COGS-711 | Philosophical Foundations in Cognitive Science This course will introduce students to the philosophical foundations of cognitive science. Topics will include the nature and distribution of consciousness, including cognitive, neurobiological, and informational theories; theories of cognition, including computational-representational and non-representational “EEE” (embodied/emergent/enactive) theories; theories of emotion, affect, valence, and motivation; theories of action and agency, and evolutionary theory. All of these discussions will be cutting-edge research in human and nonhuman animal cognition. The class will also include a discussion of competing conceptual, inferential, and conceptual strategies across the disciplines that comprise cognitive science. (Prerequisites: COGS-600 or equivalent course.) Lecture 3 . |
3 |
| PSYC-640 | 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). |
3 |
| PSYC-712 | 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). |
3 |
Electives |
3 | |
| Second Year | ||
| COGS-801 | Cognitive Science Research Colloquium The seminar will meet weekly every semester for 12 times (excluding the first and last weeks of the semester). Each week will feature a different presenter. The presenters will include speakers invited to RIT and RIT faculty members who are active in research relevant to Cognitive Science, as well as the students in the Cognitive Science PhD program when they have progressed to the level that they will have worthwhile research to present to their classmates and the program faculty. Seminar 1 (Fall, Spring). |
0 |
| COGS-720 | Teaching Practicum In addition to research and research leadership, doctoral education also prepares students for teaching in their area in higher education academic environments. This course is for students with at least one year of experience in the Cognitive Science PhD program. The course will develop knowledge and practical skills for teaching and assessment, and discuss related topics such as diversity and inclusion, cognitive science education research, and evaluation of teaching. Students will develop a portfolio of teaching-related materials focused on their discipline. (6Prerequisites: COGS-600 and (PSYC-640 or PSYC-717 or COGS-610) or equivalent courses.) Lecture 3 (Spring). |
3 |
| COGS-621 | Foundations of Scientific Computing This course will introduce students to foundational concepts in numerical computation that are useful for engineering and the mathematical, computational, and physical sciences. Topics will include floating-point arithmetic, error analysis, linear and nonlinear equations, numerical solution of systems of algebraic equations, constrained and unconstrained optimization, polynomial interpolation, numerical differentiation and integration, numerical solution of ordinary differential equations, truncation error, and basic methods for sampling stochastic processes. Implementation of various numerical methods and solvers will be done in Python, MATLAB, and R. Connections to computational modeling of cognition will be made throughout the course as motivating examples for various key concepts and tools. (Prerequisites: COGS-600 and (PSYC-640 or PSYC-717) or equivalent courses.) Lecture 3 (Fall). |
3 |
| PSYC-717 | 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). |
3 |
Electives |
9 | |
| Third Year | ||
| COGS-801 | Cognitive Science Research Colloquium The seminar will meet weekly every semester for 12 times (excluding the first and last weeks of the semester). Each week will feature a different presenter. The presenters will include speakers invited to RIT and RIT faculty members who are active in research relevant to Cognitive Science, as well as the students in the Cognitive Science PhD program when they have progressed to the level that they will have worthwhile research to present to their classmates and the program faculty. Seminar 1 (Fall, Spring). |
0 |
| COGS-899 | Cognitive Science Dissertation Research This course is to fulfill the work plan agreed by the student and the dissertation adviser. The guiding principle of the Dissertation Research course is to complete the doctoral dissertation research proposed by the doctoral candidate and approved by the candidate’s dissertation committee. The course consists of carrying out the thesis research, including collection and analysis of data, and completion and public defense of the dissertation document for partial fulfillment of the requirements of the PhD degree in Cognitive Science. Research 3 (Fall, Spring). |
12 |
| FourthYear | ||
| COGS-801 | Dissertation and Research The seminar will meet weekly every semester for 12 times (excluding the first and last weeks of the semester). Each week will feature a different presenter. The presenters will include speakers invited to RIT and RIT faculty members who are active in research relevant to Cognitive Science, as well as the students in the Cognitive Science PhD program when they have progressed to the level that they will have worthwhile research to present to their classmates and the program faculty. Seminar 1 (Fall, Spring). |
0 |
| COGS-899 | Cognitive Science Dissertation Research This course is to fulfill the work plan agreed by the student and the dissertation adviser. The guiding principle of the Dissertation Research course is to complete the doctoral dissertation research proposed by the doctoral candidate and approved by the candidate’s dissertation committee. The course consists of carrying out the thesis research, including collection and analysis of data, and completion and public defense of the dissertation document for partial fulfillment of the requirements of the PhD degree in Cognitive Science. Research 3 (Fall, Spring). |
12 |
| Total Semester Credit Hours | 60 |
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Program Electives
| Course | |
|---|---|
| CGNS-601 | 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). |
| CGNS-710 | 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). |
| CLRS-820 | 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). |
| CMPE-677 | 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). |
| CMPE-765 | 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). |
| COGS-760 | Foundations of Cognitive Modeling This course will introduce students to the mathematical and philosophical foundations of cognitive modeling as well as the key concepts and tools needed for developing and applying cognitive architectures. Furthermore, the course will survey seminal papers as well as leading computational frameworks used in understanding human cognition and intelligence.Topics will include fundamentals of signal detection theory, probability modeling and information theory, the Lens Model, statistical (Bayesian) modeling of various cognitive actions and behavior, dynamical systems, symbolic and sub-symbolic representations, and simulation using artificial neural networks. Students will learn how to use one or more major cognitive architectures, e.g., MicroSAINT, Act-R, Soar, Nengo, and build basic computational models of cognitive processes, including those related to categorization, language, memory, decision making, and reasoning, fitting and evaluating their models to different kinds of behavioral data. Lecture 3 (Biannual). |
| COGS-761 | Graduate Psycholinguistics This graduate seminar gives an advanced introduction to psycholinguistics which investigates the cognitive mechanism that allows humans to produce, comprehend and learn language. It discusses some of the central topics in the field, including (1) an historical overview of the field; (2) a survey of behavioral, neurological, and computational methodologies; (3) a review of empirical results and theoretical perspectives; (4) a roadmap for future directions that are likely to advance the field. Seminar 3 (Biannual). |
| COGS-762 | Animal Cognition This course draws on latest findings in comparative cognition sciences, broadly construed, to examine fundamental questions about the evolution of mind on Earth and what that can tell us about the nature of mind wherever it is found—whether in human beings, nonhuman animals, or machines. Lec/Lab 3 (Biannual). |
| COGS-765 | Psycholinguistics of Signed Languages The study of how humans comprehend and produce language has traditionally been informed by research into spoken language processing. In this course, students will explore the utility of psycholinguistic theories for understanding the processing of signed languages, with a focus on what can be learned about the human capacity for language by also considering the modality in which a language is expressed. The focus will be on the perception and production of signed language, from infancy to adulthood. The course will cover empirical and theoretical discussions of the concept of critical periods, age of acquisition effects, delayed first language learning and second-language learning. Finally, the course will consider how language oppression and socio- cultural factors have resulted in large variability in how signed languages are processed. Lecture 3 (Biannual). |
| COGS-766 | Neuroplasticity in Deaf and Blind Individuals This graduate seminar focuses upon understanding the human brain as a multisensory processor that integrates information from across multiple modalities in order to make flexible and adaptive decisions. Through readings and class discussion we will explore what studies of sensory processing in deaf and blind individuals reveal about the nature of the multisensory brain, and the implications for understanding how deaf and blind people successfully navigate the world. Lecture 3 (Biannual). |
| COGS-768 | Deaf Vision How do linguistic and sensory experiences during development impact how we use language and think? Through the lens of deafness and sign language, it becomes possible to understand the ubiquitous nature of human language and language processing, the relation between cognition and language, and the neural organization of language. By comparing perception, language processing, and cognition for two different modalities, visual-manual versus auditory-spoken, we are able to ask, how does our language impact the way our brains are organized, how our brains function, and how we perceive the world? This class aims to provide answers to these questions with a theoretical and empirical cognitive science approach. The study of deafness and sign language allows cognitive scientists to address questions about the nature of linguistic and cognitive systems that otherwise could not be easily addressed. Students will learn about general theories of multisensory integration, brain function, visual perception, systems of visual attention, and executive function. Although research present is within the bulwark of cognitive science, such knowledge has implications for application within clinical and educational settings. Lecture 3 (Biannual). |
| CSCI-630 | 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). |
| CSCI-631 | 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). |
| CSCI-633 | 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). |
| CSCI-635 | 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). |
| CSCI-736 | 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). |
| IMGS-620 | 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). |
| IMGS-624 | 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). |
| IMGS-684 | 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). |
| PHIL-604 | Philosophy of Mind Philosophy of mind is the philosophical discipline that explores what a mind is and how it fits in the natural world. In doing this, philosophy of mind raises further questions such as: What do we mean by mind? How do we attribute mentality? How are mental and physical properties related? What is consciousness? Can computers think? How is rationality connected to mental states like beliefs and desires? In this course we discuss and critically assess answers to these and related philosophical questions. This course is designed for both undergraduate and graduate students. Graduate students will fulfill additional requirements beyond those expected for undergraduate students. (Prerequisites: Graduate standing and (1) one prior course in philosophy.) Lecture 3 (Fall, Spring). |
| PHIL-790 | Philosophy of Action This course explores the three central philosophical issues of action theory: what is an action, what is an agent, and what is metaphysical freedom. The first part of the course examines the most significant theories of action and the different ways in which they characterize intentional behavior. The second part of this course explores the nature of agency. The third part of this course focuses on the classical problem of free will and its relation to moral responsibility. Lecture 3 (Biannual). |
| PSYC-681 | 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). |
| PSYC-682 | 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). |
| PSYC-684 | 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). |
| PSYC-711 | 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). |
| PSYC-714 | 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). |
| PSYC-715 | 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). |
| STAT-670 | 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:
- Learn tips to apply for a doctoral program and then 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 minimum cumulative GPA of 3.0 (or equivalent) is recommended.
- 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 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.
| TOEFL | IELTS | PTE Academic |
|---|---|---|
| 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).
Contact
Admissions Contact
- Laura Watts
- Senior Associate Director
- Office of Graduate and Part-Time Enrollment Services
- Enrollment Management
- 585‑475‑4901
- Laura.Watts@rit.edu
Program Contact
- Matthew Dye
- Graduate Program Director, Cognitive Science
- NTID SPACE Center
- National Technical Institute for the Deaf
- 585‑475‑2252
- mwddls@rit.edu