Graduate Program

PhD Program

Because students come to the brain and cognitive sciences (BCS) discipline with a wide range of backgrounds, the PhD program is designed to introduce students to parts of the field they might not previously have studied, and to prepare them for advanced work.

This core curriculum covers a range of topics in perception, action, cognition, language, learning, and development, each examined from the perspectives of behavioral, computational, and neural science.

The methods students master for approaching their own research may vary. However, as preparation for entering a highly interdisciplinary field, all students must acquire some expertise in at least two approaches. Students also take advanced courses and seminars in one or more areas of specialization. At all stages of their graduate careers, students are heavily engaged in research.

Program Overview

Generally students complete most of their course work during the first two years. During the third year students take a qualifying exam covering the scholarly literature surrounding their area of specialization, and thereafter typically devote themselves fully to their research. The PhD is awarded upon the completion of a dissertation containing original research in the field.

These requirements are summarized below:

  1. Core courses—four of the following, with at least one from each pair:
    1. Language/cognition
      1. BCS 501: Language
      2. BCS 502: Cognition
    2. Sensory systems/perception and action
      1. BCS 504: Sensory Systems
      2. BCS 505: Perception and Motor Systems
    3. Cellular or systems neuroscience/cognitive neuroscience
      1. BCS 508: Cognitive Neuroscience
      2. NSC 531: Integrative Neuroscience
      3. NSC 512: Cellular Neuroscience
  2. Experimental design and statistics:
    1. Students seeking a graduate-level statistics course focusing particularly on ANOVA should take BST 464; we suggest students sit in on STT 212 if they have no previous background in statistics
    2. Students who want a graduate-level course covering ANOVA and extensive linear regression should take BST 464 and CSP 519 or STT 422 and STT 441
    3. Students with a strong math background who want a theory-based introduction to probability should take STT 203
  3. Training in at least two research methodologies*, selected from the following:
    1. Behavioral science
    2. Computational modeling
    3. Introduction to fMRI: imaging, computational analysis and neural representation
    4. Neuroscience
  4. Supervised research throughout the program
  5. Advanced courses as needed to achieve scholarly background and specialized expertise
  6. Qualifying exam in area of specialization
  7. Supervised experience as teachers or teaching assistants
  8. Doctoral dissertation

*Expertise in a methodology is achieved by taking BCS methods courses, or by petitioning to satisfy the requirement through research experience.

For general information about graduate studies at the University of Rochester, and for descriptions of all graduate course offerings at the University, see the graduate studies bulletin. For more specific information about some of these requirements see the current students page