The Master of Science in Applied Cognition and Neuroscience degree (ACN) provides students with advanced training that incorporates methodologies and approaches from such diverse fields as neuroscience, experimental psychology, artificial intelligence, and human-computer interactions. In addition to preparing students for doctoral study in these areas, the scope and multidisciplinary nature of the degree make it relevant to students seeking careers in software development, engineering, education, psychology, and medical professions.
Students earning a master’s degree in applied cognition and neuroscience specialize in one of six areas: neuroscience, cognition, cognition and neuroscience, human-computer interactions, computational modeling/intelligent systems, and neurological diagnosis and monitoring.
The degree requires 36 graduate credit hours: six hours of core courses in the student’s area of specialization, six hours of research methods courses in the area of specialization, six hours of advanced elective courses in the area of specialization, 12 hours in one of the other specialization areas, and six credit hours of an industry internship, a research internship, or supervised research in behavioral and brain sciences.
Many courses in the applied cognition and neuroscience program are offered periodically as evening courses to meet the needs of busy professionals’ schedules, and part-time students are welcome.
The UT Dallas graduate catalog provides more information on the Master of Science in Applied Cognition and Neuroscience degree program. The university’s course look-up site, CourseBook, describes specific courses in the ACN program.
The Master of Science in Applied Cognition and Neuroscience degree provides students with coursework and training that span the fields of neuroscience, experimental psychology, artificial intelligence, and human-computer interactions. As such, a wide variety of career options are available to students on graduations, including doctoral study in these and related areas as well as professional careers in engineering, medical, educational and other settings. Additional information about career opportunities in the six specialization areas of the master’s degree is available on the websites of related ACN Organizations.
Admission to the Applied Cognition and Neuroscience Master’s degree program is based on a review of the applicant’s grade point average (GPA), scores on the Graduate Record Exam (GRE), three letters of recommendation and a “statement of purpose” essay describing the applicant’s specific interests and career goals. See the UT Dallas graduate admissions page for details on the application process.
To be accepted for the fall semester, completed applications are due by May 1. To be accepted for the spring semester, completed applications are due by October 15 and to be accepted for the summer semester, completed applications are due by March 1.
Specific questions about applying to the applied cognition and neuroscience master’s degree program should be directed to Melanie Davis.
General information on financial aid for graduate students is available via the UT Dallas graduate admissions page. Financial aid for master’s students in the ACN program is limited and is awarded on a competitive basis after students have been admitted to the program. Please contact the Program Head for more information about funding opportunities for master’s students in the ACN program.
Applied Cognition and Neuroscience Master’s Degree Program
ATTN Melanie Davis
School of Behavioral and Brain Sciences
The University of Texas at Dallas
800 W. Campbell Rd, GR41
Richardson, TX 75080
Introduction to the ACN Program
This video presentation provides students with an overview of the Master of Science in Applied Cognition and Neuroscience degree.
American Society of Electroneurodiagnostic Technologists
American Society of Neurophysiological Monitoring
International Organization of Societies for Electrophysiological Technology
American Clinical Neurophysiology Society
Frequently Asked Questions About Neurological Diagnosis and Monitoring