Cognitive Science Program

MS in Computational Cognitive Science

The MS in Computational Cognitive Science is a 30-credit, course-based, non-thesis program.  You'll culminate the program by completing a required experiential learning component (a co-op, internship, or other approved experience), which will provide you with a structured opportunity to apply the knowledge and skills you've developed in a real-world setting.

This program is designed for students with undergraduate preparation in cognitive science, psychology, neuroscience, computer science, engineering, or related fields who seek a master’s program that integrates cognitive theory, experimental methods, and computational modeling. The interdisciplinary program is structured as a cross-school initiative, drawing on instructional strengths in the School of Arts and Sciences and the School of Engineering.  

Program Requirements and Policies

Total credits required: 30
Duration: 15 months
Thesis: None
Experiential Learning: a co-op, internship, or other program-approved experience

Course Requirements

The core curriculum builds a working understanding of how people actually make decisions, and includes a required ethics course addressing accountability, interpretability, and responsible deployment of AI and data systems. Coursework covers dual-process reasoning, the heuristics behind everyday judgment, and why cognitive errors are diagnostic of underlying mental structure, giving graduates the reasoning they'll use on the job to explain why users trust, distrust, or misuse a given system, along with the framework to evaluate whether that system was built and deployed responsibly.

Core Courses

1. Introduction - Introduction to Cognition (course number TBD)

2. Cognition - PSY 245 Mental Representation

3. Modeling (choose one)

  • PSY 140/240 · Probabilistic Models of Perception and Cognition
  • CS 134/PSY 141 · Computational Models in Cognitive Science

4. Experimentation - CS 179 Experimental Methods for Engineers

5. Analytics - DATA 201 Data Analytics and Machine Learning with Python 

6. Ethics (choose one)

  • AI 274 - AI Ethics, Policy, and Responsible Use
  • PHIL 110 -  Philosophy of AI

Electives

Statistics

CSHD 140 Introductory Statistics for Developmental Science

CSHD 146 Intermediate Statistics for Developmental Science

CSHD 252 Structural Equation Modeling in Developmental Science

CSHD 254 Multilevel Modeling in Developmental Science

Research Methods

ENP 161 Human Factors in Product Design

PSY 262 Rigorous & Reproducible Research Practices

CSHD 0144 Qualitative And Ethnographic Methods In Applied Social Science Research

CSHD 247 Program Evaluation

CSHD 262 Cultural Sensitivity In Child And Family Research/practice

Cognition

ANTH 189 How to Pay Attention

Children

CSHD 0120 Assessment of Children

CSHD 0145 Technological Tools For Learning

CSHD 151 Advanced Intellectual Development Of Young Children

Education

ED 0111/112/119

ED 110/113

ED 114 Linguistic Approaches to Second-language Acquisition

ED 0130 Human Development and Learning

Language

PSY 212 Human Communication

CSHD 195 Developmental Disorders In Language And Reading

ANTH 137 Language & Culture

Other

CS 150-AAA Artificial Agents & Auto

CS 150-HFD HCI for Disability

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