The MS in Human Factors and Cognition prepares you to design smarter, safer, and more intuitive systems by understanding how people think, perceive, and interact with technology. Whether your background is in psychology, design, engineering, or computer science, this program empowers you to bridge the gap between human behavior and technological innovation.
Learn to apply cognitive and behavioral science to real-world challenges, evaluating how humans interact with complex systems and creating products, technologies, and environments that truly serve their users.
Earn two degrees in just five years. This master’s program can be combined with the following undergraduate degrees:
If you are interested in earning a 4+1 degree but your degree is not listed here, contact your academic advisor.
Course Name
Course #
Credits
This course provides a comprehensive survey of the field of human factors, emphasizing the theoretical foundations, empirical methods, and applied challenges involved in optimizing human performance and well-being in complex sociotechnical systems. Students will critically examine topics including perception and cognition, decision making, human error, workload, user-centered design, human-computer interaction, and system safety. Through an integration of classic literature, contemporary research, and real-world case studies, the course prepares students to evaluate and contribute to cutting-edge research in human factors across diverse domains such as healthcare, aviation, transportation, and automation. Emphasis is placed on rigorous methodological training, interdisciplinary synthesis, and scholarly communication through presentations, research proposals, and peer-reviewed writing.
PSY 5423
3
Cognitive Psychology
PSY 5213
3
Elective
XXX3
3
Total Credits:
9
Course Name
Course #
Credits
This course provides a comprehensive examination of the core domains of cognitive psychology, including perception, attention, memory, language, decision making, problem solving, and learning. The course emphasizes theoretical frameworks that characterize cognition in terms of knowledge representation and information processing, drawing on both classical models and contemporary developments in computational and neural approaches. Students will critically engage with foundational and current research to understand how cognitive functions are instantiated, measured, and modeled. Attention is also given to the application of cognitive principles in real-world contexts, including human-machine interaction, applied cognition, and the integration of human cognition with artificial systems. Through readings, discussions, written critiques, and independent projects, students will gain the theoretical and methodological expertise necessary to contribute to the advancement of cognitive science.
PSY 5843
3
This course explores the interdependent processes through which organisms acquire, interpret, and respond to information from the environment. Covering key topics in visual, auditory, tactile, and proprioceptive perception, the course examines how sensory information guides motor planning, coordination, and control. Emphasis is placed on theoretical models that link perceptual processing to action, including ecological, computational, and embodied cognition perspectives. Students will engage with current empirical research addressing topics such as sensorimotor integration, perception-action coupling, affordances, and motor learning, as well as applications in robotics, virtual environments, rehabilitation, and human-machine interaction. Through critical analysis, research proposals, and interdisciplinary synthesis, students will develop a deep understanding of how perception and action are jointly organized in adaptive behavior.
PSY 5413
3
Elective
XXX3
3
Total Credits:
9
Course Name
Course #
Credits
Research Project I
5913
3
Elective
XXX3
3
Total Credits:
6
Course Name
Course #
Credits
Research Project II
5923
3
Elective
XXX3
3
Total Credits:
6
Course Name
Course #
Credits
Course not found.
PSY 5743
3
Course not found.
PSY 5713
3
This course focuses on leadership skills specific to the allied disciplines of design within the College of Architecture and Design. Students will engage models of leadership relevant to a dynamic and evolving professional context. Course content will include typologies of leadership organization, principles of successful teamwork and collaboration, viable economic frameworks, ethics and design entrepreneurship.
DES5112
2
This course addresses issues of data visualization and visual rhetoric for design project positioning. Focus will be on current innovation spaces in the visual realm stressing digital tools and processes while reinforcing and extending fundamental principles of graphic narrative, semiotic systems of meaning, and cultural nuances in visual interpretation.
DES5113
3
An advanced exploration into generative design methods including a deeper exploration and use of coherence systems, abstraction and knowledge transfer processes. The course focuses on force-based processes, pattern applications, concept alignments, rapid feedback loops, model-based inquiry, pre-relevance explorations, and material driven processes. Particular focus will be on technique and conceptual tool acquisition.
DES5413
3
Course not found.
EME5983
3
This course introduces a machine learning technique called deep learning and its Electrical Engineering applications, as well as core machine learning concepts such as data set, evaluation, overfitting, regularization and more. Topics in: Real-time decisions in autonomous vehicles, warning systems, radar, LiDAR sensor fusion. Covers neural network building blocks: linear and logistic regression, followed by shallow artificial neural networks and a variety of deep networks algorithms and their derivations. Including interactive programming with industrial automation hardware and software. No credit after MCS 5713 Deep Learning and Neural Networks.
EEE5253
3
This course focuses on writing maintainable and extensible engineering systems code development. Topics include: smart software encryption and cybersecurity development in autonomous vehicles, physical systems semantic networks, frames, pattern matching, deductive inference rules, case-based reasoning, and discrimination trees. Project-driven. Substantial programming assignments. Including interactive programming with industrial automation hardware and software.
EEE5513
3
Course not found.
EEE5653
3
The objective of the course is to study, understand, and practice the concepts of machine learning and pattern recognition. The course will cover the basic aspects of pattern recognition and machine learning such as different approaches to feature selection, classification methods, interpolation methods, and techniques of machine learning performance evaluation. In the end of the course the students will be able to implement all aspects of pattern recognition to create a working machine learning system that will solve a real-life pattern recognition problem.
MCS5623
3
An introduction to computational scene understanding focused on computer vision. Students will study the acquisition and processing of camera data to describe the surrounding environment, using tools and methods now being deployed in industry. Included topics to range from basic image processing techniques such as camera models, object detection, and filtering to higher level scene understanding methods using neural networks to perform multi-view reconstruction, motion/tracking, and recognition.
MCS5623
3
Security measures are associated with various types of computing systems. An introduction to network security fundamentals, including compliance and operational security; threats and vulnerabilities; application, data, and host security; access control and identity management; and cryptography. New topics in network security, including psychological approaches to social engineering attacks, web application attacks, penetration testing, data loss prevention, cloud computing security, and application programming security. It is recommended, but not required, that MCS3663, MCS4613, and MCS4653 is taken prior to taking MCS5813.
MCS5813
3
As networks continue to grow and as computing becomes more and more ubiquitous, today’s IT Managers need to have a thorough understanding of security and the risks associated when inappropriate security exists. Students will explore basic security concepts, principles and strategy, how to develop and manage IT security program and how to strategize and plan an IT architecture. Students will also discuss other IT security issues as it relates to current market trends.
INT7223
3
Minimum requirements for admission to the MS in Human Factors and Cognition program are as follows:
Applications for the LTU Human Factors & Cognition M.S. program can be submitted at any time. However, applicant review will begin on March 1 for students interested in Fall 2026 enrollment. Applications will still be accepted after this date, but those received by March 1 will receive priority for available space. Please contact the HFC program director for questions.
The Master of Science in Human Factors and Cognition (MSHFC) is a 30-credit-hour graduate program designed to provide advanced knowledge in understanding human cognitive and perceptual capabilities and limitations. The program emphasizes applying this understanding to the design and evaluation of systems, products, technologies, and environments to optimize human performance and well-being. By blending principles of cognitive psychology with applied science, the program prepares students for professional roles in user experience (UX) research, usability engineering, and human-centered technology design. It is currently the only program in Michigan’s lower peninsula designed to meet the accreditation standards of the Human Factors and Ergonomics Society (HFES).
MSHFC PROGRAM OBJECTIVES
The program is designed to develop professionals with a rigorous scientific approach to human-centered design. It is intended to draw students from diverse backgrounds, including:
College graduates seeking to specialize in human-centered approaches to technology.
Design and technology professionals interested in the empirical research methods and psychological principles underlying effective interface design.
Highly motivated students seeking an accelerated 4+1 pathway to earn both a bachelor’s and master’s degree in five years.
MSHFC ADMISSION REQUIREMENTS
Submission of the Application for Graduate Admission (ltu.edu/apply) with a current resume.
A bachelor’s degree in a relevant field (e.g., Psychology, Engineering, IT, Design, or related STEM/social science) from a regionally accredited institution.
Minimum undergraduate GPA of 3.0 on a 4.0 scale.
Official transcripts of all completed college work.
Three letters of academic or professional recommendation.
A written statement of purpose outlining research interests and career goals aligned with human factors.
Documentation of English proficiency for non-native speakers.
MSHFC TRANSFER CREDIT POLICY
A maximum of six graduate semester credit hours may be transferred from an accredited graduate program, subject to approval by the program faculty. Transferred courses must have a grade of B or higher.
MSHFC CURRICULUM
TOTAL CREDIT HOURS: 30
Students must have a plan of study arranged in consultation with an advisor and approved by the program director. The program typically requires two years of Fall/Spring study. Under the accelerated 4+1 option, undergraduate students may complete up to 12 graduate credits during their junior and senior year.
1. Core Courses (12 credits, four courses)
PSY 5213 Cognitive Psychology
PSY 5413 Sensation and Perception
PSY 5423 Human Factors
PSY 5843 Research Design and Statistics
2. Graduate Electives (12 credits, four courses)
Specialized Psychology Electives:
PSY 5713 Human-Computer Interaction
PSY 5743 Psychophysiology for Human Centered Computing
Interdisciplinary Electives (with advisor approval):
Relevant 5000- or 6000-level courses may be selected from other departments to satisfy student-directed interests.
3. Applied Research Project or Master’s Thesis (6 credits, two courses) Students must gain practical experience applying human factors methodology to real-world problems through a two-course sequence:
PSY 5913 Research Project I
PSY 5923 Research Project II
These requirements may be satisfied via a thesis project or work-based research project, with approval of the program faculty