This track allows students to focus on knowledge and skills related to how technology is relevant to scientific investigation in the field of cognitive science.
Course options with computational/digital focus:
This course offers a comprehensive understanding of digital literacy in relation to teaching and researching language acquisition. Students will learn a variety of digital writing technologies and be trained to think critically about cultural and communicative consequences of the digital media. Students will also gain the critical perspective and literacy tools needed to actively apply in language teaching and researching.
CDT 23100 Learning, Design & Technology (ESS 23100 ) taught by G. Alex Ambrose (SPRING)
Technology has always been used for learning from the chalkboard in the one-room school to video lectures in massive open online courses. Regardless of time or place, the design of effective and innovative learning technologies must be grounded in research based evidence reflecting what is known about how people learn. Incorporating design, research, and field-based perspectives, students will be tasked with investigating current/emergent learning technologies and theories across a range of applied contexts in education, business, nonprofit, and government. This hybrid course involves an experiential/community-based learning component requiring students to devote one weekly two hour block of time to service in the local community. One face-to-face class meeting per week will be substituted with asynchronous interactions (i.e., online discussions and video lectures), independent/group studio time, and/or meetings with a community partner. No background in education or technology required. Course Goals: *Evaluate learning theories in terms of applicability to a technologically-enhanced learning environment. *Apply technologies to real world problems in terms of potential impact on learning *Explore the ethical, professional, and social challenges and controversies related to learning technologies (i.e., minors, privacy) *Integrate experiential and community based learning through the learning technology applications related to the coursework.
CDT 30140 Human Computer Interaction (PSY 40676/CSE 40424) taught by instructor TBD (SPRING)
An in-depth coverage of the field of Human-Computer Interaction (HCI) including its history, goals, principles, methodologies, successes, failures, open problems, and emerging areas. Topics include the fundamental principles of HCI (e.g., consistency, compatibility, pictorial realism), models of the human (e.g., perception, attention, memory, learning), interaction modalities and paradigms (e.g., windowing systems, haptic interactions), best-practice design principles (e.g., user-centered design, universal design, rapid application development), techniques to evaluate interfaces and interactions (e.g., observational methods, think-aloud protocols, cognitive walk throughs), and emerging topics in HCI (e.g., affective computing, augmented cognition, social computing, ubiquitous computing).
CDT 40310 Natural Language Processing taught by David Chiang (FALL)
Computers process massive amounts of information every day in the form of human language. Although they do not understand it, they can learn how to do things like answer questions about it, or translate it into other languages. This course is a systematic introduction to the ideas that form the foundation of current language technologies and research into future language technologies.
CDT 40510 Artificial Intelligence (PSY 40675/CSE 40171) taught by instructor TBD (NOT OFFERED AT THIS TIME)
A broad overview of the field of Artificial Intelligence (AI), including its historical and philosophical foundations, classical and contemporary approaches, cognitive systems, and recent trends and applications. Topics include traditional AI techniques (e.g., searching, problem solving, knowledge representation and reasoning, planning, constraint satisfaction, decision making), probabilistic and network based approaches (e.g., Bayesian models, neural networks), computational models of cognition (e.g., models of perception, action, memory, cognitive architectures), and recent developments in natural language processing, speech recognition, robotics, human-computer interaction, machine learning, and computational emotions.
Course options without computational/digital focus (only one is allowed):
In the last decade, the Big Data revolution and developments in Artificial Intelligence (AI) have both created promises and raised several ethical issues. Computational emerging technologies have fostered the achievement of apparent benefits, while at the same they seem to exacerbate social inequalities and threaten even our own existence as a species. In this course, we will discuss those ethical and societal issues related to the development of AI and Big Data that have direct and concrete consequences on the way we perceive ourselves as persons, as members of society, and the way we conceive our place as a species on this planet. These issues will be analyzed in light of major ethical theories, but a special emphasis will be placed on virtue ethics. Recent works in virtue ethics are well positioned to make sense of the importance of our place as human beings on this planet, but at the same time they can account for the indispensable roles that machines play in our environment. The course is divided in three main parts. In the first part, I will introduce the main ethical frameworks, and in particular virtue ethics. In the second part, we will discuss AI. Societal and ethical issues raised by AI include the threats posed to the existence of our species; whether we should trust AI or we should find a way to build artificial agents with moral characteristics; whether AI will do most of our jobs in the future and if this scenario is desirable. In the third part, we will focus on selected issues concerning the Big Data revolution, such as how the autonomy of very complex algorithms can shape our lives in opaque ways and whether transparency is desirable; if the design of algorithms may hide bias leading to social inequalities; how algorithms are changing the way healthcare is provided.Upon successful completion of this course, you will be able to: 1. Define and sketch focal points of the virtue ethics and other relevant ethical theories 2. Identify moral theories in arguments provided in support or in opposition to the use of certain AI-related and Big Data technologies 3. Compare different arguments and highlight strengths and weaknesses.
CDT 20520 - Section 01: Minds, Brains and Persons taught by Johnathan Baker (SPRING)
This course will treat some central issues in the philosophy of mind, such as freedom of the will, personal identity, and the relationship between mind and body.
Robots or "autonomous systems" play an ever-increasing role in many areas, from weapons systems and driverless cars to health care and consumer services. As a result, it is ever more important to ask whether it makes any sense to speak of such systems' behaving ethically and how we can build into their programming what some call "ethics modules." After a brief technical introduction to the field, this course will approach these questions through contemporary philosophical literature on robot ethics and through popular media, including science fiction text and video. This is an online course with required, regular class sessions each week. Class meetings are online via Zoom webinar software (provided by the University).
Note: this course is delivered fully online. The course design combines required live weekly meetings online with self-scheduled lectures, problems, assignments, and interactive learning materials. To participate, students will need to have a computer with webcam, reliable internet connection, and a quiet place to participate in live sessions. Students who will be on the Main campus or residing in the Michiana region are not eligible to enroll in this course.CDT 30370 Computational and Theological Models of the Human Person(THEO 20668) taught by Mark Graves (Fall)
How can one understand theological aspects of the human person using computational methods? Drawing upon neuroscience and the psychology of religion, one can model cognitive and linguistic aspects of human moral, religious, and spiritual development and exemplarity in ways amenable to computational analysis and simulation. The course will focus on using broadly applicable, semantic analysis techniques from artificial intelligence to extract meaning from classic and contemporary texts that are significant for theological anthropology, Christian spirituality, and moral theology. NOTE: There is an option for this class to have computational and digital focus. See course syllabus for details. NOTE: There will be an option for this course to have CDF depending on the final project. Otherwise it will not. See syllabus for details.
The brain gives rise to all thoughts, feelings, learning- much of what we study in the field of psychology. In this course, you will learn the basics of how the brain works. Topics covered will include: how neurons transmit signals; basic neuroanatomy (functions of different parts of the brain); the neural basis of sensory processes, such as vision, hearing, smell and taste; movement and autonomic functions; motivations, such as hunger and thirst; emotions and stress; and cognitive functions such as learning, memory, and language. Examples and evidence will come from studies of brain-damaged human patients as well as animal neuroscience research. The evolution of the human brain and comparison to other species' brains will also be considered.Prerequisites: Introductory psychology. Some biology coursework will also be helpful, but not required.
CDT 30540 Cognitive Psychology taught by Nate Rose (FALL)
A lecture course presenting a cognitive approach to higher processes such as memory, problem solving, learning, concept formation, and language.
What are mental phenomena? What is their place in the world? Are they identical with physical phenomena? Are they distinct from physical phenomena, yet dependent on them in some form? Or are the two classes of phenomena entirely independent of each other. Are there causal relations between mental and physical phenomena? And if so, what must the nature of mental and physical phenomena be to allow for this possibility? Is there a principled distinction between experiential and representational phenomena? Or is this a merely superficial distinction? These are some of the big questions that arise when we ask what the nature of mind is, when we raise questions about the metaphysics of Mind.