Psychology

This course introduces and discusses recent theories and studies on the linguistic information processing process from the perspectives of cognitive psychology, linguistics, and artificial intelligence. Students examine the characteristics of language information processing, acquiring effective neuroscience-based learning principles to overcome difficulties in foreign language acquisition, and the specific features of Korean language processing. 

Topics include Introduction to language, Speech production and comprehension, Word processing, Semantic processing, Sentence processing, Discourse/dialogue, Language development in infancy and early childhood, Bilingual language processing, Aphasia, Korean language processing: lexical processing, sentence processing, discourse. 

This course provides students with an opportunity to become a sophisticated, critical, and creative user of generative artificial intelligence (GenAI).  

Through this course students gain a practical mastery of current AI tools, but are also challenged and prepared to move beyond basic AI use to develop skills in prompt engineering, tool comparison, and critical output evaluation and to design and implement effective AI-powered workflows to solve complex academic and professional tasks related to research, writing, data analysis, and communication. 

Students also critically analyze the ethical responsibilities of AI use (bias, privacy, integrity) and articulate the broader philosophical implications for your work, your mind, and your identity.  

Topics include Introduction to the course's Syllabus and lab-based philosophy; What is Generative AI?; Understanding our own "mental models" of AI; The principles of effective prompt engineering; The landscape of major LLMs (open vs. closed source); Retrieval-Augmented Generation (RAG) as a tool against hallucination; Overview of specialized AI tools for academic reading and writing; AI capabilities beyond text: Vision, Voice, and Code; Integrating multiple AI tools into a single workflow; Understanding AI "agents," APIs, and the role of local LLMs; The FOCUS Method for AI-assisted research; Finding and organizing information effectively; AI as a writing partner and coding assistant; Ethical considerations in AI-assisted writing; Designing AI-powered workflows for personal productivity, email management, and lifelong learning; Key limitations of AI (bias, privacy, hallucinations); Principles of ethical AI use; University policies on academic integrity; The broader societal impact of AI on science, equity, and the future of work; and The nature of intelligence, creativity, and consciousness in the age of AI. 

This course examines major systems of the brain from the perspective of clinical neuroscience and covers behavior, cognition, emotion and development. It looks at theoretical models of the aetiology and neural mechanisms of clinical pathologies (such as anxiety, depression, psychosis), as well as considers wellbeing and cognition, and the research evidence supporting them. Research methods in clinical and cognitive neuroscience, including experimental, analogue, genetic, imaging, longitudinal and epidemiological studies will be covered to outline the strengths and limitations of these techniques. 

This course introduces students to key topics, theories and methods in the field of social psychology. Social psychology is the scientific study of how individuals’ thoughts, feelings and behaviors are influenced by the actual or imagined presence of other people. This course covers such topics as attitudes, social influence, groups, prejudice, attraction, gender and altruism. The course critically evaluates seminal and contemporary studies in social psychology and considers the insights they offer into the psychological processes that underlie human relationships, culture and society.
 

This course discusses psychological and neuroscientific studies on visual awareness and voluntary actions. It selects and critically assesses influential publications in this field and discuss their wider implications.

This course provides an overview of the challenges and opportunities for research psychologists with the growing development of social robotics. This is achieved by examining the state of the art in this domain, investigating social robotics use in clinical disorders, and exploring different areas where social robotics research holds potential to inform our understanding of human cognition and behavior.

This is a research project carried out under the guidance of a supervisor at the Simons Initiative for the Developing Brain (SIDB) at the University of Edinburgh.

This is an independent research course with research arranged between the student and faculty member. The specific research topics vary each term and are described on a special project form for each student. A substantial paper is required. The number of units varies with the student’s project, contact hours, and method of assessment, as defined on the student’s special study project form.

The field of computational psychiatry has taken off over the last decade. Research in this field uses computational modeling to identify the precise component mechanisms underlying deficits and biases in learning, decision-making and other cognitive processes. The first part of this course reviews some of the early classic papers in this new field illustrating how this approach has been used to advance understanding of psychiatric disorders ranging from anxiety and depression to addiction and schizophrenia. Each week, one or two papers are set in advance, presented using a lecture format, and discussed via class participation. These papers are selected to present some of the most widely used theoretical frameworks and experimental tasks. In the second part of the course, students are introduced to current issues in advancing the nosology of psychiatric disorders. This covers why the field has become unhappy with traditional binary diagnostic categories and alternate approaches advanced to address this, including NIMH’s Research Domain Criteria (RDoC) framework for investigating psychiatric disorders, the Hierarchical Taxonomy Of Psychopathology (HiTOP), and modeling of latent factors to tease apart symptom variance associated with comorbid conditions. Following this, students are introduced to precision and translational psychiatry and issues pertaining to the promise or perils of translating computational psychiatry findings into real-world practice.

This course examines theoretical developments in the psychology of learning from a behavior analytic perspective. It provides definitions of the basic behavioral terminology and an overview of the emergence of the experimental analysis of behavior. By focusing on theoretically important experiments, it traces the evolution of behavior analytic research, starting with animal-based work using simple classical and operant conditioning paradigms and finishing by examining modern behavior analytic research on language and higher cognition in humans. The strong scientific tradition of behavior analysis is emphasized, as evidenced by rigorous measurement of behavior, precise specification of methods, and careful interpretation of outcomes.

This course introduces fundamental modeling principles in psychology, with a particular focus on model testing using the maximum likelihood approach. It covers the formulation of model likelihoods and the application of computational techniques to maximize them. The course demonstrates the use of models through examples primarily drawn from perception and cognition. Topics include threshold models, signal detection theory, multinomial processing tree models, reinforcement learning models, and more.