Graduate School of health and Medical Sciences

Bayesian models of mind, brain and behavior
Provider: Faculty of Health and Medical Sciences

Activity no.: 3393-25-00-00	There are 26 available seats
	Enrollment deadline: 28/04/2025

Date and time	05.05.2025, at: 09:00 - 09.05.2025, at: 15:00
Regular seats	30
Course fee	4,080.00 kr.
ECTS credits	3.00

Contact person	Susanne Steffensen E-mail address: susannes@drcmr.dk

Enrolment Handling/Course Organiser	PhD administration E-mail address: phdkursus@sund.ku.dk

Aim and content This is a generic course. This means that the course is reserved for PhD students at the Graduate School of Health and Medical Sciences at UCPH. Anyone can apply for the course, but if you are not a PhD student at the Graduate School, you will be placed on the waiting list until enrollment deadline. After the enrollment deadline, available seats will be allocated to the waiting list. The course is free of charge for PhD students at Danish universities (except Copenhagen Business School), and for PhD students at NorDoc member faculties. All other participants must pay the course fee. Learning objectives A student who has met the objectives of the course will be able to: 1. Deploy core mathematical and Bayesian concepts to model basic cognitive, behavioral and neural processes. 2. Formulate a novel research question and develop a corresponding Bayesian model, representing it through formal graphical models and justifying the choice of priors and likelihoods in the context of the experimental question. 3. Analyze and integrate Bayesian models with neural data, explaining how model parameters relate to neural processes, and compare model predictions with empirical findings. 4. Present a Bayesian model to your peers, clearly explaining the research question, the model's structure, assumptions, and how the model's predictions help answer the research question, demonstrating effective communication of the key concepts. Content Understanding how the mind, brain, and behavior interact is one of the most complex challenges in the cognitive and neural sciences. Bayesian methods offer a principled and flexible framework for addressing this challenge, allowing us to model uncertainty, make predictions, and infer underlying cognitive and neural mechanisms from a diversity of types of data. Bayesian models are uniquely suited to understanding mental processes because they naturally account for uncertainty in both human cognition and experimental data. They provide a principled and unified framework for comparing models, allowing scientific questions about brain, mind, and behavior to be formally tested. In this course, students will learn to apply Bayesian models to questions in cognitive science and neuroscience, gaining practical experience in formalizing hypotheses about mental and neural processes and testing them against experimental data. The course is designed to be highly interactive and hands-on, providing students with opportunities to engage in group work, solve problems collaboratively, and develop practical skills that can be applied to their own research. Through a combination of lectures, exercises, and project work, students will learn how to implement and interpret Bayesian models in a variety of contexts, ranging from basic psychological processes to complex neural data integration. The course is divided into four progressive phases, each building on the previous one, culminating in a student-led project presentation. The course is designed to accommodate students from interdisciplinary backgrounds, and each phase will introduce new concepts and tools that will prepare students to apply Bayesian methods to their own research questions. This course is designed to give students not just theoretical knowledge, but practical skills they can apply to their own work in cognitive science, neuroscience, psychology, and related fields. By the end of the course, students will have developed a basic foundation in Bayesian modeling, including the ability to implement models, interpret results, and communicate their findings effectively. Day 1: Intro to modelling Day 2: Simple models Day 3: Intermediate models Day 4: Neural & Psychiatric applications Day 5: Presentations Participants The course is designed for anyone with interests in cognitive science, psychology, computational neuroscience, neuroimaging, behavioral science, data science, or neuroeconomics. Programming skills are advantageous but not essential. Basic statistical training and familiarity with high school mathematics is essential. As the course is explicitly designed for students from interdisciplinary backgrounds, no other prerequisites are required. Emphasis is placed on the intuition and mastery of basic concepts. Relevance to graduate programmes The course is relevant to PhD students from the following graduate programmes at the Graduate School of Health and Medical Sciences, UCPH: All graduate programme Language English Form A mixture of standard lecture, interactive lectures with interactive notebooks, and group work with expert support. Course directors Oliver Hulme, Associate professor, oliverh@drcmr.dk, Danish Research Centre for Magnetic Resonance (DRCMR), Hvidovre Hospital (HVH) David Meder, Senior researcher (DRCMR, HvH), davidm@drcmr.dk Teachers Oliver Hulme Associate Professor (Dept. of Psychology, Copenhagen University), Senior researcher (DRCMR, HvHl) David Meder, Senior Researcher (DRCMR, HvH) Simon Steinkamp, PostDoc (DRCMR, HvH) Melissa Larsen, Research fellow (DRCMR, Hvidovre Hospital) Janine Buehler, (DRCMR, HvH) Naiara Demnitz, Research fellow (DRCMR, HvH) Amin Kangavari, PostDoc (DRCMR, HvH) Dates Week 19, 5th - 9th May Course location Centre E, Auditorium 5 Copenhagen University Hospital Hvidovre Kettegard Allé 30 Registration Please register before: 28th April Course secretary: Susanne Steffensen, susannes@drcmr.dk, Tel: +45 38621184, DRCMR, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Kettegård Allé 30, DK-2650 Hvidovre Expected frequency Every 3 years Seats to PhD students from other Danish universities will be allocated on a first-come, first-served basis and according to the applicable rules. Applications from other participants will be considered after the last day of enrolment. Note: All applicants are asked to submit invoice details in case of no-show, late cancellation or obligation to pay the course fee (typically non-PhD students). If you are a PhD student, your participation in the course must be in agreement with your principal supervisor.

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