Bayesian Models of Mind Brain and Behavior
Course content
Understanding how minds, brains, and behaviors work and why is one of the most perplexing challenges in modern science. Bayesian methods offer a simple, principled, flexible and unified framework for addressing this challenge, allowing us to model uncertainty, make predictions, and infer underlying mechanisms from a diversity of types of data. Bayesian models are uniquely suited to understanding mental, behavioral and neural 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 ow research interests.
This course is designed to give students not just theoretical
knowledge, but practical skills they can apply to 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.
Topics will include:
Basic concepts in Bayes, Probabilistic reasoning, Generative
processes, Hypothesis testing, Bayes factors, Model selection,
Parameter and Model recovery, Integration of cognitive models with
neural and behavioral data.
Bacheloruddannelsen i Kognitions- og datavidenskab
Full-degree students enrolled at the Faculty of Social Science, UCPH
- Bachelor and Master Programmes in Anthropology
- Bachelor and Master Programmes in Psychology
- Bachelor and Master Programmes in Economics
- Master Programme in Social Data Science
- Master Programme in Global Development
- Master Programme in Security Risk Management
After completing the course, the student is expected to be able to:
Knowledge:
- Students will be expected to describe the basic concepts of Bayesian modelling, to define them mathematically, to mention their importance in how they apply to modelling mental, behavioral and neural processes, their philosophical interpretations, and describe their limits and advantages over other approaches.
Skills:
- Students will be expected to apply Bayesian concepts to express experimental designs via Bayesian graphical models. They will be able to write down graphical models that test particular theories, and reformulate them according to new constraints.
Competences:
- Students will be expected to analyse and evaluate Bayesian graphical models and judge what they are modelling and evaluate whether this is suited to the research question. Students will flexibly design new models for novel research questions. Students will have the competence to evaluate and diagnose whether models are performing adequately in their purpose by designing model and parameter recovery methods. Students will be expected to justify and explain the choice of priors and likelihoods in the context of the experimental question.
The teaching will be a mixture of lectures and in class exercises.
Main Literature:
Bayesian cogntive modelling: A practical course by Michael D Lee
& EJ Wagenmakers
Programming skills (python, matlab) are advantageous but not essential. Basic statistical training and familiarity with high school mathematics is essential.
Course requirements corresponding to Statistics 1 and Statistics 2 (BA in Psychology).
When registered you will be signed up for exam.
- Full-degree students – sign up at Selfservice on KUnet
The dates for the exams are found here Exams – Faculty of Social Sciences - University of Copenhagen (ku.dk)
Please note that it is your own responsibility to check for overlapping exam dates.
- ECTS
- 7,5 ECTS
- Type of assessment
-
Home assignment
- Type of assessment details
- Free written assignment.
An assignment of a maximum of 8 standard pages for 1 student, a maximum of 12 standard pages for 2 students, and a maximum of 14 standard pages for 3 students.
Students will derive a novel experimental research question in the domain of cognitive, neural or behavioral sciences. They will present the background motivation and literature, the experimental design, the proposed Bayesian model for analysing the data, and how it addresses the theoretical research question. They will justify the chosen priors and model structure and evaluate how they will assess model quality. - Examination prerequisites
-
There are no other exam prerequisites that the student must meet to participate in the exam.
- Aid
- All aids allowed
Unless otherwise specified, the Department of Psychology prohibits the use of generative AI software and large language models (AI/LLMs), such as ChatGPT, for generating novel and creative content in written exams. However, students may use AI/LLMs to enhance the presentation of their own original work, such as text editing, argument validation, or improving statistical programming code. Students must disclose in an appendix if and how AI/LLMs were used; this appendix will not count toward the page limit of the exam. This policy is in place to ensure that students’ written exams accurately reflect their own knowledge and understanding of the material.
- Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
- Exam period
-
Exam information:
The examination date can be found in the exam schedule here
The exact time and place will be available in Digital Exam from the middle of the semester.
- Re-exam
-
Same as the ordinary exam.
Reexam information:
The reexamination date/period can be found in the reexam schedule here
Criteria for exam assessment
Students are assessed on the extent to which they master the learning outcome for the course.
To obtain the top grade “12”, the student must with no or only a few minor weaknesses be able to demonstrate an excellent performance displaying a high level of command of all aspects of the relevant material and can make use of the knowledge, skills and competencies listed in the learning outcomes.
To obtain the passing grade “02”, the student must in a satisfactory way be able to demonstrate a minimal acceptable level of the knowledge, skills and competencies listed in the learning outcomes
- Category
- Hours
- Lectures
- 28
- Preparation
- 112
- Exam
- 66
- English
- 206
Kursusinformation
- Language
- English
- Course number
- APSB23700U
- ECTS
- 7,5 ECTS
- Programme level
- Bachelor
- Duration
-
1 semester
- Placement
- Spring
- Studyboard
- Department of Psychology, Study Council
Contracting department
- Department of Psychology
- Department of Anthropology
- Department of Political Science
- Social Data Science
- Department of Economics
Contracting faculty
- Faculty of Social Sciences
Course Coordinator
- Oliver James Hulme (7-75726f7c6b786e4676797f34717b346a71)
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Kursusinformation for indskrevne studerende