Only open to MSc Psychology (research) students.
Reinforcement learning is the adaptive process by which we learn to predict the consequences of our behavior through interactions with our environment. Over the last few decades, this has been intensively studied in a range of fields including psychology, artificial intelligence, animal and human neuroscience and economics. A related but largely separate literature is concerned with how we make optimal decisions based on noisy sensory information. Computational models of the putative underlying mechanisms of learning and decision making play a central role in both of these research fields. This course is intended to provide an overview of the computational and neural mechanisms of reinforcement learning and (value-based and perceptual) decision making, and to gain insight in the use of computational models to account for experimental data. Topics include classical and instrumental conditioning, Markov decision processes, the exploitation-exploration tradeoff, and sequential sampling models of perceptual decision-making.
The course meetings will be based on empirical papers that have made a significant contribution to the field and on papers that review a substantial body of research. Each student will write a short essay about a current issue in the reinforcement learning and/or decision making literature, and will initiate and lead a group discussion about this. In addition, students will gain hands-on experience with fitting computational models to experimental data, apply a model-based analysis to a data set, and present their results on a scientific poster.
Upon completion of the course, students will:
be introduced to the fields of reinforcement learning and decision-making, and obtain an overview of their key paradigms, models, findings and challenges;
gain insight and practical experience in the use of computational models to explain and interpret experimental data; and
obtain a basic understanding in how to translate abstract theoretical concepts in the field to concrete experiments, and learn to account for experimental findings through modelling considerations.
For the timetables of your lectures, work groups and exams, please select your study programme in:
Students need to enroll for lectures and work group sessions.
Master’s course registration
Students are not automatically enrolled for an examination. They can register via uSis from 100 to 10 calendar days before the date. Students who are not registered will not be permitted to take the examination.
Registering for exams
Mode of instruction
8 2-hour seminars
The assessment of the course is based on:
30% poster presentation
The Faculty of Social and Behavioural Sciences has instituted that instructors use a software programme for the systematic detection of plagiarism in students’ written work. In case of fraud disciplinary actions will be taken. Please see the information concerning fraud.
O’Reilly, J.X. (2013). Making predictions in a changing world—inference, uncertainty, and learning. Frontiers in Neuroscience, 7:105
Cohen, J. D., McClure, S. M., & Yu, A. J. (2007). Should I stay or should I go? How the human brain manages the trade-off between exploitation and exploration. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 362(1481), 933-942.
Dayan, P., & Daw, N. D. (2008). Decision theory, reinforcement learning, and the brain. Cognitive, Affective & Behavioral Neuroscience, 8(4), 429-453.
Voss A, Nagler M, Lerche V (2013). Diffusion models in experimental psychology: a practical introduction. Experimental Psychology, 60(6), 385-402.
Dr. Marieke Jepma