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Reinforcement Learning and Decision Making: Computational and Neural Mechanisms


Entry requirements

Only open to MSc Psychology (research) students.

Students should have basic programming skills in R, and have the R software installed before the start of the course. It is strongly recommended to follow the elective ‘Introduction to R’ offered in the Research Master.
Alternatively, this tutorial gives a good introductory refresher:


Cognitive models define the algorithms underlying behavioral capacities such as learning and decision-making. For instance, reinforcement learning algorithms describe the adaptive process through which agents learn to predict the consequences of their behavior, through interactions with the environment. A related but largely separate literature is concerned with how we make decisions based on noisy sensory information.

Such cognitive computational models are widely used in psychology, cognitive science, economics, neuroscience and artificial intelligence to better understand the processes that give rise to intelligent behavior. Their components have been linked to specific processes in the brain, bridging from computation to functioning of the nervous system.

This course will discuss cognitive models of reinforcement learning and decision-making, their neural basis, and the use of these models to account for experimental data. The course will be based on empirical papers that have made a significant contribution to the field, and papers that review a substantial body of research. Topics include classical and instrumental conditioning, Markov decision processes, the exploitation-exploration tradeoff, and sequential sampling models of perceptual decision-making.

Students will also gain hands-on experience in implementing cognitive models and fitting them to data. They will present the results of their models in a scientific poster. Students will also write a short essay about a current issue in the reinforcement learning or decision-making literature.

Course objectives

  • Students know the key paradigms and models employed in the fields of reinforcement learning and decision-making (based on a theoretical overview provided by the lectures);

  • Students can critically discuss current issues and future perspectives related to the reinforcement learning and cognitive modelling literature.

  • Students can identify the steps necessary to translate abstract theoretical concepts into concrete experiments;

  • Students can understand and program computational models, and fit them to experimental data;

    • This ability will be a crucial tool for students: there is a fast-growing demand for skills related to programming and computational modelling in- and outside of academia;


For the timetable of this course please refer to MyTimetable


NOTE As of the academic year 2021-2022, you must register for all courses in uSis.
You do this twice a year: once for the courses you want to take in semester 1 and once for the courses you want to take in semester 2.
Registration for courses in the first semester is possible from early August. Registration for courses in the first semester is possible from December. The exact date on which the registration starts will be published on the website of the Student Service Center (SSC)

By registering for a course you are also automatically registered for the Brightspace module. Anyone who is not registered for a course therefore does not have access to the Brightspace module and cannot participate in the first sit of the exam of that course.
Also read the complete registration procedure

Mode of instruction

The course consists of 8 sessions, held in English. The sessions are a mix of lectures that present theoretical background, hands-on practical sessions to implement computational models, and a final poster session.

Assessment method

The assessment of the course is based on:

  • 50% essay;

  • 50% poster presentation.

The Institute of Psychology follows the policy of the Faculty of Social and Behavioural Sciences to systematically check student papers for plagiarism with the help of software. Disciplinary measures will be taken when fraud is detected. Students are expected to be familiar with and understand the implications of this fraud policy.

Reading list

Reading materials will be made available via BrightSpace. Example literature includes:

  • Wilson, R. C., & Collins, A. G. (2019). Ten simple rules for the computational modeling of behavioral data. Elife, 8, e49547.

  • Gallistel C.R. (1999) Book Review of ‘Reinforcement Learning’ by Sutton & Barto, 1998. Journal of Cognitive Neuroscience, 11.1, 126-134

  • Niv, Y., & Schoenbaum, G. (2008). Dialogues on prediction errors. Trends in cognitive sciences, 12(7), 265-272.

  • Gold, J.I., Shadlen, M.N., 2002. Banburismus and the Brain. Neuron 36, 299–308.

Contact information

Dr. A. E. Urai