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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.

If this is not possible, please contact the course coordinator before the course starts.


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 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, fitting them to data and interpreting the results. They will present the results of their models in a scientific poster. Students will also investigate a current issue in the cognitive modelling literature.

Course objectives

  • Students know the key paradigms and models employed in the fields of reinforcement learning and decision-making;

  • 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;
    o 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;

  • Students can interpret their model fitting findings, and present their conclusions to a scientific audience.


For the timetable of this course please refer to MyTimetable



Students must register themselves for all course components (lectures, tutorials and practicals) they wish to follow. You can register up to 5 days prior to the start of the course.


You must register for each exam in My Studymap at least 10 days before the exam date. You cannot take an exam without a valid registration in My Studymap. Carefully read all information about the procedures and deadlines for registering for courses and exams.

Exchange students and external guest students will be informed by the education administration about the current registration procedure.

Mode of instruction

The course is 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% written assignment;

  • 50% hands-on model fitting excercise, resulting in a 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. All students are required to take and pass the Scientific Integrity Test with a score of 100% in order to learn about the practice of integrity in scientific writing. Students are given access to the quiz via a module on Brightspace. 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.

Contact information

Dr. A. E. Urai