Introduction to Computational Cognitive Neuroscience

Course manual 2025/2026

Course content

Reverse engineering the mind, or understanding the computational principles that give rise to cognition, is a common goal of cognitive science, artificial intelligence, and neuroscience. Despite this shared goal, these three disciplines largely developed independently of one another, using different languages, concepts and tools. This course will present the study of cognition from the perspective of each of these disciplines and provide an overview the various empirical approaches used in each.

We will discuss parallels and differences between the three fields and consider how bridges can be built between them within the field of cognitive computational neuroscience. We will illustrate the overlap between fields by focusing on recent examples of where these fields come together, such as the deep learning revolution in visual object recognition and natural language processing. Via hands-on computer labs, we will work through concrete examples of how computational models are used in research practice to explain experimental data obtained from human behavior or brain recordings during cognitive tasks.

Study materials

Literature

• https://compcogneuro.org

Other

• Materials and course instructions will be made available on Canvas.

Objectives

• Describe the overall goals and example empirical frameworks of cognitive science, neuroscience and artificial intelligence
• Interpret primary computational cognitive neuroscience research literature and specific modeling methods
• Explain how cognitive science, neuroscience and artificial intelligence intersect and overlap within computational cognitive neuroscience
• Understand ethical aspects and best practices of computational cognitive neuroscience research
• Demonstrate the ability to interpret empirical results that come from testing computational models against neural and behavioural data
• Design basic, feasible experiments that use methods from computational cognitive neuroscience to answer novel questions
• Assess the appropriate use of large language models for research and learning in computational cognitive neuroscience

Teaching methods

• Laptop seminar
• Presentation/symposium
• Self-study
• Computer lab session/practical training

Learning activities

Attendance

Additional requirements for this course:

All learning activities support successful completion of this course. Mandatory attendance is in place for computer practicals and journal clubs, as these require active participation to achieve the learning objectives of this course. Lectures are not mandatory, but are interactive and part of the course materials. Skipping them will likely jeopardise the student to (fully) achieve the objectives of this course.

Assessment

Fraud and plagiarism

The 'Regulations governing fraud and plagiarism for UvA students' applies to this course. This will be monitored carefully. Upon suspicion of fraud or plagiarism the Examinations Board of the programme will be informed. For the 'Regulations governing fraud and plagiarism for UvA students' see: www.student.uva.nl

Course structure

Additional information

Use of GenAI in MBCS

Within the Research Master Brain and Cognitive Sciences, you are generally allowed to use Generative AI (GenAI) to support your learning process. For example, you can use large language models (LLMs) to help your self-study by generating flashcards, or generating explanations of concepts. You do so at your own risk:  an LLM may generate inaccurate or incomplete information for your studies. You are never allowed to use GenAI to generate work that you will hand in as an assignment, unless the assignment description explicitly allows you to do so.

Note: GenAI should be a support tool to help you reach the course's learning objectives, not a system to which you delegate activities that are meant to promote your learning. The course examiner has final say on which use cases are permissible or not within their course.

Never share personal information, research data, or course materials with a GenAI system, except for UvA AI Chat (https://aichat.uva.nl/). This UvA-hosted system was built with GDPR compliance and data security in mind. If you are in doubt about sharing information, don't share. You can always check with your course coordinator whether any intended use case is responsible.  

Teachers are never allowed to use GenAI to grade your work. They may, however, use it to formulate their feedback.

Course-specific rules on GenAI use

Within this specific course, permissible GenAI use is limited to:

• Supporting your self-study (e.g. by summarising articles, testing your understanding or generating explanations)

This particular course is part of a pilot experiment in which the deliberate use (or non-use) of GenAI by students is self-reported and discussed during plenary sessions with the whole cohort. Each week, you will fill in a brief form to reflect on any GenAI use. This information will be used to inform both course development within MBCS and, together with other pilots, overall GenAI policy at UvA.

Last year's student feedback

In order to provide students some insight how we use student feedback to enhance the quality of education, we decided to include the table below in all course guides.

Contact information

Coordinator

• dr. Iris Groen