Introduction to Computational Cognitive Neuroscience

Course manual 2022/2023

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 goals and empirical frameworks of cognitive science, neuroscience and artificial intelligence
• Interpret primary computational cognitive neuroscience research literature and corresponding 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 test computational models against neural and behavioral data
• Design basic, feasible experiments that use methods from computational cognitive neuroscience to answer novel questions

Teaching methods

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

Learning activities

Attendance

Requirements of the programme concerning attendance (OER-B):

1. In the case of practicals, the student must attend at least 80%. Should the student attend less than 80%, he/she must redo the practical, or the Examinations Board may have one or more supplementary assignments issued.
2. In the case of study-group sessions with assignments, the student must attend at least 80% of the study-group sessions. Should the student attend less than 80%, he/she must redo the study group, or the Examinations Board may have one or more supplementary assignments issued.

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

Timetable

The schedule for this course is published on DataNose.

Additional information

This course makes use of Canvas for course-relevant information.

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