Duration
26h Th, 26h Pr
Number of credits
Lecturer
Language(s) of instruction
English language
Organisation and examination
Teaching in the first semester, review in January
Schedule
Units courses prerequisite and corequisite
Prerequisite or corequisite units are presented within each program
Learning unit contents
This course provides an overview of different mathematical modeling approaches to cognition. The goal of the course is to compare the various approaches constructively, trying to identify which aspects of cognition, which kind of experimental data, which type of questions are best addressed by each approach. The dynamical systems/embodied intelligence approach studied in detail in the course Brain Inspired Computing will also be touched upon here as one out of many approaches to understanding cognition.
Part I. Cognitive Modeling Paradigms (reading club and weekly presentations)
- Connectionists Models of Cognition
- Bayesian Models of Cognition
- Dynamical Systems Approaches to Cognition
- Deep Learning
- Reinforcement Learning
- Computational models of Decision Making
- Computational models of Working Memory
Each student will select a topic among the one proposed below or one they would like to propose. They will develop the chosen topic in the form of a research project, present their advances weekly to the class, and conclude with a final presentation of their project.
- Computational modeling of basic cognitive functionalities: categorization; inductive reasoning; deduction; decision-making; skill acquisition; episodic memory; cognitive control; associative learning; reinforcement learning.
- Application to cognitive fields: developmental psychology; personality and social psychology; industrial-organizational psychology; psychiatry; psycholinguistic; natural language understanding and generation; creativity; emotions and cognition-emotions interaction; morality; cognitive engineering; vision; motor control.
Learning outcomes of the learning unit
Students will be able to understand and use the most important and most widely used models of cognition, and how this model could be used in their engineering practice.
The course is open to non-engineering students, with the goal of fostering as rich an interdiscplinary discussion as possible.
Prerequisite knowledge and skills
The course is thought to be accessible by as large a variety of students as possible.
No specific previous knowledge is required.
The only requirement is the interest to learn about models of cognition.
Planned learning activities and teaching methods
The course will consist of an initial theoretical part that will be run as a reading club, guided by the professor but with active participation from the students, based on the book
Sun R, ed. The Cambridge Handbook of Computational Cognitive Sciences. 2nd ed. Cambridge University Press; 2023.
The last part of the course will be dedicated to project development.
Mode of delivery (face to face, distance learning, hybrid learning)
Face-to-face course
Additional information:
Face-to-face.
Course materials and recommended or required readings
The course will be based on the monograph:
The Cambridge Handbook of Computational Cognitive Sciences, 2nd Edition, Ron Sun Ed., Cambridge University Press, 2023.
Exam(s) in session
Any session
- In-person
written exam AND oral exam
Written work / report
Further information:
Reading club participation (50%). Final project presentation and oral exam (50%).