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Cognitive modeling

Associate teachers

Ivan Tomić, PhD

ECTS credits


Number of hours: Lectures + Seminars + Exercises

15 / 0 / 30

Course objectives

The aim of the course is to acquaint students with the leading computational frameworks of human cognition.

This course will introduce a wide range of contemporary models aimed at explaining a broad set of cognitive processes, including perception, attention, working memory, long term memory, learning, and language comprehension.

By the end of the course, students will have learned the fundamental principles and techniques, as well as caveats of cognitive modelling.

At the end of the course, students will be able to apply and critically evaluate cognitive models to understand behavioral data.

Enrolment requirements and/or entry competences required for the course


Learning outcomes at the level of the programme to which the course contributes

  • Explain major historical paradigms and recognize important new trends in cognitive science.
  • Apply theoretical knowledge of the fundamentals of the six core disciplines and their relationship within cognitive science.
  • Integrate insights, methods, and levels of analysis across different disciplines into a unified framework for understanding the human mind and cognition in general.
  • Design and conduct an interdisciplinary research project in cognitive science.
  • Employ diverse disciplinary tools in exploring and describing the nature of cognitive processes.
  • Apply specific knowledge and skills from selected disciplines constituting cognitive science.

Course content (syllabus)

  • Introduction; types of cognitive models; Paradigms; Marr’s levels of analysis
  • A cognitive architecture for modelling cognition (ACT-R)
  • Computational models of perception - overview
  • Theoretical models of attention
  • Computational models of attention
  • Working memory - history and theoretical accounts
  • Visual working memory - descriptive models
  • Visual working memory - neural implementation and the population coding approach
  • Associative memory; Computational models of episodic memory
  • Semantic memory
  • Theoretical and computational models of learning
  • Reinforcement learning
  • Attractor models
  • Models of language learning and comprehension
  • Connectionism in Cognitive Science

Student responsibilities

Class attendance, project, homework, final exam.

Required literature

  • Farrell, S., & Lewandowsky, S. (2018). Computational modeling of cognition and behavior. Cambridge University Press
  • Gazzaniga, M. S., & Mangun, G. R. (Eds.). (2014). The cognitive neurosciences (Firth edition). The MIT Press.
  • Trappenberg, T. P. (2010). Fundamentals of computational neuroscience (2nd ed). Oxford University Press.

Optional literature

  • Dayan, P., & Abbott, L. F. (2001). Theoretical neuroscience: Computational and mathematical modeling of neural systems. Massachusetts Institute of Technology Press.