Quantitative and computational models in perception

Course objectives

The objective of this course is to introduce the fundamental principles of computational approaches in studying human perception.

This course will cover classical and modern approaches, along with the signal detection framework in studying psychophysics.

At the end of the course, students will have acquired knowledge on Bayesian inference, efficient coding, and predictive coding models. Similarly, students will have learned about different ways in which we try to reverse engineer the brain to improve our understanding of human perception.

Topics covered will include technical and practical aspects of model fitting.

Enrolment requirements and/or entry competences required for the course

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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.
  • Apply specific knowledge and skills from selected disciplines constituting 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.

Course content (syllabus)

  1. Introduction
  2. Classical psychophysics (methods, threshold, laws)
  3. Modern psychophysics (magnitude estimation, laws, psychophysical scaling, adaptive methods)
  4. Signal Detection theory
  5. Perception as an inference problem
  6. Bayesian inference
  7. Representations of uncertainty
  8. Adaptation and natural stimulus statistics
  9. Decision making
  10. Modeling response times in perceptual decisions
  11. Predictive coding
  12. Saccades and Smooth Pursuit Eye Movements
  13. Motor systems and action
  14. Model fitting - ML
  15. Model fitting - Bayesian parameter estimation (hierarchical) and Bayesian model comparison

Student responsibilities

Class attendance, project, homework, final exam

Required literature

  • 1. Farrell, S., & Lewandowsky, S. (2018). Computational modeling of cognition and behavior. Cambridge University Press
  • Kingdom, F. A. A., & Prins, N. (2016). Psychophysics: A practical introduction (Second edition). Elsevier/Academic Press.
  • Ma, W.J., Kording, K., Goldreich, D. (2021) Bayesian models of perception and action.

Optional literature

  • 1. Lu, Z.-L., & Dosher, B. (2014). Visual psychophysics: From laboratory to theory. The MIT Press.