Course teacher

Associate teachers

-

ECTS credits

5

Number of hours: Lectures + Seminars + Exercises

30 / 15 / 0

Course objectives

The aim of this course is to introduce the students to important recent advances and emerging new trends in cognitive science research.

The focus of the course will be on predictive processing, an emerging new paradigm in cognitive science purportedly providing a unified theoretical framework for understanding perception, thought and action. Predictive processing has developed primarily out of the predictive coding paradigm in theoretical computational neuroscience. While predictive coding has for the most part been confined to the research of vision, predictive processing has generalized its theoretical framework so as to encompass cognition as a whole. After a brief conceptual and historical introduction to predictive processing, the first part of the course will be dedicated to Shannon’s information theory, and Bayesian methods in computational neuroscience - two theoretical pillars of predictive coding. Building from this, the second part of the course will provide an account of the main theoretical tenets of predictive processing focusing on Karl Friston’s formulations and the philosophical reception of his ideas (Hohwy, Clark). The last part of the course will present both important extensions of the predictive processing framework to others areas of research as well as highlight the main critical objections leveled at the framework as a whole.

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.
• Integrate insights, methods, and levels of analysis across different disciplines into a unified framework for understanding the human mind and cognition in general.
• Critically evaluate cognitive science findings and synthesize information to be employed in a collaborative professional environment.
• Employ cognitive science insights in developing innovative, human-friendly and sustainable technological solutions.
• Apply interdisciplinary approach in examining phenomena pertaining to cognition.
• 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)

• Introduction to the course. Predictive processing - fundamental concepts and the history of the idea.
• Fundamental concepts of probability theory. What is probability and how to measure it? Introduction to communication theory.
• Shannon's postulates of information theory. Surprisal as the measure of information.
• Entropy. Typical set and equipartition.
• Shannon's theorem on compression and Shannon's theorem on coding.
• Bayes' theorem. Bayesian statistics.
• Bayesian approaches in computational neuroscience.
• Predictive coding 1 – perception as causal inference; perception and Bayes's rule; prediction error minimization.
• Predictive coding 2 – prediction error, context, and precision; action and expected experience; active inference in perception.
• Free energy principle (Friston).
• The predictive mind – predictive coding and the mind-body problem (Hohwy).
• Predictive processing and embodied cognition (Clark).
• Active inference and enactive cognition. Active inference as a unified account of cognition and culture.
• Towards a Bayesian mechanics.
• Main objections and open questions on predictive processing.

Student responsibilities

Class attendance, seminar paper, project, written exam

Required literature

• Hohwy, J. (2013). The predictive mind. Oxford: OUP.
• Clark, A. (2016). Surfing uncertainty: prediction, action, and the embodied mind. Oxford: OUP.

Optional literature

• Dina Mendonça, D., Curado, M., Gouveia, S. (Eds.). (2020). The Philosophy and Science of Predictive Processing. New York: Bloomsbury Publishing.
• Metzinger, T., Wiese, W. (Eds.). (2017). Philosophy and Predictive Processing. Frankfurt am Main: MIND Group.
• Stone, J. (2015). Information Theory: A Tutorial Introduction. Sebtel Press.
• Ma, W.J., Kording, K., Goldreich, D. (2022). Bayesian models of perception and action. Cambridge, US-London, UK: MIT Press.
• Parr, Th., Pezzulo, G., Friston, K. (2022). Active Inference: The Free Energy Principle in Mind, Brain, and Behavior. Cambridge, US-London, UK: MIT Press.