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Introduction to cognitive science

Associate teachers


ECTS credits


Number of hours: Lectures + Seminars + Exercises

30 / 30 / 0

Course objectives

Cognitive science is an interdisciplinary field of inquiry into the nature of the human mind and cognition in general operating at the intersection of psychology, computer science (artificial intelligence), neuroscience, linguistics, philosophy and anthropology.

The objective of this course is to introduce students to its history, fundamental problems, basic concepts, core disciplines, main tools and possible applications. Given the highly heterogeneous nature of the field, the emphasis will be on integrating important insights across different levels of analysis and disparate methodologies into a unified framework for understanding the mind and cognition. Paramount in this regard will be the representational-computational conception of the mind as an information processing system which will be the guiding thread of the course.

Accordingly, the focus for the most part will be on the crucial aspects of the representational-computational paradigm of cognitive science, both in its historical (classical computationalism and connectionism) and contemporary articulations (predictive coding/predictive processing). However, in order to provide a comprehensive account of the field of contemporary cognitive science the course will also present the basic tenets of the major anti-representational accounts of cognition, most notably dynamical systems theory and 4E cognition: cognition as embodied, embedded, extended and enactive.

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.
  • 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. Cognitive science – the history of the idea and of the term. The interdisciplinary nature of cognitive science – core disciplines, different levels of analysis (Marr's tri-level hypothesis) and methodologies. Cognitive science or cognitive sciences? The integration challenge.
  • The computational fundamentals of cognitive science - the idea of the universal Turing machine; Shannon's conception of information.
  • The cognitive turn in psychology - from behaviourism to a conception of the mind as an information-processing system (Miller).
  • Classical computationalism 1 - Physical symbol system hypothesis (Simon and Newell); formal analysis of language (Chomsky).
  • Classical computationalism 2 - philosophical aspects - machine functionalism (Putnam); the language of thought, the modularity of the mind (Fodor); the Chinese room argument (Searle).
  • Connectionism: McCulloch-Pitts artificial neuron model; Hebbian learning; Rosenblatt – perceptron; Minsky and Papert – critique of perceptron; Rumelhart and McLelland – parallel distributed processing.
  • Connectionism 2: artificial neural networks; network architecture (single-layer feedforward networks, multi-layer feedforward networks, recurrent networks); learning algorithms (error-correction learning, Hebbian learning, competitive learning....) and learning paradigms (supervised learning, reinforcement learning, unsupervised learning).
  • The debate between classical computationalism and connectionism - eliminative materialism (Churchland); Fodor and Pylyshyn's critique of connectionism.
  • The challenges to the representational-computational conception of the mind - dynamical systems theory, 4E cognition: cognition as embodied, embedded, extended and enactive.
  • Bayesian approaches in cognitive science: Bayes' theorem; perception as a Bayesian problem; predictive coding/predictive processing.
  • The cognitive science of attention and the attention economy.
  • The cognitive science of rationality.
  • Social cognition and theory of mind.
  • Emotions: from cognitive science to affective science.
  • The cognitive science of consciousness.

Student responsibilities

Class attendance, seminar paper, project, written exam

Required literature

  • Bermudez, J. L. (2020), Cognitive Science: An Introduction to the Science of the Mind (3rd Edition), Cambridge: CUP.
  • Friedenberg, J. D., Silverman, G. W. (2015). Cognitive Science: An Introduction to the Study of Mind (3rd Edition), Los Angeles: SAGE. (Selected chapters)
  • Chipman, S. E. F. (Ed.). (2017). The Oxford handbook of cognitive science. Oxford: Oxford University Press. (Selected chapters)
  • Newen, A., De Bruin, L., and Gallagher, S. (Eds.). (2018). The Oxford Handbook of 4E Cognition. Oxford: OUP. (Selected chapters)

Optional literature

  • Thagard, P. (2005). Mind: Introduction to Cognitive Science, 2nd Edition. Cambridge, US-London, UK: MIT Press.
  • Wilson, R., Keil, F. (Eds.). (1999). The MIT Encyclopedia of the Cognitive Sciences. Cambridge, US-London, UK: MIT Press.
  • Harre, R. (2002). Cognitive science. A philosophical introduction. London: SAGE Publications.