This course will provide an introduction to practical methods for making inferences from data using probabilistic models for observed and missing data.  This approach is an alternative to frequentist statistics, the presently dominant inference technique in sciences, and it supports a common-sense interpretation of statistical conclusions by using probabilities explicitly to quantify uncertainty of inferences. The course will introduce Bayesian inference starting from first principles using basic probability and statistics, elementary calculus and linear algebra.  We will progress by first discussing the fundamental Bayesian principle of treating all unknowns as random variables, and by introducing the basic concepts (e. g. conjugate, noninformative priors) and the standard probability models (normal, binomial, Poisson) through some examples.  Next, we will discuss multi-parameter problems, and large-sample asymptotic results leading to normal approximations to posterior distributions.  We will continue with hierarchical models, model construction and checking, sensitivity analysis and model comparison. We will conclude the course with explicitly contrasting frequentist and Bayesian treatment of null hypothesis testing and Bayesian formulation of classical statistical tests.  Students in the course will get familiar with the software packages R and JAGS, which will allow them to fit complex Bayesian models with minimal programming expertise.  Familiarity with Matlab or C++ programming is required.


Research combines experimental and theoretical approaches and focuses on linguistic phenomena at the interface between semantics and pragmatics, such as scalar implicature, presupposition and metaphor. She is particularly interested in the acquisition of pragmatic abilities in typically and atypically developing children.
This course will provide a hands-on introduction to programming in Matlab with a special focus on applying it to create psychological experiments and to analyze human behavioral data. After a general introduction to the basic ingredients of programming (variables, loops, good programming styles etc.), we will use Matlab to write little experiments and to collect, analyze and plot real data. This will involve simple reaction time experiments but the course will also offer an introduction to collecting and analyzing 3D human movement data with the Polhemus motion tracking system. Course participants will be required to do programming assignments.

Suggested handbook: David A. Rosenbaum, Matlab for Behavioral Scientists (copies available in the CogSci library)

The course provides an introduction into current-day philosophically inspired cognitive developmental theory and evolutionary perspectives of the nature of human concepts and their origins. It covers the current theorizing and empirical research on the central issues of the phylogenetic and ontogenetic origins of concepts and the nature of the human mind’s ability for conceptual change. The core reading for the course will be the book by Susan Carey entitled “The origin of concepts” (OUP, 2009). The in-depth reading and discussion of this representative book and some additional papers will cover the comparative analysis of the nature of processes of conceptual change, reorganization, and theory construction in cognitive development on the one hand, and in history of science, on the other.
What are the psychological bases of the rich social interactions and cultural life that characterise human societies? This course will review some of the answers provided by recent studies in cognitive psychology, evolutionary psychology and social anthropology. It will cover a wide range of topics related to social cognition and human sociality, including:
• Mind reading
• Naive sociology
• Communication, social learning, imitation
• The biological evolution of social cognitive capacities
• Models of Man in the social sciences
• The cultural diversity of human psychology
• How human psychology constrains culture
• Models of cultural evolution
• Co-operation and moral cognition


The course is structured in three parts that focus on different aspects of social cognition and human sociality. The first parts is focused on the social cognitive skills that humans have. It will include sessions on mind-reading, social perception and naïve sociology, and the biological evolution of social cognition. The two last parts are focused on culture and cognition. The second part will review social scientists’ take on human psychology and how it influences their understanding of social phenomena. The third part will deal with specific themes in cognition and culture: morality, religion and science.

This course aims at improving oral and written presentation skills that are vital for Cognitive Scientists. How does one write an abstract, a methods section, or a results section for an empirical paper? How can experimental results be presented most effectively? What are good strategies for dealing with reviewers’ comments when revising a paper? How does one write a review? What is important to keep in mind when writing a research proposal? What makes for a good oral presentation? Course participants will learn about all of these and many more aspects of exposition through hands-on experience.
This course introduces students to the use of electroencephalography (EEG) for measuring brain function to access cognitive mechanisms in humans. This is a practical course, where students receive hands-on experience in recording and analyzing EEG data, as well as in designing experiments and interpreting findings using this method.