Course description:

How do we infer other people’s mental states? The course will provide the opportunity to make an in-depth analysis of the cognitive processes underlying “mind reading” on the basis of a selected review of classic and state of the art empirical findings and theoretical models.

Compared to other domains of cognition such as memory, language, or perception, the field of “mind reading” has far less been subject to a cognitive analysis. During the course, classic concepts of cognitive psychology will be used to (a) examine current theories and empirical evidence (in adults), (b) highlight points of agreement, disagreement, and needs for clarification (c) explore future avenues to develop cognitive models of “mind reading”.


Learning outcomes:

At the end of the course, the student will be able to apply concepts of cognitive psychology to analyze experimental designs, empirical findings and theories in the field of mind reading.

Part I. Setting a conceptual common ground
1. About Mind Reading
-Terminology: Theory of Mind, perspective taking, mentalizing, mind reading, empathy
2. Conceptual distinctions
-Implicit versus explicit mind reading
-Cognitive versus affective theory of mind
-Decoding versus inferring versus representing mental states
-Having versus using a theory of mind
3. Current models
-Cognitive descriptions in current theories (e.g., simulation, theory Theory, ToMM model, 2-systems)
-Commonalities, divergences, compatibilities

Part II. Evidence helping characterizing the cognitive underpinnings of mind reading
4. The notion of automaticity
-Triggering factors
-Top-down control
5. The notion of domain-specificity
-Links with language
-Links with attention
-Links with executive control
6. The notion of neurally and functionally independent components
-Evidence from patients with brain-damage
-Evidence from neuroscientific tools with healthy participants

Part III. Where are we now and where are we heading to?
7. Are we closer to “cognitive models” of mind reading?
8. Open questions for the future

Course description: 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.

Learning Outcomes

By the end of this course, students will

•           know the basics of writing an empirical paper

•           know what to look out for when writing a research proposal

•           know how to best visualize empirical results or predictions

•           be familiar with the publishing process

•           have gained practice in preparing a poster

•           have gained insight into how to improve their oral presentation skills


Course Requirements: completion of writing assignments; presentation of posters and talks; regular attendance; participation in discussion

Venue: Frankel Leo 30-34, room 206.

Course Description

The aim of the course is to enhance the participants’ understanding of how research questions in Cognitive Science can be addressed with experimental designs. The course will enable the participants to turn well-formulated questions about the mind and brain into experiments that produce well interpretable results. It also aims to improve participants’ ability to judge whether experiments do or do not support the conclusions drawn from them.

There will be two parts to each session of this course. A hands-on part where the participants will help each other to design experiments that help them to answer their research questions. The more theoretical part will consist in readings/assignments that will provide good and bad examples of experiments and the use of various behavioral and neuroscience measures in Cognitive Science and Cognitive Neuroscience.

Learning Outcomes

By the end of this course, students will:

- know the main principles of experimental design

- be familiar with a variety of measure used in psychology and neuroscience

- be able to critically evaluate experimental designs used in published research

- be able to turn scientific questions into viable experiments

- know the grain size of questions that can be addressed in experiments 

Course Description

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.


Learning Outcomes

  • Getting acquainted with probabilistic thinking and interpretations of data
  • Understanding the logic of Bayesian data analysis
  • Gaining a basic knowledge about R, RStudio and JAGS
  • Being able to perform Bayesian analyses on your own data

Venue: Frankel Leo 30-34, room 206.

From social cognition to social phenomena

Course description

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

(Note that some key themes will be omitted. Joint action, for instance, has been taught in N. Sebanz and G. Knoblich’s research course. Mind-reading is a key ability that ground most aspects of our social life, but it will be dealt more thoroughly in D. Samson’s elective course. I have also included no brain studies).

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.