This course will examine how evolutionary approaches to psychology illuminate a wide range of social phenomena, including group identity, coalitions, religion, politics, fairness, morality, cooperation, gender roles, parenting, and more. Drawing in part on Pascal Boyer’s recent book "Minds Make Societies", the course aims to show how evolved cognitive mechanisms help explain the structure and dynamics of human societies.

Prerequisites: None
ECTS: 4

Science is a miracle. We are mammals with a slightly enlarged brain, chimpanzees with an advanced communication system. Yet we have discovered far away galaxies, understood the intricate details of cell metabolism, and grasped the fundamental laws of nature. How is that possible? How can a hairless ape create science and its infinite wonders? The human mind evolved to care about preys and predators, friends and enemies, not far away galaxies, the intricate details of cell metabolism, or the fundamental laws of nature.

Scientific ecology has focused mainly on the exchange of matter and energy. ‘Sensory ecology' takes a different perspective, focusing on the acquisition of information and the way in which living organisms respond to this sensory information to organise their interactions with their terrestrial or marine environment.

During the last decade, governments around the world have set up behavioural science units in order to improve the conception and delivery of policies: Obama's Social and Behavioral Sciences Team, the Behavioural Insights Team in Downing Street, the EU Policy Lab, etc. These have grown in influence, at times facing some backlash from public opinion. This course introduces you to the why and how of applying behavioural sciences to public policy.

Education can be seen as an ideal arena for the translation of research in cognitive science into society, and can be considered as an exemplary case study for reflecting on the opportunities, limits, conditions and issues of translational research. 

This course is an advanced introduction to theoretical and computational neuroscience. It introduces quantitative approaches to central questions in neuroscience: What functions and computations does the brain accomplish? By which mechanisms?  The scope of the course is threefold. First, to present a number of questions for which a quantitative approach is relevant. Second, to introduce mathematical tools necessary to the study of these questions, as well as to the study of similar questions in related fields (psychophysics, computer science, biophysics,...).

The objective of this course is to present and discuss artificial neural networks and their applications in the study of cognition. The course aims at showing the latest advances in machine learning and its use to understand our cognition, while also highlighting the limits posed by current techniques if considered as models of the brain. In a first part, the fundamental bases, the history and the development of these techniques will be presented.

Course content:
Introduction to Bayesian Inference, Conditional Probability and Bayes Theorem,
Asymptotic inference, Entropy of a distribution, Cross entropy, Posterior Distribution, Kullback Leibler Divergence, Irrelevance of prior distribution, Entropy of a Poisson Proces
Information and Shannon’s Entropy, Mutual Information, The Maximal Entropy Principle
Principal Component Analysis, Most Informative directions and top components ; Retarded learning phase transition.
Clustering, Online PCA

The course aims to provide students with an overview of the field of comparative cognition and a presentation of its concepts and methods. Starting from a brief presentation of the field, students will be faced with a variety of issues such as communication, sociality, cognitive mechanisms and metacognition. Each class will be divided into two parts: the first half of the class (1h) will consist in a lecture by an expert of the field, and the second part will consist in discussions around a given study (aka “journal club”), led by two students.