The purpose of this course is to present an introduction to several formal frameworks relevant for linguistics (mostly within discrete mathematics). The first part bears on formal language theory (finite state automata, formal grammar, complexity of formal and natural languages). The next topic is first order logic, viewed mostly as a means to represent natural language semantics. Finally, some elements of lambda-calculus will be presented, so that students can get a first idea of Montague’s research program: treat English as a formal language.

The objective of this course is to train students in statistical methods that allow for the estimation of causal relationships, using randomized experiments or quasi-experiments. These methods involve either implementing controlled experimental protocols or leveraging statistical data to exploit “natural” experiments or social, economic, or institutional events, which under certain assumptions, produce differentiated exposure to treatment among various populations, making a causal interpretation plausible.

Why do fashions come in cycles? How many generations can a legend be remembered for? Can we predict the success of a movie? These are the kind of questions that cultural evolutionists seek to answer. The emerging field of cultural evolution combines models derived from the study of biological evolution with the methods of the behavioural sciences to shed light on the cultural dimension of social life. Culture, in this view, can be analysed as a set of transmitted ideas, norms, and patterns of action.

This course will explore some of the most popular computational frameworks employed for understanding human intelligence and cognition. Psychologists, among other data scientists, have to deal with increasingly large and multidimensional datasets of human behavior. Computational cognitive modeling aims to understand this type of complex behavioral data, by building mathematical descriptions of the latent (hidden) cognitive processes that produce the observed data.

This course introduces mathematical and computational modelling approaches to brain information processing. The objective is to initiate students to computational neuroscience and to teach key quantitative concepts. The course is organized in three modules:

The aim of this practical course is to learn to implement computational models of neural systems, and to perform basic analyses of neural data. The students will use Python to work on a series of projects that will cover the following topics:
1 - Estimating the activity statistics from recorded neural activity
2 - Modeling and simulating a spiking neuron
3 - Population coding: extracting information from neural activity
4 - Simulating and analyzing feed-forward and recurrent network models
5 - Training networks to perform cognitive tasks using deep learning.

The goal of this class is to introduce the fundamentals of Cognitive Anatomy through 13 lectures (Mondays, 2 hours/week, 8:30-10:30 AM, Salle Ribot, 29 Rue d’Ulm) and 10 practical sessions (2 hours/week, Fridays, 2:00-4:00 PM, Salle Ribot, 29 Rue d’Ulm). Two final sessions will be dedicated to evaluations, including group presentations of a research proposal. Each theoretical lecture will systematically cover a specific cognitive system, ranging from low-level sensory and motor networks to more complex and distributed higher cognitive functions.

The goal of this class is to familiarize students to research methods in experimental psychology with both a theoretical and practical approach. The course will introduce students to signal-detection theory and ideal-observer analysis. This will be followed by a systematic presentation of classic experimental paradigms (eg, single-interval vs multiple-interval paradigms; magnitude scales) and aspects of mental chronometry (measure of reaction times).

Reading, understanding, and critiquing scientific literature is a determinant skill for nearly any endeavor you will undertake in the future. If you stay in academia, this will form the foundation of your own research. If you go on to a career in R&D, your ability to critically appraise fundamental research could make or break your job standing. And in any case, you may find yourself trying to make evidence-based decisions on aspects of your life for years to come: Should you go on this diet or that one? Should you accept this treatment your doctor is suggesting?

This course is designed for students with formal quantitative training to learn mathematical tools that are useful for data-driven analyses in the empirical sciences, in particular cognitive science and neuroscience. The focus is practical; while mathematical derivations will frequently be demonstrated, these will be curated to help give intuition behind the tools rather than to provide an entirely rigorous foundation for the material. Some lectures may provide an overview of many different techniques, while others will delve deep into a single fundamental concept.