Course catalog

This course offers an extensive overview of neuroscience, structured to be accessible for students with varied academic backgrounds. It covers fundamental neuroscientific concepts, the structure and function of the nervous system, the different main functions of the brain and the latest methodologies used in neuroscience research.

The course methodically explicates that brain phenomena are the result of specific neuronal activities and interactions within complex neural networks, comprising both active and inactive neurons. This fundamental understanding dispels the ambiguity surrounding cognitive processes, offering a clear and scientifically grounded perspective.

The course lays a solid foundation for students aspiring to specialize in Neuroscience, ensuring they grasp these core principles. Simultaneously, this knowledge significantly enriches the comprehension of cognitive science across a spectrum of disciplines ranging from linguistics, anthropology, philosophy, and social science up to computer science and artificial intelligence.

This course will cover the bases of three fundamental psychological processes: human perception, core knowledge and memory. The course will focus mainly on experimental psychology, neuropsychology and neurophysiology providing an overview of the behavioral and neural correlates of these three pillars of human psychology.

The purpose of this course is twofold: (1) to introduce the range of widely used computational models across neuroscience, cognitive science, and artificial intelligence, and (2) to introduce key concepts in these fields. Regarding the first aim, students will learn about different types of models and their implicit assumptions. For the second aim, students will learn different computational frameworks, ranging from single neurons to social interaction. Key equations will be explained to illustrate these concepts, but students will not be required to solve or manipulate equations. As such, a quantitative background is not a prerequisite.

This course aims to provide students with the fundamental concepts and tools necessary to study human behavior and societies through an integrative approach. Key concepts from evolutionary biology, behavioral ecology and cognitive psychology will be introduced to explain a variety of social phenomena, ranging from political attitudes to narrative fictions. Through a variety of interdisciplinary case studies students will learn to apply these theories to real-world issues, including vaccine hesitancy, climate change and school drop-out. While focusing primarily on human social behavior, the course will also address the influence of environmental variability, subsistence strategies, and ecological legacies on human societies.

Learning Outcomes:

On successful completion of this course, students should be able to:

• Understand the interaction between evolutionary, cognitive and cultural factors in shaping human behavior, and the role of environmental variability in this process.

• Master the key concepts from evolutionary psychology and behavioral ecology, such as life history traits, evolutionary stable strategies, conditional cooperation, and signaling.

• Develop a critical understanding of how humans produce social structures, institutions, and collective actions emerge in response to environmental and cultural factors.

• Apply the key concepts from evolutionary psychology and behavioral ecology to explore social sciences questions such as religious beliefs, political attitudes and aesthetic preferences

This course is an introduction to the principled study of human natural language. Our chief interest and goal is to examine mathematically rigorous theories of (fragments of) the human cognitive capacity for language. To this end, we will introduce the fundamental concepts and theories in phonology, morphology, syntax, semantics, and pragmatics. We will discuss in some detail the philosophical foundations for the study of human language from this cognitive and mathematically intelligible perspective: Are all of the formal symbols that occur in our theories somehow in the head?

In less detail due to time constraints, we will also introduce classics and in some cases illustrate recent work on the neurobiological bases of human language (neurolinguistics), the specifics of how the human capacity for language is deployed by humans in language production and comprehension (psycholinguistics), how children learn their native language effortlessly and with no substantive instruction (language acquisition), and what the points of contact and divergence are between the human faculty for language and the impressive behavioral achievements of modern large language models.

Assessment will involve eight homework assignments (60%), a final in-class exam (30%), and attendance and participation in class and TA sessions (10%).

This course has no prerequisites, in particular this course does not presuppose any background in traditional grammar besides what you will have inevitably seen in high school, or any background in formal language theory or related topics.

This course aims to (i) provide the conceptual foundation for and (ii) introduce students to core debates in philosophy of cognitive science.

This course aims to provide an integrated training in how to be a good quantitative cognitive scientist. A huge part of being a cognitive scientist, indeed being any kind of scientist, is data science: the skills involved in capturing the right data, organizing the data in the right way, visualizing data, making inferences from data, writing about data, and curating your data openly. The course combines several elements. It introduces students to the statistical programming language R. It provides a basic course in statistics and data visualization. It covers study design and issues of measurement. Finally, it covers good research practices such as pre-registration, validity, reliability and reproducibility.

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). Concepts taught in class will be illustrated by practical examples (i.e., how to choose between experimental paradigms based on theoretical and practical constraints). The goal is to provide a tool-box to students interested in pursuing research in experimental psychology, or any other cognitive science that uses behavior as a measure. Another goal is to allow students to develop critical thinking skills about methods in psychology, and the appropriateness of a given method to a particular scientific question.

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 content will address three main aspects of each system:

1. The key neuroanatomical structures and networks involved, their general topographical organization, and their localization in different types of brain imaging.

2. The cognitive/functional contributions underlying physiological mechanisms, including relevant research assessment tests to explore these processes.

3. Examples of pathologies associated with dysfunctions of these systems, which are used as experimental models to explore these processes or for diagnostic & therapeutic applications.

Program of theoretical lectures

After an introduction in Lecture 1: Cognitive Anatomy and Methods in the domain, the systems covered this semester by the class will include: Lecture 2: The oculomotor and eye movement systems; Lecture 3: Voluntary action and motor learning systems including the cerebellum; Lecture 4: Auditory pathways and central sound processing structures; Lecture 5: Systems for visual processing and spatial attention; Lecture 6: Systems underlying memory, learning, and the effects of sleep and emotions; Lecture 7: Executive and cognitive control systems; Lecture 8: Decision Making, Basal Ganglia & Learning; Lecture 9: Motivation and Reward Systems and Emotions; Lecture 10: Language and Communication systems; Lecture 11: The neural basis and systems underlying Consciousness; Lecture 12: Neural basis and systems for Social Interaction; and we will wrap up the course with a more general final Lecture 13: Brain Anatomy, Plasticity & Rehabilitation.

Program of practical sessions

Practical sessions will begin with Session 1, which will include a visit to the CENIR (Imaging platform of the Institut du Cerveau (ICM) at the Pitié Salpêtrière Hospital, 82 Boulevard de l’Hôpital, Bâtiment ICM, Hall of Floor -1, Paris, Chevaleret, Line 6, or Saint Marcel, Line 5). The subsequent practical sessions will be divided into 4 parts: Sessions 2, 3, and 4 will focus on Structural MRI; Sessions 5 and 6 will cover Functional MRI; Sessions 7 and 9 will address Ultrasound Imaging and Non-invasive Brain Stimulation coupled with EEG recordings, respectively. Finally, Sessions 8 and 10 will cover EEG, MEG, and sEEG methods and analysis. During these sessions, other techniques used to explore the anatomy and functions of the brain will be presented, such as intracranial electrophysiology and functional ultrasounds (FUS). In some of these practical ‘hands-on’ sessions, we aim to show you how these techniques work and how they lead to conclusions, from raw data to finished results, using real human datasets and custom analysis scripts.

Home scored assignments and evaluations

Students will be required to organize themselves into groups of three and develop a joint 10- to 15-page written essay (excluding references and annex documents) on a ‘hot’ research topic in cognitive neuroanatomy involving healthy human participants or patient populations and one or several of the cognitive systems discussed in class. The essay will be graded and should take the format of a research proposal, including the following sections: (1) Title and Summary (½ page): A brief overview of the proposal; (2) State of the Art (3–5 pages): A systematic review of existing research in the chosen area of interest; (3) Research Question and Objectives (1–2 pages): Identification of a relevant and novel research question within this context, along with the general and specific objectives of the proposal. (4) Methodology and Design (3–5 pages): A detailed description of the methods and experimental design best suited to assess the research question and test the hypothesis (5) Predictions and Hypotheses (1–2 pages): A series of expected outcomes for each specific aim; (6) Methodological and Experimental Risks (1 page): Identification of major risks and proposed alternatives to mitigate their impact on the study; (7) Ethical Considerations (1 page): Discussion of medical and psychological risks, participants’ rights (including privacy and data confidentiality), gender biases, and proposed solutions; (8) Scientific and Societal Impact (1 page): An explanation of the potential scientific, clinical, social, and technological impact of the proposal; (9) References: A list of all cited references; (10) Figures and Annex Documents: Additional figures or supplementary materials (no page limit).

Two evaluation sessions will be organized (Sessions 11 & 12), during which each student group will prepare a PowerPoint presentation and orally present their written proposals in 15 minutes before their peers and the coordinators, followed by a Q&A session. The presenting teams will be evaluated on their ability to effectively communicate the key aspects of their proposals within a limited time and respond to audience questions. Attending teams will also be scored based on their ability to raise and ask insightful questions to their peers. In each session, following the presentations and discussions, a short quiz will assess students’ ability to identify brain structures on images and describe their names and cognitive functions.

The final grade will consist in a weighted mean of written assay/project (40%), its public presentation and defense by all three members (40%) and the results of the anatomical quizzes (20%). Additional +10% will be added for insightful and relevant questions during project/assay presentations. Note the written assay/project, the public defense and the +10% for insightful relevant questions evaluate collective coordinated effort and will be assigned to each member of the group.

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.

This course should be taken in conjunction with Computational Neuroscience

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:

1. Modeling of cognition and behavior (classical and operant conditioning, reinforcement learning, decision-making)
2. Information processing (neural decoding, population encoding, sensory processing, linear receptive fields,)
3. Dynamics and mechanisms (biophysics of neurons, feedforward and recurrent neural networks, synaptic plasticity, associative memories)

Learning outcomes

Students will be introduced to and familiarized with concepts of computational modelling in neuroscience.

Students will be able to:

• understand models of reinforcement learning
• understand models of neural decoding using signal detection theory
• understand models of population coding and decision making based on single neuron activity
• understand models of neuronal biophysics and network dynamics
• read and analyse papers in computational neuroscience

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. Over the past decade, computational cognitive models have become instrumental in cognitive science to understand and even predict human behavior using mathematical descriptions of the mind. The main goal of this course is to provide students with the objectives, philosophy, and technical underpinnings of computational cognitive modeling.

Throughout lectures, students will delve into various subjects, including signal detection theory, reinforcement learning, Bayesian modeling, model fitting, model comparison, and artificial neural networks. The scope of modeling examples encompasses a wide array of psychological abilities such as categorization, learning, memory, decision-making, and reasoning. By the end of the course, students will possess a deeper understanding of how computational modeling can move cognitive science forward when it is applied adequately to address a particular research question. Students will also acquire the skills to fit, evaluate and compare computational cognitive models for a deeper understanding of complex multidimensional behavioral data.

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. Cognitive science is increasingly being used to help us better understand how this transmission works, and in return, cognitive scientists have gained a better grasp of topics like the nature of social learning or the motivations for cooperation. This class will seek to introduce the bases of cultural evolutionary research as it is practised today, in a oecumenical spirit, covering notions from all the relevant “schools” that are active today in the field (behavioural ecology, evolutionary psychology, cultural evolution, cultural attraction).

Learning outcomes: The goal is to learn how to engage with contemporary debates in the field of cultural evolution, to understand and apply simple cultural evolutionary models, and to analyse cultural data from experiments, historical sources, or web data.

Previous attendance of the course ‘Human behavior, cultures, and societies’ is recommended but not required.

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.

The main chapters are:

1. Rubin Causal models and RCTs
2. Imperfect compliance
3. Instrumental variables and LATE
4. Difference-in-difference
5. Regression discontinuity
6. Design-based inference
7. Experimental designs
8. Introduction to Machine learning for causal models
9. Topics

Grading will be based on quizz routinely filled before or during lectures and a final exam.

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.

This course is a fast-paced introduction to natural language semantics and pragmatics.

Human beings can produce and understand, without any apparent effort, sentences that have never been used or understood before. This capacity must rely on a mechanism whereby the meaning of a complex expression can be computed on the basis of that of its parts and the way they are combined (compositional semantics). Furthermore, the interpretation of sentences does not only rely on their linguistically encoded meaning, but also on all the inferences that we are able to draw, in a given context, regarding the communicative intentions of speakers (pragmatics). Formal semantics and pragmatics aim to explore these two dimensions of meaning.

The course is roughly organized around three main themes:

Compositional semantics: extensional semantics; compositionality; predication and modification; pronouns and quantification, polarity phenomena (60%)

Semantics and Pragmatics: presuppositions and scalar implicature, questions and answers (25%)

Intensional semantics (15%): limitations of extensional approaches; modality; propositional attitudes.

The class is taught in English, but questions can be asked in either French or English, remarks can be made in either language. Homework assignments can be done either in English or French.

The course explores what human beings know about the sound patterns of their languages, how they learn it, and how this knowledge is represented in their minds. We begin with an overview of the major characteristics of sound patterns, and introduce core phonological concepts (phoneme, feature, alternation). We will then look at research that has sought to determine what phonological generalizations speakers extract from the learning data, and at the implications of these findings for achieving a descriptively adequate grammatical framework: basic rule notation, features, and constraint interaction. Next, we will consider a range of methods that are used to determine speakers’ implicit phonological knowledge, computational models of phonological acquisition, and the role of auditory perception in shaping phonological grammars. Students will also learn core concepts in articulatory and acoustic phonetics, and learn basic tools to analyze the speech signal into abstract units of representation.

This course aims to teach philosophical writing skills for students who have decided to specialize in philosophy.

Maximal enrollment: 6 students

In this course we will engage with a series of current debates in the philosophy of psychology. We will ask how recent findings in the cognitive sciences bear on traditional philosophical questions concerning psychological capacities such as emotion, perception, memory, imagination and high level cognition.

First year, second semester

Number of hours: 24h

The course approaches various concepts revolving around the neural code and neural computations, with examples drawn both from the experimental literature and computational models. The concepts can be understood with minimal mathematical background. The course is organized around distinct modules, each targeting a central concept in cognitive neuroscience. We will discuss methodological and epistemological challenges and how they structure current research. Examples will be drawn from all fields of cognition (e.g., memory & learning, consciousness, decision making) and include both human and animal examples.

Learning outcomes

On successful completion of this course, students should be able to:

• Understand key concepts in cognitive neuroscience and how they relate to experimental strategies, in vivo and in silico
• Identify constraints on interpretation in experimental research articles,
• Develop a critical mindset when analyzing the literature, when building a research project
• Learn how concepts evolved and continue to evolve

Level: M2

Number of hours: 24h

Language: English

The course aims at understanding how humans make voluntary decisions and adaptively act in the environment. The course notably addresses the neural bases of central executive functions (judgment & decision-making) in humans. These functions associated with the frontal lobes form the capacity to decide not only in response to external events but also in relation with intentions and choices stemming from motives, preferences and beliefs, which in turn derive from overt behaviour and covert reasoning. The course will address this issue from the viewpoint of cognitive and computational neurosciences and to a lesser extent, from modern philosophy.

This course provides an overview of behavioural and computational approaches to value-based decision-making. It examines how decision variables (such as expected utility and its components, including value, probability, risk, and delay) are combined to guide choices.

Particular emphasis is placed on the use of computational models to formalise hypotheses about decision-making and to connect observed behaviour with underlying cognitive processes. The course introduces key modelling frameworks used to describe how individuals evaluate options, learn from experience, and make decisions under risk and uncertainty. It also considers how behavioural observations can reveal systematic departures from normative predictions and how these deviations can be incorporated into descriptive and mechanistic models of choice.

The final part of the course extends these approaches to contemporary questions concerning human–machine interaction, including how people evaluate, use, and respond to recommendations or decisions generated by computational systems.

More information at this link.

The goal of this seminar is two-fold. First, it aims to provide basic knowledge about the “Bayesian brain hypothesis”, the idea that Bayesian inference is useful to describe the functioning of the mind (psychology) and the brain (neuroscience). The course will illustrate several domains of cognition in which the Bayesian brain hypothesis has been successful. The course will also be critical, emphasizing what are the caveats and limitations of this hypothesis. At the end of the course, students should be more knowledgeable of this field, and have a nuanced perspective on the “Bayesian brain hypothesis” by distinguishing different components of this hypothesis (the use of priors, predictions, uncertainty, Bayes rule, conditional probabilities).

The first part of the course will provide elements of background knowledge. Then, the seminar will focus on more specialized, currently hotly debated aspects of the Bayesian brain, by presenting questions and studies at the front edge of current research. The goal of this second part is not to be exhaustive, but to present a sample of representative hot topics.

The course explores the interactions literature on self and the literature on consciousness. We will ask whether consciousness entails some degree of self representation, we will also see the many ways the self can impinge on consciousness. To do so we will rely on philosophical analysis, psychological constructs and neurobiological mechanisms. Special emphasis is placed on the experiencing self at a given moment.

Cognitive science and Art both deal with the human mind, often with different methods, but always with a shared sense of wonder at this most puzzling of phenomena. The aim of the “Cognitive Science & Art”course is to present parallel artistic and cognitive approaches to current topics relevant to both fields of inquiry.

For each topic, we will have joint seminars by a researcher from one of the labs of the Department of Cognitive Studies at Ens, and by an artist or researcher in a cultural discipline. In addition, there will be the opportunity to discover three creative institutions in Paris, with “hors les murs” classes at the Ecole des Arts Décoratifs (ENSAD); Sony Computer Science Lab (Sony CSL); Institut de Recherche et Coordination Acoustique/Musique (Ircam).

The course will be deeply interdisciplinary, with contributions from most labs and disciplines of the Master in Cognitive Science - but with no prerequisite, so all backgrounds are most welcome. Given the somewhat unusual format, students should not expect a traditional academic transmission of knowledge, but rather, they should be keen to participate in open discussions with individuals contributing right now to the ongoing and perhaps accelerating dialog between Art and Science.

The list of topics and speakers for 2025/2026 is here:

https://moodle.psl.eu/course/view.php?id=25358.

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. Additionally, students will present a literature review (written report or oral presentation) on a subject of their choice due by the end of the semester. The aim of such a broad overview, combined with the homework, is for students to become aware of the main research questions addressed by the field, and to be able to understand and critically discuss studies in comparative cognition.

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

Priors regularisation and Sparsity, Priors for least squared regressions, Cross validation for optimal prior strength

Graphical Inference : Network reconstruction for multivariate Gaussian variables, Ising model and Inverse Ising model, Pseudo Likelihood, Boltzmann Machine Learning, mean field inference, Inference of couplings from neuronal data

Unsupervised learning: Autoencoders, Restricted Boltzmann Machines. Linear and Non LinearActivation functions, Representations

Classification with neural network and perceptron learning algorithm, Multilayer neural networks

Markov models and Hidden Markov Models

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. After a presentation of the perceptron, the gradient descent and backpropagation algorithms, the course will present the main contemporary neural methods and architectures, in particular convolutional networks, recurrent networks (such as LSTM), and Transformers, which are modern networks based on a concept of attention that are used in large language models such as ChatGPT. In a second part, guest lecturers will present the use made of these models in their research in cognitive science. Particular focus will be on the comparison between patterns of activations in artificial and biological neural systems. One session will be devoted to the implementation of a learning algorithm. The last part will be devoted to students’ presentations, considered as part of the course.

Notice: This course is NOT a substitute for an advanced course on deep learning or more broadly machine learning. This course does NOT address issues in statistical inference.

Recommended Level: Biology, Cognitive Science: M2; Mathematics, Physics, Computer Science: M1 or higher. The course is also of interest for PhD students or postdocs in Cognitive Science or Computational Neuroscience.

Webpage: https://l-bg.github.io/dlcs/

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,…). Third, and maybe most importantly, to discuss concrete examples relevant to brain function in which one can make progress through modelling. Questions and examples that will be discussed include: How do neurons code inputs to the brain? Is ‘function’ carried out by single neurons or by groups of neurons? How can one model the learning and storage of memories? How does the brain generate outputs such as motor outputs?

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 explores the impact of cognitive science on education. How can it contribute to education? Under what conditions, in what way? What are the constraints, the limits, and the potential pitfalls?

Over 11 lectures, the course will cover major areas of the application of cognitive science to education. Three leading themes will guide the discussion throughout:

1. From the brain to the classroom. Fundamental knowledge and how it can help designing education.
2. “Evidence-based education” and “translational research” in education: What does it mean? Why does it matter? What are the methods?
3. From theory to practice: How to design and produce interventions in education, inspired by research in cognitive science?

As a sampler, the lectures will include the following topics:

• Evidence-based education and experimental research in education
• Neuromyths in education
• Learning to read
• Learning mathematics
• Social experiments in education
• Explicit teaching
• Metacognition
• Memory and learning
• International evaluations
• Critical thinking
• Nature and nurture

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. We present the basic theories and experimental findings which are most commonly leveraged to design behaviourally aware policies and policy delivery. We simultaneously provide a hands-on experience through the analysis of actual behaviourally informed policy experiments and through the design of prospective ones.

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. Von Uexkull (1921) was the first proponent of sensory ecology to stress the uniqueness of the sensory worlds in which different living organisms live and, consequently, to recognise the need to identify the particular characteristics of the environment that are relevant to each species. This sensory information is crucial when it comes to understanding how living organisms respond to rapid human-induced environmental change. Sensory ecology can then be used as a tool in conservation and management strategies to understand how certain species respond to these rapid environmental changes and how the negative impacts of these changes can be mitigated. This interdisciplinary course lies at the interface of neuro-ethology, ecology, experimental psychology and philosophy. Its aim is to familiarise students with the fundamental concepts of sensory ecology and to initiate reflection on its applications to humans. The course begins with a general presentation of the conceptual framework of sensory ecology, followed by a presentation of the diversity of the visual, auditory, tactile and chemical ‘sensory worlds’ of mammals, birds, amphibians and insects.This presentation is then enriched by a problematisation and philosophical analysis of the questions and concepts of sensory ecology. It concludes with a presentation of ecological issues relating to acoustic, visual and chemical ‘pollution’ of habitats, ‘sensory danger zones’ and ‘ecological traps’.

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. Scientists have dedicated their careers, risked their lives, to explore, discover, understand things that humans shouldn’t give a damn about. To test abstruse theories of visual perception, Isaac Newton inserted a needle between his eye and its socket. Marie Curie slowly, painfully ground tons of uranium ore to isolate less than a gram of a new element with no (then) obvious practical use, slowly killing herself in the process. Albert Einstein endlessly pondered what it would be like to ride alongside a light beam, even though he never believed his discoveries would have any application whatsoever. We will explore the quirks of human psychology generating these bizarre obsessions, without which there could be no science. We will also explain why scientists are so keen on sharing their hard earned knowledge, how they are able to reach a consensus, and why they are funded and trusted by governments and by the public, in spite of their limited understanding of science.

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.

How do we follow a conversation in a busy café, or recognize a long-time favourite melody from a musical piece we may not have heard for years? Even though these feats seem natural and effortless to most of us, the acoustic problems to be solved are dauntingly complex. Starting from a description of the acoustic signal and historical attempts to quantify auditory perception, we will cover classic issues such as detection thresholds, masking, pitch, timbre, to progress towards more recent strands of investigation, such as computational models of auditory processing, the consequences of hearing impairment, scene analysis, neural plasticity, memory, attention, speech coding, and music. The aim of such a broad overview, combined with the personal work from students based on discussions of recent publications, is to become aware of the many open and exciting research questions addressed by the field, and to be technically and conceptually equipped to delve more deeply into some of them if needed.

The objective of this course is to give you the keys to understand the fundamental concepts in visual perception, following a multi-disciplinary approach in neuroscience, psychology and modeling. In neuroscience, the visual system is presented from the processing of the retinal image to the cortex. In the healthy adult human, we present how the visual scene is analyzed in its motion, form, color, and depth, and how attention modulates perception. From a computational point of view, the principles of neuronal coding and decoding are exposed, as well as those underlying inferential perception. Various experimental techniques are exposed, including those using signal processing and those analyzing eye movements such as pupillometry. Finally, the course presents a comparative approach of interspecies vision, from fly to human, as well as a debate on the revolution of deep learning for vision.

What are the characteristics of the human brain that allow the existence and creation of language? How does the environment contribute to its development? Once language networks are stabilized, how do they shape the perception and production of a variety of stimuli? We draw on insights from current and classic research in many disciplines (e.g., linguistic theory & laboratory linguistics, experimental & developmental psychology, neuropsychology, neuroimaging, computational modeling) to shed light on some key psycholinguistic questions ranging from phonology to semantics.

The aim of the course is to address the significance of sign languages in discovering the properties of human ability for language. The course provides a deep understanding of the main issues of sign language linguistics at various levels. A selection of phenomena that are important for understanding the structure of sign languages and their relation to spoken languages is presented and discussed.

While formal semantics has been a success story of contemporary linguistics, it has been narrowly focused on spoken language. Systematic extensions of its research program have recently been explored: beyond spoken language, beyond human language, beyond language proper, and even beyond systems with an overt syntax. First, the development of sign language semantics calls for systems that integrate logical semantics with a rich iconic component. This semantics-with-iconicity is also crucial to understand the interaction between co-speech gestures and logical operators, an important point of comparison for sign languages. Second, several recent articles have proposed analyses of the semantics/pragmatics of animal calls, an important topical extension of semantics. Third, recent research has developed a semantics for music, based in part on insights from iconic semantics. Finally, the methods of formal semantics have newly been applied to concepts, which do not have a syntax that can be directly observed. The overall result is a far broader typology of meaning operations in nature than was available a few years ago. The course will offer a survey of some of these results, with topics that will change from year to year.

Why do some phonological patterns recur across languages, and what can their phonetic and cognitive bases tell us about phonological grammars? This seminar explores how speech perception, articulation, learning, and attention may contribute to shaping phonological patterns. We will examine classic and recent work on phonetically grounded phonology, perceptual and articulatory constraints on contrast, the interaction between prosodic structure and segmental realization, substantive biases in phonological learning, and the mechanisms through which phonetic biases may become phonologized and transmitted over time. A final part of the course will consider how attention and temporal expectations can modulate access to phonological information during online speech perception.

The course is organized around discussion of classic and recent research papers, combining theoretical proposals with experimental evidence from phonetics, psycholinguistics, psychoacoustics, and language learning. Particular attention will be paid to what different kinds of experimental evidence can, and cannot, tell us about phonological representation and grammar.

This is the second module on natural language semantics offered at the Department of Cognitive Studies. We will cover advanced topics in semantics that weren’t covered in Semantics I, and we will revisit topics from Semantics I under a new light. We will focus on the following broad topics.

• Quantification
• Anaphora, in particular dynamic semantics
• Intensional semantics: propositional attitudes, modals, conditionals
• Meaning beyond declaratives: questions and imperatives

Assessment will involve a small number of homework assignments (2 or 3), discussion of primary reading in class, and a term paper.

Note that Semantics I is a prerequisite for this course. If you have not taken Semantics I but would like to take this course, you must contact the instructor as soon as possible to see if this is a possibility.

The content of this course changes every year; this year it will be taught by Pierre Jacob and will focus on:

Human mentalizing: its scope and limits

To mentalize is to attribute a mental state (e.g. a belief, an intention, a desire, an emotion etc.) to self and others, i.e. to form a belief about one’s own or another’s mental state. The cognitive capacity to mentalize has largely been taken for granted by philosophers who have addressed such logical puzzles as referential opacity, the intensionality of belief-ascription and the aspectuality of beliefs. The course will review these philosophical puzzles, but it will pay special attention to the developmental psychological investigation of how the capacity to mentalize arises in human ontogeny (and to some extent in phylogeny). It will examine the central role assigned to the capacity to attribute false beliefs to others. It will address the puzzle raised by discrepant developmental findings and it will compare the various resolutions of this puzzle offered in the past ten or so years. In particular, it will evaluate so-called dual-process (or two-systems) approaches to mentalizing. As the course will also address some of the limits of the human capacity to mentalize, it will highlight the extent to which the human capacity to attribute reasons is beyond the human capacity to mentalize.