Discovering the universal features of human musicality is a prerequisite for explaining the biological and cultural evolution of music. What is universal about the psychological representation of music, and what varies? In this talk I will present analyses of the Natural History of Song Discography, which includes songs recorded in 86 mostly small-scale societies, and experiments using these songs. We find that acoustical forms of songs are predictive of their primary behavioral functions across cultures.

Behaviors and disorders related to problems in self-regulation, such as substance use disorders, childhood behavior problems, and adult antisocial behavior are collectively referred to as Externalizing. In this talk, I will describe research that pooled information on multiple forms of externalizing behavior in ~1.5 million people and identified more than 500 genetic loci associated with a general liability to Externalizing.

This project is based on the training data of French medical students on the SIDE

Pendant que nous rêvons, nous inventons des mondes qui n’ont rien à voir avec le calme de notre chambre à coucher. Il est même plutôt rare que des éléments de notre environnement immédiat soient intégrés à nos rêves. Pour mieux comprendre comment le cerveau se protège des influences extérieures, des chercheurs et chercheuses du CNRS et de l’ENS-PSL ont invité 18 participants à faire une grasse matinée au Centre du sommeil et de la vigilance de l’AP-HP. En effet, le sommeil du matin est riche en rêves.

Every typically developing child learns to talk, but children differ tremendously in how and when they do so. What predicts this variability? And which aspects of early language learning are consistent across the world’s languages and cultures? We use data from tens of thousands of children learning dozens of different languages to create a data-driven picture of what varies and what is more universal in children's early language.

To attend the conference and to receive the zoom details, please conatct clementine.fourrier-eyraud@ens.fr.

Adolescence, defined as 10-24 years, is a period of life often characterised by behaviours that can appear, prima facie, irrational such as dangerous risk-taking and impulsivity. However, these behaviours can be interpreted as adaptive and rational given that a key developmental goal of this period of life is to mature into an independent adult while navigating a social world that is unstable and changing.

Western history of thought abounds with claims that knowledge is valued and sought. Yet people often choose not to know. We call the conscious choice not to seek or use knowledge (or information) deliberate ignorance. Using examples from a wide range of domains, we demonstrate that deliberate ignorance has important functions. We systematize types of deliberate ignorance, describe their functions, discuss their normative desirability, and consider how they can be modeled.

At the Babylab (Laboratoire de Sciences Cognitives et Psycholinguistique), researchers are studying the mechanisms underlying learning from birth to age 5. To do so, they observe children as they play or watch short films adapted to their age and arousing their interest. They then analyse their behaviour, eye movement or brain activity to understand how these complex abilities develop.

Alex Cristia joined the LSCP for the first time in 2009 as a postdoctoral fellow, after obtaining a PhD in linguistics at Purdue University (USA). From 2011 to 2012, she was a researcher at the Max Planck Institute for Psycholinguistics. In 2013 she returned to the LSCP where she held a researcher position at the CNRS.

Thermal soaring is a major natural instance of animal behavior in the presence of complex orientation cues. The problem is deeply rooted in physics and biology, with the prowess by birds constituting a challenge for artificial systems built for technological applications. I will first introduce the natural phenomenology, then review the physics that controls the complexity of the orientation cues, and finally show how machine learning methods are brought to bear on identifying effective flying strategies.