Despite a number of studies, it remains unclear whether male and female human newborns manifest different visual preferences or perceptual abilities. The goal of this project is to determine with greater confidence whether this is the case or not. In particular, we are interested in finding out whether male and female newborns show different spontaneous preferences for human faces vs. inanimate objects, as suggested by various studies, old and new (e.g., Lewis et al. 1966; Connellan et al. 2000).

Jury :

•  M. Roberto Casati (Directeur de thèse), EHESS
•  Mme Mariela Aguilera, CONICET
•  M. Anouk Barberousse, Sorbonne Université
•  M. John Kulvicki, Dartmouth College
•  M. François Recanati, College de France

Most cognitive models are domain-specific, meaning that their scope is restricted to a single type of problem. The human mind, on the other hand, does not work like this -- it is a unified system whose processes are deeply intertwined. In this talk, I present our work on building domain-general computational models and using them to understand human cognition. I start by outlining how meta-learning can be used to construct cognitive models across various domains.

Animals use afferent feedback to rapidly correct ongoing movements in the presence of a perturbation. Repeated exposure to a predictable perturbation leads to behavioural adaptation that counteracts its effects. Primary motor cortex (M1) is intimately involved in both processes, integrating inputs from various sensorimotor brain regions to update the motor output. Here, we investigate whether feedback-based motor control and motor adaptation may share a common implementation in M1 circuits.

Dezső Németh's work shows how prefrontal cortex functions can impair predictive processing, notably statistical learning, using developmental data, TMS, and experimental manipulations. 

Jury

• German Sumbre, CNRS research director,  Jury president
• Giovanna Ponte, Researcher, Stazione Zoologica Anton Dohrn, rapporteur
• Georges Debregeas, CNRS research director, rapporteur
• Aurore Avarguès-Weber, CNRS researcher, examinator
• Claire Wyart, CNRS research director, examinator
• Peter Neri, PhD supervisor

Abstract 

8h45 - 9h00  : Registration, coffee

9h00 - 9h15 : Welcome word

9h15 - 10h00 : David Beaver (The University of Texas at Austin)

10h00 - 10h20 : Commentary by Paul Portner (Georgetown University)

Imagination and creativity seem so inextricably intertwined. This strong tie is entrenched in ordinary language and illustrious philosophical theories of the past. Yet, quite surprisingly, contemporary philosophers have paid little attention to it. A possible explanation lies in the received view according to which imagination is neither sufficient nor necessary for creativity.

Several of the most prominent proponents (Hohwy 2013, Clark 2016) of predictive processing claim that predictive processing is particularly well placed to explain imagination : it involves generating the predictive hypothesis in a decoupled manner by turning down the gain on prediction error. As we proposed in Jones and Wilkinson (2020), this conflates imagery and imagination. While predictive processing might be good at explaining imagery, imagery is not sufficient for imagination, nor, arguably, is it even necessary.