The Probabilistic Language of Thought

About

Computational modeling of cognition is at the heart of cognitive science. However, our most developed and expensive models, artificial neural networks, struggle to match some striking human skills: few- or zero-shot learning (figuring out a rule from few examples) and manipulating compositionally structured representations and symbols with a rich logical structure. This course focuses on a promising framework to model these human cognitive skills: the probabilistic Language of Thought (LoT). We will start with the philosophical underpinnings of the program in the work of Jerry Fodor. Then, we will discuss recent developments, combining the LoT with probabilistic approaches to learning to develop models of category acquisition across a variety of conceptual domains. This will require a discussion of several technical tools (formal grammars, compositional semantics, Bayesian inference). Finally, we will look at recent promising developments in the field (neurosymbolic learning, the child as a hacker).

Course leader

Fausto Carcassi and Michael Franke

Target group

Students

Fee info