ANR CausaL (2018/2020)

With Andrea Brovelli (INT), Mateus Joffily (GATE)…


Humans have an extraordinary capacity to infer cause-effect relations. In particular, we excel in forming ​beliefs ​about the ​causal effect of actions​. Causal learning provides the basis for rational decision-making and allows people to engage in meaningful life and social interactions. Causal learning is a form of goal-directed learning, defined as the capacity to rapidly learn the consequence of actions and to select behaviours according to goals and motivational state. This ability is based on internal models of the consequence of our behaviors​ and relies on learning rules determined by the​ contingency between actions and outcomes​. At a first approximation, contingency Δ​P ​is operationalized as the difference between two conditional probabilities: i) P(O|A), the probability of outcome O given action A; ii) P(O|¬A), the probability of the outcome when the action is withheld. In everyday life, people perceive their actions as causing a given outcome if the contingency is positive, whereas they perceive them as preventing​ ​it​ ​if​ ​negative;​ ​when​ ​P(O|A)​ ​and​ ​P(O|¬A)​ ​are​ ​equal,​ ​people​ ​report​ ​no​ ​causal​ ​effect​​ ​. Despite the centrality of causal learning, a clear understanding of both the internal computations and neural substrates (the so-called ​cognitive architectures​) is currently missing. ​Our project will therefore address​ ​two​ ​key​ ​questions:

  1. What are the key ​internal representations of causal beliefs and what are the ​computational processes​​ ​that​ ​enable​ ​their​ ​formation​ ​during​ ​learning?

  2. How ​​are ​​internal​ ​representations​ ​and ​​computational​​ processes​ ​​implemented​ ​​in ​​the ​​brain? CausaL​ ​​will​ ​address​ ​these​ ​two​ ​objectives​ ​through​ ​two​ ​dedicated​ ​research​ ​work​ ​packages​ ​(WPs).

Acknowledgement : This work was supported by ANR project ANR-18-AAPG–“CAUSAL, Cognitive Architectures of Causal Learning”.

Laurent U Perrinet
Laurent U Perrinet
Researcher in Computational Neuroscience

My research interests include Machine Learning and computational neuroscience applied to Vision.