Causal learning by infants and young children: From computational theories to language practices

Abstract

Causal reasoning-the ability to reason about causal relations between events-is fundamental to understanding how the world works. This paper reviews two prominent theories on early causal learning and offers possibilities for theory bridging. Both theories grow out of computational modeling and have significant areas of overlap while differing in several respects. Explanation-Based Learning (EBL) focuses on young infants' learning about causal concepts of physical objects and events, whereas Bayesian models have been used to describe causal reasoning beyond infancy across various concept domains. Connecting the two models offers a more integrated approach to clarifying the developmental processes in causal reasoning from early infancy through later childhood. We further suggest that everyday language practices offer a promising space for theory bridging. We provide a review of selective work on caregiver-child conversations, in particular, on the use of scaffolding language including causal talk and pedagogical questions. Linking the research on language practices to the two cognitive theories, we point out directions for further research to integrate EBL and Bayesian models and clarify how causal learning unfolds in real life. This article is categorized under: Psychology > Learning Cognitive Biology > Cognitive Development.

Keywords: Bayesian models; Explanation‐Based Learning (EBL); causal learning; language practices.