Associative learning or Bayesian inference? Revisiting backwards blocking reasoning in adults

Abstract

Causal reasoning is a fundamental cognitive ability that enables humans to learn about the complex interactions in the world around them. However, the cognitive mechanisms that underpin causal reasoning are not well understood. For instance, there is debate over whether Bayesian inference or associative learning best captures causal reasoning in human adults. The two experiments and computational models reported here were designed to examine whether adults engage in one form of causal inference called backwards blocking reasoning, whether the presence of potential distractors affects performance, and how adults' ratings align with the predictions of different computational models. The results revealed that adults engaged in backwards blocking reasoning regardless of whether distractor objects are present and that their causal judgements supported the predictions of a Bayesian model but not the predictions of two different associative learning models. Implications of these results are discussed.