Computational Modeling of Backwards-blocking Reasoning in Human Adults

Benton, Deon T., and David H. Rakison. 2020. “Computational Modeling of Backwards-blocking Reasoning in Human Adults.” PsyArXiv. May 27. doi:10.31234/osf.io/xq8ws

Causal reasoning is a fundamental cognitive ability that enables humans to learn about the complex interactions in the world around them. However, it remains unknown whether causal reasoning is underpinned by a Bayesian mechanism or an associative one. For example, some maintain that a Bayesian mechanism underpins human causal reasoning because it can better account for backward-blocking (BB) and indirect screening-off (IS) findings than certain associative models. However, the evidence is mixed about the extent to which learners engage in both kinds of reasoning. Here, we report an experiment and several computational models that examine to what extent adults engage in BB and IS reasoning using the blicket-detector design. The results revealed that adults’ causal ratings in a backwards-blocking and indirect screening-off condition were consistent with associative rather than a Bayesian computational model. These results are interpreted to mean that adults use associative processes to reason about causal events.