Unlimited Associative Learning as a Null Hypothesis

Abstract

AbstractA common strategy in comparative cognition is to require that one reject associative learning as an explanation for behavior before concluding that an organism is capable of causal reasoning. In this paper, I argue that standard causal-reasoning tasks can be explained by a powerful form of associative learning: unlimited associative learning (UAL). The lesson, however, is not that researchers should conduct more studies to reject UAL, but that they should instead focus on 1) enriching the cognitive hypothesis space and 2) testing a broader range of information processing patterns—errors, biases and limits, rather than successful problem solving alone.