Everyday Conditional Reasoning with Working Memory Preload

Abstract

There are two accounts explaining how background information can affect the conditional reasoning performance: the probabilistic account and the mental model account. According to the mental model theory reasoners retrieve and integrate counterexample information to attain a conclusion. According to the probabilistic account reasoners base their judgments on likelihood information. It is assumed that reasoning by use of a mental model process requires more working memory resources than solving the inference by use of likelihood information. We report a thinking-aloud experiment designed to compare the role of working memory for the two reasoning mechanisms. It is found that when working memory is preloaded participants use less counterexample information, instead they are more inclined to accept the inference or to use likelihood information. The present results add to the growing evidence showing that working memory is a crucial determinant of reasoning strategy and performance.

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