More evidence for a dual-process model of conditional reasoning

Abstract

Many studies have shown that the deductive inferences that people make have global properties that reflect the statistical information implicit in the premises. This suggests that such reasoning can be explained by a single, underlying probabilistic model. In contrast, the dual process model of conditional reasoning (Verschueren, Schaeken, & d'Ydewalle, 2005b) proposes that people can use either a logical, counterexample-based strategy or a probabilistic one. In two studies, we presented reasoners with sequences of affirmation-of-the-consequent inferences that differed with respect to the statistical properties of the premises, either explicitly or implicitly. As predicted by the dual-process model, an analysis of individual response patterns showed the presence of two distinct strategies, with use of the counterexample strategy being associated with higher levels of abstract-reasoning competence. Use of the counterexample strategy was facilitated by the explicit presentation of counterexample information. In a further study, we then examined explicitly probabilistic inferences. This study showed that although most reasoners made statistically appropriate inferences, the ability to make more-accurate inferences was associated with higher levels of abstract reasoning competence. These results show that deductive inferential reasoning cannot be explained by a single, unitary process and that any analysis of reasoning must consider individual differences in strategy use.