Nikola Verschueren

A dual-process specification of causal conditional reasoning

There are two accounts describing causal conditional reasoning: the probabilistic and the mental models account. According to the probabilistic account, the tendency to accept a conclusion is related to the probability by which cause and effect covary. According to the mental models account, the tendency to accept a conclusion relates to the availability of counterexamples. These two accounts are brought together in a dual-process theory: It is argued that the probabilistic reasoning process can be considered as a heuristic process whereas the mental models account can be seen as its analytic counterpart. Experiment 1 showed that the two processes differ on a temporal dimension: The variation in fast responses was best predicted by the variation in likelihood information, while the variation in slow responses was best predicted by variation in counterexample information. Experiments 2 and 3 validate the override principle: The likelihood conclusion can be overwritten when specific counterexamples are retrieved in time. In Experiment 2 both accounts were compared based on their difference in input. In Experiment 3 we used a verbal protocol analysis to validate the dual-process idea at the output level. The data of the three experiments provide converging support for framing the two reasoning accounts in a dual-process theory.

Everyday conditional reasoning: A working memory—dependent tradeoff between counterexample and likelihood use

Considerable evidence has revealed that working memory capacity is an important determinant of conditional reasoning performance. There are two accounts describing the conditional inference process, the probabilistic and the mental models accounts. According to the mental models account, reasoners retrieve and integrate counterexample information to attain a conclusion. According to the probabilistic account, reasoners base their judgments on probabilistic information. It can be assumed that reasoning according to the mental models process would require more working memory resources than would solving the inference on the basis of probabilistic information. By means of a verbal report study, we showed that participants with low working memory capacity more often use probabilistic information, whereas participants with higher working memory capacity are more likely to use counterexample information. Working memory capacity thus not only relates to reasoning performance, it also determines which process reasoners will engage in.

Working Memory Capacity and a Notorious Brain Teaser The Case of the Monty Hall Dilemma

The Monty Hall Dilemma (MHD) is an intriguing example of the discrepancy between peoples intuitions and normative reasoning. This study examines whether the notorious difficulty of the MHD is associated with limitations in working memory resources. Experiment 1 and 2 examined the link between MHD reasoning and working memory capacity. Experiment 3 tested the role of working memory experimentally by burdening the executive resources with a secondary task. Results showed that participants who solved the MHD correctly had a significantly higher working memory capacity than erroneous responders. Correct responding also decreased under secondary task load. Findings indicate that working memory capacity plays a key role in overcoming salient intuitions and selecting the correct switching response during MHD reasoning.

The difference between generating counterexamples and using them during reasoning

The aim of this article is to provide insight into the types of long-term knowledge that are used for solving causal conditional inferences. Two taxonomies were constructed to map the types of counterexample. The available counterexamples are traditionally probed via a counterexample generation task. We observed that there are some significant differences in the types of counterexample retrieved in the reasoning task versus the generation task. The generation task can be used for predicting answers that sprout from a reasoning process that takes counterexample into account, but some participants use a different reasoning process in which the available semantic information is not used as contrasting evidence. Nonetheless, we found that the results of the generation task validly predicted the proportion of inferences accepted as well as the number of counterexamples used during reasoning.

The relevance of selecting what's relevant: A dual process approach to transitive reasoning with spatial relations

The present paper focuses on the heuristic selection process preceding the actual transitive reasoning process. A part of the difficulty of transitive reasoning lies in the selection of the relevant problem aspects. Two experiments are presented using the paradigm introduced by Markovits, Dumas, and Malfait (1995), in which children were asked to make “higher than” inferences about arrays of coloured blocks. In order to discriminate between genuine transitive inference and a simple strategy of relative position, Markovits et al. interspersed white blocks with the coloured blocks, such that the relative position strategy leads to erroneous responses. However, we argue that the white blocks cause confusion due to their ambiguity, which interferes with the heuristic selection process. Two methodological adaptations were introduced, which are hypothesised to facilitate the selection process and improve transitive reasoning: (1) the white blocks were replaced by coloured blocks, and (2) a less abstract context was added to the experimental design. The colour manipulation leads to a clear increase in the use of a transitive strategy by 9-year-old children; 8-year-old children mainly used the relative position strategy. When adding a context story, 9-year-old children used the transitive strategy regardless of the colour of the interspersed blocks. The overall performance of 8-year-olds improved slightly. These results are interpreted as support for a dual-process model of transitive reasoning.