Everyday Conditional Reasoning with Working Memory Preload

Everyday Conditional Reasoning with Working Memory Preload Niki Verschueren (Niki.Verschueren@psy.kuleuven.ac.be) Walter Schaeken (Walter.Schaeken@psy.kuleuven.ac.be) Gery.d’Ydewalle (Gery.d’Ydewalle@psy.kuleuven.ac.be) University of Leuven, Lab of Experimental Psychology, Tiensestraat 102 3000 Leuven – Belgium 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. Introduction There is evidence for a general link between working memory capacity and performance in a range of reasoning tasks (see e.g., Barrouillet, 1996; Gilhooly, Logie, & Wynn, 1999; Kyllonen & Christal, 1990). Previous studies showed that skilled reasoners generally give more normative answers and follow a high demand reasoning strategy (see e.g., Copeland & Radvansky, in press; Gilhooly, Logie, & Wynn, 1999). It is assumed that these normative answers are obtained by an analytic reasoning mechanism that hinges on working memory capacity (Klauer, Stegmaier, & Meiser, 1997; Meiser, Klauer, & Naumer, 2001). The present research continues this line of research and concerns causal conditional reasoning with everyday sentences. Without labeling conclusions as (in)valid, we will investigate how people solve the following two conditional inferences with everyday causal sentences: Modus Ponens (MP) If cause, then effect Cause occurs. Does the effect follow? Affirmation of the Consequent (AC) If cause, then effect Effect occurs. Did the cause precede? Examples of everyday ‘if cause, then effect’ sentences are: If you phone someone, then his telephone rings. If you eat salty food, then you will get thirsty. If someone has a high income, this person will be rich. If a dog has fleas, then it will scratch constantly . Abundant research established that when people reason on everyday conditionals, they spontaneously bring relevant background knowledge into account (for a review see Politzer & Bourmaud, 2002). This contextualization process is characteristic for common- sense reasoning and is responsible for our ability to adaptively cope with everyday situations. The current study focuses on how background knowledge is used for deriving conditional inferences. There are two reasoning mechanisms describing how background information is used during reasoning. First, according to the probabilistic account reasoners derive the probability that the conclusion follows given the categorical premise and use this probability to draw a gradual conclusion (Lui, Lo, & Wu, 1996; Oaksford, Chater, & Larkin, 2002). For MP, reasoners will confine their knowledge base to the situations where the cause occurs. Based on this range of situations they then determine the likelihood that the effect follows. If they can induce that a particular effect always or mostly follows the cause, they conclude that the effect will (probably) follow. The endorsement of MP is thus directly proportional to L(effect|cause). AC is solved in analogy with MP. Reasoners activate all relevant situations where the effect occurs. Within this subset they infer the likelihood that the cause preceded the occurring effect. This likelihood L(cause|effect) directly reflects the AC acceptance rate. According to the second reasoning mechanism the conclusion is attained by taking possible counterexamples into account. There is a strong and reliable effect of the number of available counterexamples on inference acceptance (see e.g., Cummins, Alksnis, Lubart, & Rist, 1991). The mental models theory describes how participants reason with counterexample information (Johnson-Laird & Byrne, 1991; Markovits & Barrouillet, 2002). When given a problem based on a causal rule, for instance, ‘ If you water a plant well, the plant stays green’, reasoners will start by representing the content of the conditional as a possibility: It is possible that a plant is well watered and green. Active consideration of the problem