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
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