The Psychological Record, 2010, 60, 729–734
Book Review
SCHAEKEN, W., VANDIERENDONCK, A., SCHROYENS, W., & D’YDEWALLE, G. (EDS.)
(2007)
The Mental Models Theory of Reasoning: Refinements and Extensions
Mahwah, NJ: Lawrence Erlbaum
pp. vii–224, ISBN 978-0-8058-4183-1
The mental model theory (MMT) is one of the most influential theories of cognition and reasoning. MMT suggests that reasoning occurs via
semantic–­situational models that represent possible states (Johnson-Laird,
1983). Mental models have been used to explain performance in a variety of
areas such as language comprehension (MacWhinney, 2008), analogical reasoning (Gentner, 2002), and deductive reasoning (Johnson-Laird, 2005). For
example, MMT has been used extensively in the investigation of deductive
reasoning as an alternative to rule-based theories. Taking the title of The
Mental Models Theory of Reasoning: Refinements and Extensions as a starting
point for discussion, the following sections outline what I take as both the
refinements and extensions/modifications of MMT. Any change to a theory
must increase its explanatory power and scope yet must not change the
basic axioms of the theory or create novel axioms that modify its essential
tenets (see Kuhn, 1969; Laudan, 1977, for a discussion). In his seminal book,
Mental Models, Johnson-Laird (1983) outlined an extremely ambitious idea
in cognitive science. After such a “revolution,” the next step was the process
of “normal science” in which scientists accumulate empirical results (Kuhn,
1969), which by their very nature cause modifications to the theory. The
Mental Model Theory of Reasoning: Refinements and Extensions describes at
least four refinements to the original theory.
1. The role of working memory and presentation format. MMT suggests
that working memory capacity limits the number of models created
and searched and the amount of information that can be conveyed
within these models. Barrouillet and Grosset (Chapter 1) examine the
influence of working memory on model creation in a developmental
investigation of conditional reasoning. The authors conducted two
experiments that demonstrate that fleshing out models (i.e., creating
veridical models) requires sufficient space to integrate statements
with meanings stored in long-term memory. Schaeken, Van der Henst,
and Schroyens (Chapter 7) demonstrated that participants in their
study attended more to relevant than irrelevant information and
tended to avoid redundant information. These results suggest that
meaning provides critical links to existing information and between
concepts (e.g., causal or temporal relations). Further, these links may
be aided by strategies in which efficient representation of information
730
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(e.g., chun k ing) reduces work ing memory burden. Dierck x and
Vandierendonck (Chapter 6) investigate how the temporal ordering of
stimuli influences reasoning. Their results indicate that inconsistent
orders (i.e., in which the presentation sequence was different than
the temporal sequence) were more difficult to encode and process
than consistent orders because inconsistent orders required the construction of alternative models, a process that is costly for working
memory. These results are similar to previous research demonstrating that presentation formats (e.g., diagrams) reduce working memory
costs by making relations more transparent in model-based representation than propositional representations (Stenning & Oberlander,
1995). Overall, these results suggest that model creation may be
linked to available processing resourses, current knowledge, and reasoning context (more on the latter two below) and do not occur in
a content-independent manner. The results also suggest variation in
how models are created (i.e., strategies) and extend recent research
into the role of model creation and search strategies (see Van der
Henst, Yang, & Johnson-Laird, 2002).
2. The role of content. Because models are created via meaning, a second
critical issue is how meaning and context influence representation.
Because of this focus on meaning (rather than underlying syntactic
structure), MMT often better accounts for performance than theories
outlining propositional representations (e.g., mental logic theory;
Braine & O’Brien, 1998). Byrne (Chapter 3) suggests that the use of
whether indicates a more restrictive set of conditions under which
relations are evaluated compared to if (suggesting that the antecedent may not be necessary). Although provides slightly different restrictive conditions than whether in that although seems to eliminate
many presuppositions from consideration. The results suggest that
the contexts in which people hear and use conditionals in language
exchanges may be very different than the contexts used in experimental investigations. Roberts (Chapter 5) demonstrates that task
construal (defined as how individuals interpret information, e.g., logical terms) influences task solutions. For example, the term some is
often misinterpreted, and this interpretation may change the nature
of the task itself.
3. The number of models created. Early MMT research demonstrated that
increases in problem complexity were associated with increases in the
number of models necessary for a solution (Johnson-Laird, Byrne, &
Schaeken, 1992). Although this certainly holds true under some conditions, it is possible that model creation itself is “costly” and that the
production of a single model may allow reasoners to satisfice rather
than reason through a problem exhaustively. As noted previously,
working memory limitations restrict model creation and search; thus
it is possible that reasoners may use heuristics in the construction
and search of models (Gigerenzer, 2000; see also Schaeken, Van der
Henst, & Schroyens, Chapter 7). The results from chapters by Handley
and Feeney (Chapter 2) and Barrouillet and Grosset (Chapter 1) suggest that reasoners often create a single model during reasoning,
which strongly influences the types of inferences drawn. This finding
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is consistent with earlier research that demonstrated that young children typically create a single model (Sloutsky & Goldvarg, 2004) when
solving a variety of logical propositions (Morris & Sloutsky, 2002).
4. How reasoners establish truth–falsity. How do reasoners determine
whether statements are true or false? Evans, Over, and Handley
(Chapter 4) suggest that the Ramsey test (i.e., Probability[q|p]) is a
better approximation of human conditional performance than an
implicit truth table. The authors suggest that reasoners do not use
unweighted possibilities (i.e., binary truth values) but that possible
states are always linked to their probability. This probability is likely
derived from background knowledge of objects and their relations.
Evans et al. offer the most trenchant suggestions for extension and
revision in the volume and challenge several foundational axioms
of MMT. They suggest that models may encode not only semantic information but also the probability that this information occurs with
other information. Such information may provide the foundations for
relational events (including causal relations) as well as truth conditions for propositions and may point to a possible convergence with
researchers in computational modeling in both the connectionist
(McClelland, 2009) and Bayesian traditions (Tenenbaum, Griffiths, &
Kemp, 2006).
A good theory must also be extended to explain a wide range of phenomena within its domain. Laudan (1977) suggested that one way of evaluating a
scientific theory is to examine its explanatory scope, that is, the number of
problems for which it provides an empirically based explanation. The second
aim of The Mental Models Theory of Reasoning: Refinements and Extensions
is to extend MMT theory into new areas of cognitive science. The book describes three extensions of MMT.
1. The extension of models to include information beyond semantics. Early
cognitive psychologists viewed representations as being purely abstract and amodal (e.g., Chomsky, 1965). However, there has been a
move away from this position toward a view of representations that
has broadened to include grounded information from multiple systems (Barsalou, 2005). Although MMT has never excluded such information, the inclusion of modal information into mental models (e.g.,
Handley & Feeney, Chapter 2) has the potential to increase the explanatory power of MMT. Evans, Over, and Handley (Chapter 4) suggest
that models include information about the probabilities associated
with various words (e.g., if ) and reasoning outcomes. Such information may help reasoners develop expectations for conditions of truth
or falsity and may help to explain common errors in reasoning.
2. New areas of cognition such as causality and probability. Girotto and
Gonzalez (Chapter 8) extend MMT to explain naïve probabilistic reasoning. The authors suggest that although people are relatively poor
at computing formal probabilities (e.g., requiring a formula), they are
surprisingly adept at simple probability judgments (e.g., considering
simple combinations). In an experiment with adults and children, the
authors demonstrate that reasoners set up simple and relatively accurate models of quantities in probability judgments. This finding
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fits well with recent evidence in both the efficacy of simple heuristic
judgments (Todd & Gigerenzer, 2000) and intuitive quantitative judgments (e.g., Dehaene, 1997). Johnson-Laird and Goldvarg-Steingold
(Chapter 9) propose that semantic models of cause and effect can
be achieved by ordering events in time (i.e., Cause A is sufficient for
Effect B, Effect B does not occur before Cause A). The authors suggest that such models of cause would distinguish between relations
and evidence and thus allow an implicit encoding of possibility and
necessity.
3. Training and informal argumentation. Does the process of model creation and representation show the same patterns in informal argumentation and after training? Green (Chapter 10) examines the utility
of model-based representation applied to informal argumentation
and suggests a dual representation of argumentation. One component
is the model for each individual argument, an iterative process that
is updated throughout the course of the argument itself. The second
component is a model of the overall thesis, updated with counterarguments and new information. The process of argumentation is the
change in both representations in real time across the course of the
argument. The results again suggest that minimal model creation
may be common outside of formal reasoning situations.
Can formal reasoning be improved through training? Klauer and
Meiser (Chapter 11) suggest that training can improve reasoning performance; however, not all types of training are equally beneficial.
One important component of successful training is to help reasoners
better comprehend the language of logical reasoning (a point echoed
in Chapter 5 by Roberts). Although training on constructing models
of the premises also appeared to be helpful to participants in a study
by Klauer and Meiser, training on formal syntax did not show similar
improvement and learning was relatively task specific. These chapters suggest that strategies underlying model creation and search
implement the most efficient operations given processing constraints
(e.g., working memory capacity).
Laudan (1977) stated that “the adequacy or effectiveness of individual
theories is a function of how many significant empirical problems they
solve, and how many important anomalies and conceptual problems they
generate” (p. 119). The high-quality work presented in The Mental Models
Theory of Reasoning suggests that MMT is an effective theory. The authors
and editors should be praised for including a large number of new experiments in the volume in addition to high-quality reviews of the literature. The
book is essential for those researching higher-order cognition, particularly
those working in deduction, causality, or probabilistic reasoning.
That said, the volume may have been more effective had it addressed
three issues. One critical question is how MMT should be adapted to novel
findings. For example, are the extensions and refinements presented in this
volume compatible with the basic concept of the theory? If not, do the extensions increase explanatory power at the expense of parsimony? Finally, do
the refinements and extensions add to the theory or do they require prior
premises to be revised or deleted? One indication of the latter point is the
chapter by Evans, Over, and Handley (Chapter 4), in which they challenge
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733
current MMT notions of how reasoners establish truth–falsity. To this end,
the volume might have benefited from a more structured introduction and
conclusion in which these issues could have been addressed. Similarly, although I understand the editorial decision to distribute introductory information across chapters, the book would have been more accessible to nonexperts if the first chapter had presented an overview of MMT. As written, the
book is an excellent addition for those already well-versed in MMT but less
accessible to audiences such as new graduate students.
A second issue is that the volume addresses on ly a small part of
cognition. Although much research in the MMT tradition has focused on
formal reasoning, particularly deductive reasoning, model-based representations are being used in many other areas of research on reasoning.
For example, quantity representations may be model based. Number (and
time) may be represented in models that encode “more or less” rather
than as propositional representations of exact amount (see Mix, Levine,
& Huttenlocher, 2002, for a discussion). Although early writings on MMT
closely tied reasoning and language processing (Johnson-Laird, 1983),
recent work focuses more on reasoning than language. Yet MMT holds
great explanatory potential for language processing unrelated to formal
reasoning. For example, MacWhinney (2008) suggested that model-based
representations of language might better explain embodied representations used in language.
Finally, the book provides little information about how models may be
instantiated neurologically. Although there has been some research examining brain function and model-based reasoning (see Goel, 2005, for a review),
there is little discussion on this topic in this volume. Examining behavior
at multiple levels of analysis may provide new evidence related to recurring
questions (e.g., rules or models in deduction), would undoubtedly sharpen
existing questions, and would likely provoke new issues. For example, recent
research has demonstrated that brain regions active during spatial perception are active when comprehending sentences involving spatial imagery
(Just, Newman, Keller, McEleney, & Carpenter, 2004). Communication with
researchers investigating model-based representations in other cognitive domains would strengthen research in deduction and MMT itself.
These limitations, however, do not diminish the important contributions of this volume. The chapters are well written, filled with trenchant
reviews of extant literature and well-designed experiments. Most important,
the chapters are filled with interesting ideas regarding the nature of human cognition that will stimulate debate and generate ideas for future work.
Therefore, I highly recommend this book.
Bradley J. Morris, Grand Valley State University

References
BARSALOU, L.W. (2005). Abstraction as dynamic interpretation in perceptual
symbol systems. In L. Gershkoff-Stowe & D. Rakison (Eds.), Building
object categories (pp. 389–431). Carnegie Symposium Series. Mahwah, NJ:
Lawrence Erlbaum.
BOOK REVIEWS
734
BRAINE, M. D. S., & O’BRIEN, D. P. (1998). Mental logic. Mahwah, NJ: Lawrence
Erlbaum.
CHOMSKY, N. (1965). Aspects of the theory of syntax. Cambridge, MA: MIT Press.
DEHAENE, S. (1997). The number sense. New York: Oxford University Press.
GENTNER, D. (2002). Mental models, psychology of. In N. J. Smelser & P. B.
Bates (Eds.), International encyclopedia of the social and behavioral
sciences (pp. 9683–9687). Amsterdam: Elsevier Science.
GIGERENZER, G. (2000). Adaptive thinking: Rationality in the real world. New
York: Oxford University Press.
GOEL, V. (2005). Cognitive neuroscience of deductive reasoning. In K.
Holyoak & R. Morrison (Eds.), The Cambridge handbook of thinking &
reasoning (pp. 475–492). Cambridge, MA: Cambridge University Press.
JOHNSON-LAIRD, P. N. (1983). Mental models. Cambridge, MA: Harvard
University Press.
JOHNSON-LAIRD, P. N. (2005). Mental models in thought. In K. Holyoak and
R. J. Sternberg (Eds.). The Cambridge handbook of thinking and reasoning
(pp. 179–212). Cambridge, MA: Cambridge University Press.
JOHNSON-LAIRD, P. N., BYRNE, R. M. J., & SCHAEKEN, W. S. (1992).
Propositional reasoning by model. Psychological Review, 99, 418–439.
JUST, M. A., NEWMAN, S. D., KELLER, T. A., MCELENEY, A., & CARPENTER, P. A.
(2004). Imagery in sentence comprehension: An fMRI study. NeuroImage,
21, 112–124.
KUHN, T. S. (1969). The structure of scientific revolutions. Chicago: University
of Chicago Press.
LAUDAN, L. (1977). Progress and its problems: Towards a theory of scientific
growth. Berkeley, CA: University of California Press.
MACWHINNEY, B. (2008). How mental models encode embodied linguistic
perspectives. In R. Klatzky, B. MacWhinney, & M. Behrmann (Eds.),
Embodied cognition (pp. 365–405). Mahwah, NJ: Lawrence Erlbaum.
MCCLELLAND, J. L. (2009). The place of modeling in cognitive science. Topics
in Cognitive Science, 1, 11–38.
MIX, K., LEVINE, S. C., & HUTTENLOCHER, J. (2002). Quantitative development
in infancy and early childhood. Oxford: Oxford University Press.
MORRIS, B. J., & SLOUTSKY, V. (2002). Children’s solutions of logical vs. empirical
problems: What’s missing and what develops? Cognitive Development,
16(4), 907–928.
SLOUTSKY, V. M., & GOLDVARG, Y. (2004). Mental representation of logical
connectives. Quarterly Journal of Experimental Psychology: Human
Experimental Psychology, 57A(4), 636–665.
STENNING, K., & OBERLANDER, J. (1995). A cognitive theory of graphical and
linguistic reasoning: Logic and implementation. Cognitive Science, 19(1),
97–140.
TENENBAUM, J. B., GRIFFITHS, T. L., & KEMP, C. (2006). Theory-based Bayesian
models of inductive learning and reasoning. Trends in Cognitive Sciences,
10(7), 309–318.
TODD, P. M., & GIGERENZER, G. (2000). Précis of simple heuristics that make
us smart. Behavioral and Brain Sciences, 23, 727–741.
VAN DER HENST, J.-B., YANG, Y., & JOHNSON-LAIRD, P. N. (2002). Strategies in
sentential reasoning. Cognitive Science, 26, 425–468.