Mercier S Per Be R Why Do Humans Reason

This is a non copy-edited version of a paper accepted for publication in Behavioral and Brain Sciences
Why do humans reason?
Arguments for an argumentative theory
Hugo Mercier
Philosophy, Politics and Economics Program
University of Pennsylvania
313 Cohen Hall
249 South 36th Street
Philadelphia, PA 19104
hmercier@sas.upenn.edu
http://hugo.mercier.googlepages.com/
Dan Sperber
Institut Jean Nicod (EHESS-ENS-CNRS)
29, rue d’Ulm
75005, Paris
and
Department of Philosophy
Central European University, Budapest
dan@sperber.fr
http://www.dan.sperber.fr
Mercier & Sperber Why do humans reason
Word count
Long abstract: 247; Main text: 17,501; References: 8,821; Document total: 27,714.
Keywords.
Argumentation, Confirmation bias, Decision making, Dual process theory, Evolutionary psychology,
Motivated reasoning, Reason-based choice, Reasoning.
Short abstract (98 words).
Reasoning is generally seen as a means to improve knowledge and make better decisions. However,
much evidence shows that reasoning often leads to epistemic distortions and poor decisions. This
suggests that the function of reasoning should be rethought. Our hypothesis is that the function of
reasoning is argumentative. It is to devise and evaluate arguments intended to persuade. Reasoning
so conceived is adaptive given humans’ exceptional dependence on communication and vulnerability
to misinformation. A wide range of evidence in the psychology of reasoning and decision making can
be reinterpreted and better explained in the light of this hypothesis.
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Long abstract (247 words).
Reasoning is generally seen as a means to improve knowledge and make better decisions. However,
much evidence shows that reasoning often leads to epistemic distortions and poor decisions. This
suggests that the function of reasoning should be rethought. Our hypothesis is that the function of
reasoning is argumentative. It is to devise and evaluate arguments intended to persuade. Reasoning
so conceived is adaptive given the exceptional dependence of humans on communication and their
vulnerability to misinformation. A wide range of evidence in the psychology of reasoning and
decision making can be reinterpreted and better explained in the light of this hypothesis. Poor
performance in standard reasoning tasks is explained by the lack of argumentative context. When
the same problems are placed in a proper argumentative setting, people turn out to be skilled
arguers. Skilled arguers, however, are not after the truth but after arguments supporting their views.
This explains the notorious confirmation bias. This bias is apparent not only when people are actually
arguing but also when they are reasoning proactively from the perspective of having to defend their
opinions. Reasoning so motivated can distort evaluations and attitudes and allow erroneous beliefs
to persist. Proactively used reasoning also favours decisions that are easy to justify but not
necessarily better. In all these instances traditionally described as failures or flaws, reasoning does
exactly what can be expected of an argumentative device: look for arguments that support a given
conclusion, and favour conclusions for which arguments can be found.
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Inference (as the term is most commonly understood in psychology) is the production of new mental
representations on the basis of previously held representations. Examples of inferences are the
production of new beliefs on the basis of previous beliefs, the production of expectations on the
basis of perception, or the production of plans on the basis of preferences and beliefs. So
understood, inference need not be deliberate or conscious. It is at work not only in conceptual
thinking but also in perception and in motor control (Kersten, Mamassian, & Yuille, 2004; Wolpert &
Kawato, 1998). It is a basic ingredient of any cognitive system. ‘Reasoning’, as commonly understood,
refers to a very special form of inference at the conceptual level, where not only is a new mental
representation (or 'conclusion') consciously produced, but the previously held representations (or
‘premises’) that warrant it are also consciously entertained. The premises are seen as providing
reasons to accept the conclusion. Most work in the psychology of reasoning is about reasoning so
understood. Such reasoning is typically human. There is no evidence that it occurs in non-human
animals or in pre-verbal children.1
How do humans reason? Why do they reason? These two questions are mutually relevant, since the
mechanisms for reasoning should be adjusted to its function. While the how-question has been
systematically investigated (e.g., Evans, Newstead, & Byrne, 1993; Johnson-Laird, 2006; Oaksford &
Chater, 2007; Rips, 1994) there is very little discussion of the why-question. How come? It may be
that the function of reasoning is considered too obvious to deserve much attention. According to a
long philosophical tradition, reasoning is what allows the human mind to go beyond mere
perception, habit, and instinct. In the first, theoretical section of this article we sketch a tentative
answer to the how question and then focus on the why-question: We outline an approach to
reasoning based on the idea that the primary function for which it evolved is the production and
evaluation of arguments in communication. In sections 2 to 5, we consider some of the main themes
and findings in the experimental literature on reasoning and show how our approach helps make
better sense of much of the experimental evidence, and hence gains empirical support from it.
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1 Reasoning: Mechanism and function
1.1 Intuitive inference and argument
Since the 1960s, much work in the psychology of reasoning has suggested that, in fact, humans
reason rather poorly, failing at simple logical tasks (Evans, 2002), committing egregious mistakes in
probabilistic reasoning (Kahneman & Tversky, 1972; Tversky & Kahneman, 1983), and being subject
to sundry irrational biases in decision making (Kahneman, Slovic, & Tversky, 1982). This work has led
to a rethinking of the mechanisms for reasoning, but not—or at least not to the same degree—of its
assumed function of enhancing human cognition and decision making. The most important
development has been the emergence of dual process models that distinguishes between intuitions
and reasoning (or system 1 and system 2 reasoning) (Evans, 2007; Johnson-Laird, 2006; Kahneman,
2003; Kahneman & Frederick, 2002, 2005; Sloman, 1996; Stanovich, 2004). Here we outline our own
dual process approach: We contend in particular that the arguments used in reasoning are the
output of a mechanism of intuitive inference (Mercier & Sperber, 2009; Sperber, 1997, 2001).
A process of inference is a process the representational output of which necessarily or
probabilistically follows from its representational input. The function of an inferential process is to
augment and correct the information available to cognitive system. An evolutionary approach
suggests that inferential processes, rather than being based on a single inferential mechanism or
constituting a single integrated ‘system’, are much more likely to be performed by a variety of
domain-specific mechanisms, each attuned to the specific demands and affordances of its domain
(see, e.g., Barkow, Cosmides, & Tooby, 1992). The inferential processes carried out by these
mechanisms are unconscious: they are not mental acts that individuals decide to perform, but
processes that takes place inside their brain, at a “sub-personal” level (in the sense of Dennett,
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1969). People may be aware of having reached a certain conclusion, be aware, that is, of the output
of an inferential process, but we claim that they are never aware of the process itself. All inferences
carried out by inferential mechanisms are in this sense ‘intuitive’. They generate ‘intuitive beliefs’
that is beliefs held without awareness of reasons to hold them.
The claim that all inferential processes carried out by specialized inferential mechanisms are
unconscious and result in intuitive inferences may seem to contradict the common experience of
forming a belief because one has reflected on reasons to accept it and not, or not only, because of its
intuitive force. Such beliefs, held with awareness of one’s reasons to hold them, are better described
not as ‘intuitive’ but as ‘reflective beliefs’ (Sperber, 1997). Our consciously held reason for accepting
a reflective belief may be trust in its source (the professor, the doctor, the priest). Our reasons may
also have to do with the content of the belief: we realize for instance that it would be inconsistent on
our part to hold to our previous beliefs and not accept some given new claim. Far from denying that
we may arrive at a belief through reflecting on our reasons to accept it, we see this as reasoning
proper, the main topic of this article. What characterizes reasoning proper is indeed the awareness
not just of a conclusion but of an argument that justifies accepting that conclusion. We suggest,
however, that arguments exploited in reasoning are the output of an intuitive inferential mechanism.
Like all other inferential mechanisms, its processes are unconscious (as also argued by Johnson Laird,
2006, p. 53 and Jackendoff, 1996) and its conclusions are intuitive. However, these intuitive
conclusions are about arguments, that is, about representations of relationships between premises
and conclusions.
The intuitive inferences made by humans are not only about ordinary objects and events in the
world. They can also be about representations of such objects or events (or even about higher order
representations of representations). The capacity to represent representations, and to draw
inferences about them, is a ‘metarepresentational’ capacity with formal properties relevant to the
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mental computations involved (Recanati, 2000 ; Sperber, 2000b). Several mental mechanisms make
use of this metarepresentational capacity. In particular, humans have a mechanism for representing
mental representations and for drawing intuitive inferences about them. This ‘Theory of Mind’
mechanism is essential to our understanding of others and of ourselves (Leslie, 1987; Premack &
Woodruff, 1978). Humans also have a mechanism for representing verbal representations and for
drawing intuitive inferences about them. This ‘pragmatic’ mechanism is essential to our
understanding of communicated meaning in context (Grice, 1975; Sperber & Wilson, 2002).
We want to argue that there is yet another intuitive metarepresentational mechanism, a mechanism
for representing possible reasons to accept a conclusion—that is, for representing arguments—and
for evaluating their strength. Arguments should be sharply distinguished from inferences. An
inference is a process the output of which is a representation. An argument is a complex
representation. Both an inference and an argument have what can be called a conclusion, but in the
case of an inference, the conclusion is the output of the inference; in the case of an argument, the
conclusion is a part—typically the last part—of the representation. The output of an inference can be
called a ‘conclusion’ because what characterizes an inferential process is that its output is justified by
its input; the way however in which the input justifies the output is not represented in the output of
an intuitive inference. What makes the conclusion of an argument a ‘conclusion’ (rather than simply
a proposition) is the fact that the reasons for drawing this conclusion on the basis of the premises are
(at least partially) spelled out. As Gilbert Harman has justly argued (Harman, 1986), it is a common
but costly mistake to confuse the causally and temporally related steps of an inference with the
logically related steps of an argument. The causal steps of an inference need not recapitulate the
logical step of any argument for it to be an inference, and the logical step of an argument need not
be followed in any inference for it to be an argument.
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Descartes’ famous Cogito argument, “I think therefore I am,” provides an illustration of the manner
in which an argument can be the output of an intuitive inference. Most people believe intuitively that
they exist, and are not looking for reason to justify this belief. But should you look for such reasons,
that is, should you take a reflective stance towards the proposition that you exist, Descartes
argument would probably convince you: It is intuitively evident that the fact that you are thinking is a
good enough reason to accept that you exist, or, in other terms, that it would be inconsistent to
assert “I think” and to deny “I am.”What is not at all obvious in this particular case are the reasons
for accepting that this intuitively good argument is truly a good argument, and philosophers have
been hotly debating the issue (e.g., Katz, 1986 ).
Simple as the Cogito or more complex, all arguments must ultimately be grounded in intuitive
judgments that given conclusions follow from given premises. In other words, we are suggesting that
arguments are not the output of a ‘system 2’ mechanism for explicit reasoning, that would be
standing apart from, and in symmetrical contrast to, a ‘system 1’ mechanism for intuitive inference.
Rather, arguments are the output of one mechanism of intuitive inference among many that delivers
intuitions about premise-conclusion relationships. Intuitions about arguments have an evaluative
component: Some arguments are seen as strong, others as weak. Moreover there may be competing
arguments for opposite conclusions and we may intuitively prefer one to another. These evaluation
and preferences are ultimately grounded in intuition.
If we accept a conclusion because of an argument in its favour that is intuitively strong enough, this
acceptance is an epistemic decision that we take at a personal level. If we construct a complex
argument by linking argumentative steps each of which we see as having sufficient intuitive strength,
this is a personal-level mental action. If we verbally produce the argument so that others will see its
intuitive force and will accept its conclusion, it is a public action that we consciously undertake. The
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mental action of working out a convincing argument, the public action of verbally producing this
argument so that others will be convinced by it, and the mental action of evaluating and accepting
the conclusion of an argument produced by others correspond to what is commonly and traditionally
meant by ‘reasoning’ (a term that can refer to either a mental or a verbal activity).
Why should the reflective exploitation of one mechanism for intuitive inference among many stand
out as so important that it has been seen as what distinguishes humans from beasts? Why, in dual-
process theories of reasoning, should it be contrasted on its own with all the mechanisms for
intuitive inference taken together? We see three complementary explanations for the saliency of
reasoning. First, when we reason, we know that we are reasoning, whereas the very existence of
intuitive inference was seen as controversial in philosophy before its discovery in cognitive science.
Second, while an inferential mechanism that delivers intuitions about arguments is, strictly speaking,
highly domain-specific, the arguments that it delivers intuitions about can be representations of
anything at all. Thus, when we reason on the basis of these intuitions, we may come to conclusions in
all theoretical and practical domains. In other words, even though inferences about arguments are
domain-specific (as evolutionary psychologists would expect), they have domain general
consequences, and provide a kind of virtual domain-generality (without which traditional and dual-
process approaches to reasoning would make little sense). Third, as we will now argue, the very
function of reasoning puts it on display in human communication.
1.2 The function of reasoning
We use ‘function’ here in its biological sense (see Allen, Bekoff, & Lauder, 1998). Put simply, a
function of a trait is an effect of that trait that causally explains its having evolved and persisted in a
population: thanks to this effect, the trait has been contributing to the fitness of organisms endowed
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with it. In principle, several effects of a trait may contribute to fitness, and hence a trait may have
more than a single function. Even then, it may be possible to rank the importance of different
functions, and in particular to identify a function for which the trait is best adapted as its main
function. For instance, human feet have the functions of allowing us both to run and to walk, but
their plantigrade posture is better adapted for walking than for running, and this is strong evidence
that walking is their main function (Cunningham, Schilling, Anders, & Carrier, 2010). In the same vein,
we are not arguing against the view that our reasoning ability may have various advantageous
effects, each of which may have contributed to its selection as an important capacity of the human
mind. We do argue, however, that reasoning is best adapted for its role in argumentation, which
should therefore be seen as its main function.
There have been a few tentative attempts in dual-process approaches to explain the function and
evolution of reasoning. The majority view seems to be that the main function of reasoning is to
enhance individual cognition. This is expressed, for instance, by Kahneman (2003, p. 699), Gilbert
(2002), Evans and Over (1996, p.154), Stanovich (2004, p. 64) and Sloman (1996, p. 18). This classical
view of reasoning—it goes back to Descartes and to Ancient Greek philosophers—faces several
problems that become apparent when its functional claims are laid out in slightly greater detail. It is
sometimes claimed (e.g., by Kahneman, 2003) that the meliorative function of system 2 reasoning is
achieved by correcting mistakes in system 1 intuitions. However, reasoning itself is a potential source
of new mistakes. Moreover, there is considerable evidence that when reasoning is applied to the
conclusions of intuitive inference, it tends to rationalize them rather than to correct them (e.g., Evans
& Wason, 1976).
According to another hypothesis, conscious reasoning “gives us the possibility to deal with novelty
and to anticipate the future” (Evans & Over, 1996, p.154). But giving an organism the possibility to
deal with novelty and to anticipate the future is less a characterization of reasoning than it is of
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learning (or even, it could be argued, of cognition in general). After all, learning can be defined as
“the process by which we become able to use past and current events to predict what the future
holds” (Niv & Schoenbaum, 2008, p. 265). The issue is not whether, on occasion, reasoning can help
correct intuitive mistakes, or better adapt us to novel circumstances. No doubt, it can. The issue is
how far these occasional benefits explain the costs incurred, and hence the very existence of
reasoning among humans, and also explain its characteristic features. In any case, evolutionary
hypotheses are of little help unless they are precise enough to yield testable predictions and
explanations. To establish that reasoning has a given function, we should be able at least to identify
signature effects of that function in the very way reasoning works.
Here we want to explore the idea that the emergence of reasoning is best understood within the
framework of the evolution of human communication. Reasoning allows people to exchange
arguments that, on the whole, make communication more reliable and hence more advantageous.
The main function of reasoning, we claim, is argumentative (Sperber, 2000a, 2001, see also Billig,
1996; Dessalles, 2007; Kuhn, 1992; Perelman & Olbrechts-Tyteca, 1969; Haidt, 2001, and Gibbard,
1990, offer a very similar take on the special case of moral reasoning).
For communication to be stable, it has to benefit both senders and receivers; otherwise they would
stop sending or stop receiving, putting an end to communication itself (Dawkins & Krebs, 1978; Krebs
& Dawkins, 1984). But stability is often threatened by dishonest senders who may gain by
manipulating receivers and inflicting too high of a cost on them. Is there a way to ensure that
communication is honest? Some signals are reliable indicators of their own honesty. Costly signals
such as a deer antlers or a peacock tail both signal and give evidence of the fact that the individual is
strong enough to pay that cost (Zahavi & Zahavi, 1997). Saying “I am not mute” is proof that the
speaker is indeed not mute. However, for most of the rich and varied informational contents that
humans communicate among themselves, there are no available signals that would be proof of their
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own honesty. To avoid being victims of misinformation, receivers must therefore exercise some
degree of what may be called “epistemic vigilance” (Sperber et al., In press). The task of epistemic
vigilance is to evaluate communicator and the content of their messages in order to filter
communicated information.
Several psychological mechanisms may contribute to epistemic vigilance. The two most important of
these mechanisms are trust calibration and coherence checking. People routinely calibrate the trust
they grant different speakers on the basis of their competence and benevolence (Petty & Wegener,
1998). Rudiments of trust calibration based on competence have been demonstrated in 3-year-old
children (Clément, in press; Harris, 2007, for reviews). The ability to distrust malevolent informants
has been shown to develop in stages between the ages of three and six (Mascaro & Sperber, 2009).
The interpretation of communicated information involves activating a context of previously held
beliefs and trying to integrate the new with old information. This process may bring to the fore
incoherencies between old and newly communicated information. Some initial coherence checking
thus occurs in the process of comprehension. When it uncovers some incoherence, an epistemically
vigilant addressee must choose between two alternatives. The simplest is to reject communicated
information, thus avoiding any risk of being misled. This may however deprive the addressee of
valuable information and of the opportunity to correct or update earlier beliefs. The second, more
elaborate alternative consist in associating coherence checking and trust calibration and allow for a
finer grained process of belief revision. In particular, if a highly trusted individual tells us something
that is incoherent with our previous beliefs, some revision is unavoidable: we must revise either our
confidence of the source, or our previous beliefs. We are likely to choose the revision that re-
establishes coherence at the lesser cost, and this will often consist in accepting the information
communicated and in revising our beliefs.
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What are the options of a communicator wanting to communicate a piece of information that the
addressee is unlikely to accept on trust? One option may be for the communicator to provide
evidence of her reliability in the matter at hand (for instance, if the information is about health
issues, she might inform the addressee that she is a doctor). But what if the communicator is not in a
position to boost her own authority? Another option is to try to convince her addressee by offering
premises the addressee already believes or is willing to accept on trust, and showing that, once these
premises are accepted, it would be less coherent to reject the conclusion than to accept it. This
option consists in producing arguments for one’s claims and in encouraging the addressee to
examine, evaluate and accept these arguments. Producing and evaluating arguments is, of course, a
use of reasoning.
Reasoning contributes to the effectiveness and reliability of communication by allowing
communicators to argue for their claim and by allowing addressees to assess these arguments. It
thus increases both in quantity and in epistemic quality the information humans are able to share.
Claiming as we do that this role of reasoning in social interaction is its main function fits well with
much current work stressing the role of sociality in the unique cognitive capacities of humans (Byrne
& Whiten, 1988; R. I. M. Dunbar, 1996; R. I. M. Dunbar & Shultz, 2003; Hrdy, 2009; Humphrey, 1976;
Tomasello, Carpenter, Call, Behne, & Moll, 2005; Whiten & Byrne, 1997). In particular, the
evolutionary role of small group cooperation has recently been emphasized (Dubreuil, In press;
Sterelny, In press). Communication plays an obvious role in human cooperation both in the setting of
common goals and in the allocation of duties and rights. Argumentation is uniquely effective in
overcoming disagreements that are likely to occur, in particular in relatively equalitarian groups.
While there can hardly be any archaeological evidence for the claim that argumentation already
played an important role in early human groups, we note that anthropologists have repeatedly
observed people arguing in small-scale traditional societies (Boehm et al., 1996; D. E. Brown, 1991;
Mercier, submitted-b).
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The main function of reasoning is argumentative: reasoning has evolved and persisted mainly
because it makes human communication more effective and advantageous. As most evolutionary
hypotheses, this claim runs the risk of being perceived as another ‘just so story’. It is therefore crucial
to show that it entails falsifiable predictions. If the main function of reasoning is indeed
argumentative, then it should exhibit as signature effects strengths and weaknesses related to the
relative importance of this function compared to other potential functions of reasoning. This should
be testable through experimental work done here and now. Our goal now is to spell out and explain
what signature effects we predict, to evaluate these predictions in light of the available evidence,
and to see whether they help make better sense of a number of well-known puzzles in the
psychology of reasoning and decision making. Should one fail, on the other hand, to find such
signature of the hypothesized argumentative function of reasoning, and even more should one find
that the main features of reasoning match some other function, then our hypothesis should be
considered falsified.2
Several predictions can be derived from the argumentative theory of reasoning. The first and most
straightforward is that reasoning should do well what it evolved to do, that is, producing and
evaluating arguments (sections 2.1 and 2.2). In general, adaptations work best when they are used to
perform the task they evolved to perform. Accordingly, reasoning should produce its best results
when used in argumentative contexts, most notably in group discussions (section 2.3). When we
want to convince an interlocutor with a different viewpoint, we should be looking for arguments in
favour of our viewpoint rather than in favor of hers. Therefore, the next prediction is that reasoning
used to produces argument should exhibit a strong confirmation bias (section 3). A further related
prediction is that when people reason on their own about one of their opinions, they are likely to do
so proactively, that is, anticipating a dialogic context, and to mostly find arguments that support
their opinion. Evidence of the existence of such ‘motivated reasoning’ will be reviewed in section 4.
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Finally, we want to explore the possibility that even in decision making, the main function of
reasoning is to produce arguments to convince others rather than to find the best decision. Thus, we
predict that reasoning will drive people towards decisions for which they can argue—decisions that
they can justify—even if these decisions are not optimal (section 5).
2 Argumentative skills
2.1 Understanding and evaluating arguments
In this section, we review evidence showing that people are skilled arguers, using reasoning both to
evaluate and to produce arguments in argumentative contexts. This, in itself, is compatible with
other accounts of the main function of reasoning. However, this evidence is relevant because the
idea that people are not very skilled arguers is relatively common; if it were true, then the
argumentative theory would be a non-starter. It is therefore crucial to demonstrate that this is not
the case and that people have good argumentative skills, starting with the ability to understand and
evaluate arguments.
The understanding of arguments has been studied in two main fields of psychology: persuasion and
attitude change, on the one hand, and reasoning, on the other. The aims, methods and results are
different in the two fields. Within social psychology, the study of persuasion and attitude change has
looked at the effects of arguments on attitudes. In a typical experiment participants hear or read an
argument (a ‘persuasive message’) and the evolution of their attitude on the relevant topic is
measured. For instance, in a classic study by Petty and Cacioppo (1979), participants were presented
with arguments supporting the introduction of a comprehensive senior exam. Some participants
heard strong arguments (such as data showing that “graduate and professional schools show a
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preference for undergraduates who have passed a comprehensive exam”), while others heard much
weaker arguments (such as a quote from a graduate student saying that “since they have to take
comprehensives, undergraduates should take them also”). In this experiment, it was shown that
participants who would be directly affected by the setting up of a comprehensive exam were much
more influenced by strong arguments than by weak ones. This experiment illustrates the more
general finding stemming from this literature that, when they are motivated, participants are able to
use reasoning to accurately evaluate arguments (see Petty & Wegener, 1998, for a review).
The demonstration that people are skilled at assessing arguments seems to stand in sharp contrast
with findings from the psychology of reasoning. In a typical reasoning experiment, participants are
presented with premises and asked either to produce or to evaluate a conclusion that should follow
logically. Thus, they may have to determine what, if anything, follows from premises such as “If there
is a vowel on the card, then there is an even number on the card; There is not an even number on
the card”. In such tasks, Evans recognizes that “logical performance *...+ is generally quite poor”
(Evans, 2002, p. 981). To give just one example, it was found in a review that an average of 40% of
participants fail to draw the simple modus tollens conclusion that was used as an example (if p then
q, not q, therefore not p) (Evans et al., 1993). However, reasoning, according to the present view,
should mostly provide a felicitous evaluation in dialogic contexts—when someone is genuinely trying
to convince us of something. This is not the case in these decontextualized tasks that involve no
interaction or in abstract problems. In fact, as soon as these logical problems can be made sense of in
an argumentative context, performance improves. In fact, as soon as these logical problems can be
made sense of in an argumentative context, performance improves. For instance, participants can
easily understand a modus tollens argument when it is of use not simply to pass some test but to
evaluate communicated information (see (V. A. Thompson, Evans, & Handley, 2005); the production
of valid modus tollens arguments in argumentative contexts is also “surprisingly common”
(Pennington & Hastie, 1993, p.155).
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While students of reasoning focus on logical fallacies, other scholars have turned to the study of the
fallacies of argumentation. Unlike logical fallacies, fallacies of argumentation come in degrees:
depending on their content and context, they can be more or less fallacious. For instance, a ‘slippery
slope fallacy’ (where a claim is criticized for being a step on a slope that ends up with a blatant
mistake) is in fact valid to the extent that having made the first step on the slope, it is probable that
one will continue all the way down (Corner, Hahn, & Oakfsord, 2006).
Various experiments have shown that participants are generally able to spot other argumentative
fallacies (Hahn & Oaksford, 2007 experiment 3; Neuman, 2003; Neuman, Weinstock, & Glasner,
2006; Weinstock, Neuman, & Tabak, 2004; see also Corner & Hahn, 2009). Not only do they spot
them, but they tend to react appropriately: rejecting them when they are indeed fallacious or being
convinced to the degree that they are well grounded (Corner et al., 2006; Hahn & Oaksford, 2007;
Hahn, Oaksford, & Bayindir, 2005; Oaksford & Hahn, 2004; Rips, 2002). When researchers have
studied other skills specific to argumentation, performance has proved to be satisfactory. Thus
participants are able to recognize the macrostructure of arguments (Ricco, 2003), to follow the
commitments of different speakers (Rips, 1998), and to appropriately attribute the burden of proof
(Bailenson & Rips, 1996, see also Rips, 1998, experiment 3). On the whole, the results reviewed in
this section demonstrate that people are good at evaluating arguments both at the level of individual
inferences and at the level of whole discussions.
2.2 Producing arguments
The first studies that systematically investigated argument production used the following
methodology.3 Participants were asked to think about a given topic, such as “Would restoring the
military draft significantly increase America's ability to influence world events?” (Perkins, 1985) or
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“What are the causes of school failure?” (Kuhn, 1991). After being left to think for a few minutes,
they had to state and defend their view to the experimenter. The conclusions of these studies were
quite bleak, and highlighted three main flaws. The first is that people resort to mere explanations
(‘make sense’ causal theories) instead of relying on genuine evidence (data) to support their views.
However, later research has shown that this is mostly an artefact of the lack of evidence available to
the participants: when evidence is made available, participants will favour it (in both production and
evaluation) (Brem & Rips, 2000; see also Hagler & Brem, 2008; Sá, Kelley, Ho, & Stanovich, 2005). A
second flaw noted by Perkins and Kuhn is the relative superficiality of the arguments used by
participants. This can be explained by a feature of the tasks: unlike in a real debate, the experimenter
didn’t challenge the arguments of the participants, however weak they were. In a normal
argumentative setting, a good argument is an argument that is not refuted. As long as they are not
challenged, it makes sense to be satisfied with seemingly superficial arguments. On the other hand,
people should be able to generate better arguments when engaged in a real debate. This is exactly
what Kuhn and her colleagues observed: participants who had to debate on a given topic showed a
significant improvement in the quality of the arguments they used afterwards (Kuhn, Shaw, & Felton,
1997, see Blanchette & Dunbar, 2001, for similar results with analogical reasoning).
The third flaw, according to Perkins and Kuhn, is the most relevant one here. Participants had
generally failed to anticipate counter-arguments and generate rebuttals. For these two authors, and
indeed the critical thinking tradition, this is a very serious failing. Seen from an argumentative
perspective, however, this may not be a simple flaw, but rather a feature of argumentation that
contributes to its effectiveness in fulfilling its function. If one’s goal is to convince others, one should
be looking first and foremost for supportive arguments. Looking for counter-arguments against one’s
own claims may be part of a more sophisticated and effortful argumentative strategy geared to
anticipating the interlocutor’s response, but in the experimental setting, there was no back-and-forth
to encourage such an extra effort (and participants knew not to expect such a back and forth). If this
18
is a correct explanation of what need not be a flaw after all, then the difficulty people seem to have
in coming up with counter-arguments should be easily overcome by having them challenge someone
else’s claims rather than defending their own. Indeed, when mock jurors were asked to reach a
verdict and were then presented with an alternative verdict, nearly all of them were able to find
counter-arguments against it (Kuhn, Weinstock, & Flaton, 1994). In another experiment all
participants were able to find counter-arguments against a claim (which was not theirs), and to do so
very quickly (Shaw, 1996).
When people have looked at reasoning performance in felicitous argumentative settings, they have
observed good results. Resnick and her colleagues created groups of three participants who
disagreed on a given issue (Resnick, Salmon, Zeitz, Wathen, & Holowchak, 1993). Analyzing the
debates, the researchers were “impressed by the coherence of the reasoning displayed. Participants
*…+ appear to build complex arguments and attack structure. People appear to be capable of
recognizing these structures and of effectively attacking their individual components as well as the
argument as a whole” (pp. 362-3, see also Blum-Kulka, Blondheim, & Hacohen, 2002; Hagler & Brem,
2008; Stein, Bernas, & Calicchia, 1997; Stein, Bernas, Calicchia, & Wright, 1995; It is worth noting that
a strikingly similar pattern emerges from developmental studies, see Mercier, submitted-a)
To sum up, people can be skilled arguers, producing and evaluating arguments felicitously. This good
performance stands in sharp contrast with the abysmal results found in other, non argumentative,
settings, a contrast made particularly clear by the comparison between individual and group
performance.
2.3 Group reasoning
19
If people are skilled at both producing and evaluating arguments, and if these skills are displayed
most easily in argumentative settings, then debates should be especially conducive to good
reasoning performance. Many types of tasks have been studied in group settings, with very mixed
results (see Kerr, Maccoun, & Kramer, 1996; Kerr & Tindale, 2004 for recent reviews4). The most
relevant findings here are those pertaining to logical or, more generally, intellective tasks “for which
there exists a demonstrably correct answer within a verbal or mathematical conceptual system”
(Laughlin & Ellis, 1986, p.177). In experiments involving this kind of task, participants in the
experimental condition typically begin by solving problems individually (pre-test), then solve the
same problems in groups of 4 or 5 members (test), and then solve them individually again (post-test),
to make sure that any improvement does not come simply from following other group members.
Their performance is compared to those of a control group of participants who take the same tests,
but always individually. Intellective tasks allow for a direct comparison with results from the
individual reasoning literature, and the results are unambiguous. The dominant scheme (Davis, 1973)
is truth wins, meaning that as soon as one participant has understood the problem, she will be able
to convince the whole group that her solution is correct (B. L. Bonner, Baumann, & Dalal, 2002;
Laughlin & Ellis, 1986; Stasson, Kameda, Parks, Zimmerman, & Davis, 1991). 5 This can lead to big
improvements in performance. Some experiments using the Wason selection task dramatically
illustrate this phenomenon (Moshman & Geil, 1998; see also Augustinova, 2008; Maciejovsky &
Budescu, 2007). The Wason selection task is the most widely used task in reasoning, and the
performance of participants is generally very poor, hovering around 10% of correct answers (Evans,
1989; Evans et al., 1993; Johnson-Laird & Wason, 1970). However, when participants had to solve the
task in groups they reached the level of 80% of correct answers.
Several challenges can be levelled against this interpretation of the data. It could be suggested that
the person who has the correct solution simply points it out to the others, who immediately accept it
without argument, perhaps because they have recognized this person as the ‘smartest’ (Oaksford,
20
Chater, & Grainger, 1999). The transcripts of the experiments show that this is not the case: most
participants are only willing to change their mind once they have been thoroughly convinced that
their initial answer was wrong (see for instance Moshman & Geil, 1998; Trognon, 1993). More
generally, many experiments have shown that debates are essential to any improvement of
performance in group settings (see Schulz-Hardt, Brodbeck, Mojzisch, Kerschreiter, & Frey, 2006 for a
review and some new data, and Mercier, submitted-a, for similar evidence in the development and
education literature). Moreover, in these contexts, participants decide that someone is smart based
on the strength and relevance of her arguments, and not the other way around (Littlepage &
Mueller, 1997). Indeed, it would be very hard to tell who is “smart” in such groups—even if general
intelligence were easily perceptible, it only correlates .33 with success in the Wason selection task
(Stanovich & West, 1998). Finally, in many cases, no single participant had the correct answer to
begin with. Several participants may be partly wrong and partly right, but the group will collectively
be able to retain only the correct parts and thus converge on the right answer. This leads to the
‘assembly bonus effect’ in which the performance of the group is better than that of its best member
(Blinder & Morgan, 2000; Laughlin et al., 2002; Laughlin et al., 2006; Laughlin et al., 2003;
Lombardelli, Proudman, & Talbot, 2005; Michaelsen, Watson, & Black, 1989b; Sniezek & Henry, 1989;
Stasson et al., 1991; Tindale & Sheffey, 2002). Once again there is a striking convergence here with
the developmental literature showing how groups—even when no member had the correct answer
initially—can facilitate learning and comprehension of a wide variety of problems (Mercier,
submitted-a).
According to another counter-argument, people are simply more motivated, generally, when they
are in groups (Oaksford et al., 1999). This is not so.6 On the contrary, “The ubiquitous finding across
many decades of research (e.g., see Hill, 1982; Steiner, 1972, is that groups usually fall short of
reasonable potential productivity baselines” (Kerr & Tindale, 2004, p.625). Moreover, other types of
motivation have no such beneficial effect on reasoning. By and large, monetary incentives, even
21
substantial ones, fail to improve performance in reasoning and decision making tasks (Ariely, Gneezy,
Loewenstein, & Mazar, In Press; S. E. Bonner, Hastie, Sprinkle, & Young, 2000; S. E. Bonner &
Sprinkle, 2002; Camerer & Hogarth, 1999; and see Johnson-Laird & Byrne, 2002, and Jones & Sugden,
2001, in the specific case of the Wason selection task). Thus, not any incentive will do: group settings
have a motivational power to which reasoning responds specifically.7
The argumentative theory also helps predict what will happen in non-optimal group settings. If all
group members share an opinion, a debate should not arise spontaneously. However, in many
experimental and institutional settings (juries, committees), people are forced to discuss, even if they
already agree. When all group members agree on a certain view, each of them can find arguments in
its favour. These arguments will not be critically examined, let alone refuted, thus providing other
group members with additional reasons to hold that view. The result should be a strengthening of
the opinions held by the group (see Sunstein, 2002 for a review, and Hinsz, Tindale, & Nagao, 2008,
for a recent illustration). Contra Sunstein’s ‘law of group polarization’, it is important to bear in mind
that this result is specific to artificial contexts in which people debate even though they tend to agree
in the first place. When group members disagree, discussions often lead to depolarisation (Kogan &
Wallach, 1966; Vinokur & Burnstein, 1978). In both cases, the behaviour of the group can be
predicted on the basis of the direction and strength of the arguments accessible to group members,
as demonstrated by research carried out in the framework of the Persuasive Argument Theory
(Vinokur, 1971), which ties up with the prediction of the present framework (Ebbesen & Bowers,
1974; Isenberg, 1986; Kaplan & Miller, 1977; Madsen, 1978).
The research reviewed in this section shows that people are skilled arguers: they can use reasoning
both to evaluate and to produce arguments. This good performance offers a striking contrast with
the poor results obtained in abstract reasoning tasks. Finally, the improvement in performance
22
observed in argumentative settings confirms that reasoning is at its best in these contexts. We will
now explore in more depth a phenomenon already mentioned in this section: the confirmation bias.
3 The confirmation bias: A flaw of reasoning or a feature of argument production?
The confirmation bias consists in the “seeking or interpreting of evidence in ways that are partial to
existing beliefs, expectations, or a hypothesis in hand” (Nickerson, 1998, p.175). It is one of the most
studied biases in psychology (see Nickerson, 1998 for review). While there is some individual
variation, it seems that everybody is affected to some degree, irrespective of factors like general
intelligence or open mindedness (Stanovich & West, 2007, 2008a, 2008b). For standard theories of
reasoning, the confirmation bias is no more than a flaw of reasoning. For the argumentative theory,
however, it is a consequence of the function of reasoning and hence a feature of reasoning when
used for the production of arguments.
In fact, we suggest, the label ‘confirmation bias’ has been applied to two distinct types of case, both
characterized by a failure to look for counter-evidence or counter-arguments to an existing belief,
both consistent with the argumentative approach, but brought about in different ways. In cases that
deserve the label ‘confirmation bias’, people are trying to convince others. They are typically looking
for arguments and evidence to confirm their own claim, and ignoring negative arguments and
evidence unless they anticipate having to rebut them. While this may be seen as a bias from a
normative epistemological point of view, it clearly serves the goal of convincing others. In another
type of case, we are dealing not with biased reasoning but with an absence of reasoning proper. Such
an absence of reasoning is to be expected when people already hold some belief on the basis of
perception, memory or intuitive inference, and do not have to argue for it. Say, I believe that my keys
are in my trousers because this is where I remember putting them. Time has passed and they could
now be in my jacket, for instance. However, unless I have some positive reason to think otherwise, I
23
just assume that they are still in my trousers, and I don’t even make the inference (which, if I am
right, would be valid) that they are not in my jacket or any of the other places where, in principle,
they might be. In such cases, people typically draw positive rather than negative inferences from
their previous beliefs. These positive inferences are generally more relevant to testing these beliefs.
For instance, I am more likely to get conclusive evidence that I was right or wrong by looking for my
keys in my trousers rather than in my jacket (even if they turn out not to be in my jacket, I might still
be wrong in thinking that they are in my trousers). We spontaneously derive positive consequences
from our intuitive beliefs. This is just a trusting use of our beliefs, not a confirmation bias (see
Klayman & Ha, 1987).
The theory we are proposing makes three broad predictions. The first is that the genuine
confirmation bias (as opposed to straightforward trust in one’s intuitive beliefs and their positive
consequences) should occur only in argumentative situations. The second is that it should occur only
in the production of arguments. The rationale for a confirmation bias in the production of arguments
to support a given claim does not extend to the evaluation of arguments by an audience that is just
aiming to be well informed. The third prediction is that the confirmation bias in the production of
arguments is not a bias in favour of confirmation in general and against disconfirmation in general: it
is a bias in favour of confirming one's own claims, which should be naturally complemented by a bias
in favour of disconfirming opposing claims and counterarguments.
3.1 Hypothesis testing: No reasoning, no reasoning bias
One of the areas in which the confirmation bias has been most thoroughly studied is that of
hypothesis testing, often using Wason’s rule discovery task (Wason, 1960). In this task, participants
are told that the experimenter has in mind a rule for generating number triples and that they have to
discover it. The experimenter starts by giving participants a triple that conforms to the rule (2, 4, 6).
24
Participants can then think of a hypothesis about the rule and test it by proposing a triple of their
own choice. The experimenter says whether or not this triple conforms to the rule. Participants can
repeat the procedure until they feel ready to put forward their hypothesis about the rule. The
experimenter tells them whether or not their hypothesis is true. If it is not, they can try again or give
up.
Participants overwhelmingly propose triples that fit with the hypothesis they have in mind. For
instance, if a participant has formed the hypothesis “three even numbers in ascending order”, she
might try 8, 10, 12. As argued by (Klayman & Ha, 1987), such an answer corresponds to a ‘positive
test strategy’ of a type that would be quite effective in most cases. This strategy is not adopted in a
reflective manner, but is rather, we suggest, the intuitive way to exploit one's intuitive hypotheses,
as when we check that our keys are where we believe we left them, as opposed to checking that they
are not where it follows from our belief that they should not be. What we see here, then, is a sound
heuristic rather than a bias.
This heuristic misleads participants in this case only because of some very peculiar (and expressly
designed) features of the task. What is really striking is the failure of attempts to get participants to
reason in order to correct their ineffective approach. It has been shown that even when instructed to
try and falsify the hypotheses they generate, fewer than one participant in 10 is able to do so
(Poletiek, 1996; Tweney et al., 1980). Since the hypotheses are generated by the participants
themselves, this is what we should expect in the current framework: the situation is not an
argumentative one, and does not activate reasoning. However, if a hypothesis is presented as coming
from someone else, it seems that more participants will try to falsify it, and they will give it up much
more readily in favour of another hypothesis (Cowley & Byrne, 2005). The same applies if the
hypothesis is generated by a minority member in a group setting (Butera, Legrenzi, Mugny, & Pérez,
25
1992). So falsification is accessible provided that the situation encourages participants to argue
against a hypothesis that is not their own.
3.2 The Wason selection task
A similar interpretation can be used to account for results obtained with the Wason selection task
(Wason, 1966). In this task, participants are given a rule describing four cards. In the original version,
the cards have a number on one side and a letter on the other, although only one side is visible—
they might see, for instance, 4, E, 7 and K. The rule might read: “if there is a vowel on one side, then
there is an even number on the other side.” The task is to say what cards need to be turned over in
order to determine whether the rule is true. In this task, too, it is useful to distinguish the effects of
intuitive mechanisms from those of reasoning proper (as has long been suggested by Wason and
Evans, 1975). Intuitive mechanisms involved in understanding utterances will draw the participants’
attention to the cards that are made most relevant by the rule and the context (Girotto,
Kemmelmeier, Sperber, & Van der Henst, 2001; Sperber, Cara, & Girotto, 1995). In the standard case,
these will simply be the cards mentioned in the rule (the vowel, E, and the even number, 4), as
opposed to those that would yield the correct answer (the E and the 7). Given that the 4 can only
confirm the rule but not falsify it, the behaviour of participants who select this card could be
interpreted as showing a confirmation bias. However, as first discovered by Evans (Evans & Lynch,
1973), the simple addition of a negation in the rule (“if there is a vowel on one side, then there is not
an even number on the other side”) leaves the answers unchanged (the E and 4 are still made
relevant), but in this case these cards correspond to the correct, falsifying, response. So these
intuitive mechanisms are not intrinsically linked to either confirmation or falsification: they just
happen to point to cards that in some cases might confirm the rule and, in other cases, might falsify
it.
26
Confirmation bias does occur in the selection task, but at another level. Once the participants’
attention has been drawn to some of the cards and they have arrived at an intuitive answer to the
question, reasoning is used not to evaluate and correct their initial intuition, but to find justifications
for it (Evans, 1996; Lucas & Ball, 2005; Roberts & Newton, 2002). This is a genuine confirmation bias.
As with hypothesis testing, this does not mean that participants are simply unable to understand the
task or to try to falsify the rule—only that an appropriate argumentative motivation is lacking. That
participants can understand the task is shown by the good performance in group settings, mentioned
above. Participants should also be able to try and falsify the rule when their first intuition is that the
rule is false and they want to prove it wrong. Researchers have used rules such as “all members of
group A are Y”, where Y is a negative or positive stereotype (Dawson, Gilovich, & Regan, 2002).
Participants who were most motivated to prove the rule wrong—those belonging to group A when Y
was negative—were able to produce more than 50% of correct answers, whereas participants from
all the other conditions (groups other than A and/or positive stereotype) remained under 20%.
3.3 Categorical syllogisms
Categorical syllogisms are one of the most studied types of reasoning. Here is a typical example: “No
C are B; All B are A; Therefore some A are not C”. Although they are solvable by very simple programs
(see for instance (Geurts, 2003), syllogisms can be very hard to figure out—the one just offered by
way of illustration, for instance, is solved by less than 10% of participants (Chater & Oaksford, 1999).
In terms of the mental model theory, what the participants are doing is constructing a model of the
premises and deriving a possible conclusion from it (Evans, Handley, Harper, & Johnson-Laird, 1999).
This constitutes the participants’ initial intuition. In order to correctly solve the problem, participants
should then try to construct counterexamples to this initial conclusion. But this would mean trying to
falsify their own conclusion. The present theory predicts that they will not do so spontaneously. And
indeed, “any search for counterexample models is weak *…+ participants are basing their conclusions
27
on the first model that occurs to them” (Evans et al., 1999, p. 1505, see also Klauer, Musch, &
Naumer, 2000; Newstead, Handley, & Buck, 1999).
Again, we suggest, this should not be interpreted as revealing a lack of ability, but only a lack of
motivation. When participants want to prove a conclusion wrong, they will find ways to falsify it.
This happens with normal conclusions presented by someone else (Sacco & Bucciarelli, 2008), or
when participants are faced with so-called ‘unbelievable’ conclusions such as “All fish are trout”. In
this case, they will try to prove that the premises lead to the logical opposite of the conclusion (“Not
all fish are trout”) (Klauer et al., 2000). Given that falsification leads to better answers on these tasks,
this explains why participants actually perform much better when the conclusion is unbelievable (see
for instance (Evans, Barston, & Pollard, 1983). It is not that they reason more in this case—they
spend as much time trying to solve problems with believable conclusions as with unbelievable ones
(V. A. Thompson, Striemer, Reikoff, Gunter, & Campbell, 2005). It is just that the direction reasoning
takes is mostly determined by the participants’ initial intuitions. If they have arrived at the conclusion
themselves, or if they agree with it, they try to confirm it. If they disagree with it, they try to prove it
wrong. In all cases, what they do is try to confirm their initial intuition.
3.4 Rehabilitating the confirmation bias
In all three cases reviewed above—hypothesis testing, the Wason selection task, and syllogistic
reasoning— a similar pattern can be observed. Participants have intuitions that lead them towards
certain answers. If reasoning is used at all, it is mostly used to confirm these initial intuitions. This is
exactly what one should expect of an argumentative skill, and so these results bolster our claim that
the main function of reasoning is argumentative. By contrast, if people were easily able to abstract
from this bias, or if they were subject to it only in argumentative settings, then this would constitute
evidence against the present theory.
28
According to a more standard explanation of the confirmation bias, it is an effect of limitations in
cognitive resources, and in particular in working memory (e.g. Johnson-Laird, 2006). But it is hard to
reconcile this explanation with the fact that people are very good at falsifying propositions when
they are inclined to disagree with them. In those cases, people are not held back by limited
resources, even though the tasks are not cognitively easier.
However, the idea that the confirmation bias is a normal feature of reasoning that plays a role in the
production of arguments may seem surprising in light of the poor outcomes it has been claimed to
cause. Conservatism in science is one example (see Nickerson, 1998, and references therein).
Another is the related phenomenon of groupthink, which has been held responsible for many
disasters, from the Bay of Pigs fiasco (Janis, 1982), to the tragedy of the Challenger shuttle (Esser &
Lindoerfer, 1989; Moorhead, Ference, & Neck, 1991) (see Esser, 1998, for review). In such cases,
reasoning tends not to be used in its normal context, i.e. the resolution of a disagreement through
discussion. When one is alone or with people who hold similar views, one’s arguments will not be
critically evaluated. This is when the confirmation bias is most likely to lead to poor outcomes.
However, when reasoning is used in a more felicitous context, that is, in arguments among people
who disagree but have a common interest in the truth, the confirmation bias contributes to an
efficient form of division of cognitive labour.
When a group has to solve a problem, it is much more efficient if each individual looks mostly for
arguments supporting a given solution. They can then present these arguments to the group, to be
tested by the other members. This method will work as long as people can be swayed by good
arguments, and the results reviewed in section 2 show that this is generally the case. This joint
dialogic approach is much more efficient than one where each individual on his or her own has to
carefully examine all possible solutions.8 The advantages of the confirmation bias are even more
29
obvious given that each participant in a discussion is often in a better position to look for arguments
in favour of his or her favoured solution (situations of asymmetric information). So group discussions
provide a much more efficient way of holding the confirmation bias in check. By contrast, the
teaching of critical thinking skills, which is supposed to help us overcome the bias on a purely
individual basis, does not seem to yield very good results (Ritchart & Perkins, 2005; Willingham,
2008).
For the confirmation bias to play an optimal role in discussions and group performance, it should be
active only in the production of arguments and not in their evaluation. Of course, in the back-and-
forth of a discussion, the production of one’s own arguments and the evaluation of those of the
interlocutor may interfere with one another, making it hard to properly assess the two processes
independently. Still, the evidence reviewed in section 2.1 on the understanding of arguments
strongly suggests that people tend to be more objective in evaluation than in production. If this were
not the case, the success of group reasoning reviewed in section 2.3 would be very hard to explain.
4 Proactive reasoning in belief formation
According to the argumentative theory, reasoning is most naturally used in the context of an
exchange of arguments during a discussion. But people can also be proactive and anticipate
situations in which they might have to argue to convince others that their claims are true, or that
their actions are justified. We would say that much reasoning anticipates the need to argue. In this
section, we will show that work on motivated reasoning can be usefully reinterpreted in this
perspective, and in the next section, we will show that the same applies to work on reason-based
choice.
30
Many of our beliefs are likely to remain unchallenged because they are relevant only to ourselves
and we don’t share them, or because they are uncontroversial among the people we interact with, or
because we have sufficient authority to be trusted when we assert them. While we think of most of
our beliefs—to the extent that we think about them at all—not as ‘beliefs’ but just as pieces of
knowledge, we are also aware that some of them are unlikely to be universally shared, or to be
accepted on trust just because we express them. When we pay attention to the contentious nature
of these beliefs, we typically think of them as 'opinions'. Opinions are likely to be challenged, and
may have to be defended. It makes sense to look for arguments for our opinions before we find
ourselves called upon to state them. If the search for arguments is successful, we will be ready. If not,
then perhaps it might be better to adopt a weaker position, one that is easier to defend. Such uses of
reasoning have been intensively studied under the name of motivated reasoning9 (Kunda, 1990, see
also Kruglanski & Freund, 1983; Pyszczynski & Greenberg, 1987, and Molden & Higgins, 2005 for a
recent review).
4.1 Motivated reasoning
A series of experiments by Ditto and his colleagues, involving reasoning in the context of a fake
medical result, illustrate the notion of motivated reasoning (Ditto & Lopez, 1992; Ditto, Munro,
Apanovitch, Scepansky, & Lockhart, 2003; Ditto, Scepansky, Munro, Apanovitch, & Lockhart, 1998).
Participants had to put some saliva on a strip of paper and were told that if the strip changed colour,
or did not change colour, depending on the condition, this would be an indication of an unhealthy
enzyme deficiency. Participants, being motivated to believe they were healthy, tried to garner
arguments for this belief. In one version of the experiment, participants were told the rate of false
positives, which varied across conditions. The use they made of this information reflects motivated
reasoning. When the rate of false positives was high, participants who were motivated to reject the
conclusion used it in order to undermine the validity of the test. This same high rate of false positives
31
was discounted by participants who were motivated to accept the conclusion. In another version of
the experiment, participants were asked to mention events in their medical history that could have
affected the results of the test, which gave them an opportunity to discount these results.
Participants motivated to reject the conclusion listed more such events, and the number of events
listed was negatively correlated with the evaluation of the test. In these experiments, the very fact
that the participant’s health is being tested indicates that it cannot be taken for granted. The
reliability of the test itself is being discussed. This experiment, and many others to be reviewed
below, also demonstrates that motivated reasoning is not mere wishful thinking (a form of thinking
that, if it were common, would in any case be quite deleterious to fitness, and would not be coherent
with the present theory). If desires did directly affect beliefs in this way, then participants would
simply ignore or dismiss the test. Instead, what they do is look for evidence and arguments to show
that they are healthy, or at least for reasons to question the value of the test.
Other studies have demonstrated the use of motivated reasoning to support various beliefs that
others might challenge. Participants dig in, and occasionally alter their memories to preserve a
positive view of themselves (Dunning, Meyerowitz, & Holzberg, 1989; M. Ross, McFarland, &
Fletcher, 1981; Sanitioso, Kunda, & Fong, 1990). They modify their causal theories to defend some
favored belief (Kunda, 1987). When they are told the outcome of a game on which they had made a
bet, they use events in the game to explain why they should have won when they lost (Gilovich,
1983). Political experts use similar strategies to explain away their failed predictions and bolster their
theories (Tetlock, 1998). Reviewers fall prey to motivated reasoning and look for flaws in a paper
when they don’t agree with its conclusions, in order to justify its rejection (Koehler, 1993; Mahoney,
1977). In economic settings, people use information in a flexible manner so as to be able to justify
their preferred conclusions, or arrive at the decision they favour (Boiney, Kennedy, & Nye, 1997;
Hsee, 1995, 1996a; Schweitzer & Hsee, 2002).
32
All these experiments demonstrate that people sometimes look for reasons to justify an opinion they
are eager to uphold. From an argumentative point of view, they do this not to convince themselves
of the truth of their opinion but to be ready to meet the challenges of others. If they find themselves
unprepared to meet such challenges, they may become reluctant to express an opinion they are
unable to defend, and less favourable to the opinion itself, but this is an indirect individual effect of
an effort that is aimed at others. In a classical framework, where reasoning is seen as geared to
achieving epistemic benefits, the fact that it may be used to justify an opinion already held is hard to
explain, especially since, as we will now show, motivated reasoning can have dire epistemic
consequences.
4.2 Consequences of motivated reasoning
4.1.1 Biased evaluation and attitude polarization
In a landmark experiment, Lord and colleagues asked participants who had been previously selected
as being either defenders or opponents of the death penalty to evaluate studies relating to its
efficiency as a deterrent (Lord, Ross, & Lepper, 1979). The studies given to the participants had
different conclusions: while one seemed to show that the death penalty had a significant deterrent
effect, the other yielded the opposite result. Even though the methodologies of the two studies were
almost identical, the studies that yielded a conclusion not in line with the participants’ opinions were
consistently rated as having been much more poorly conducted. In this case, participants used
reasoning not so much to objectively assess the studies as to confirm their initial views by finding
either flaws or strengths in similar studies, depending on their conclusion. This phenomenon is
known as biased assimilation or biased evaluation. This second description is somewhat misleading.
In this experiment—and the many related experiments that have followed it—participants are
indeed asked to evaluate an argument. However, what they do is mostly produce arguments to
33
support or rebut the argument they are evaluating, depending on whether they agree with its
conclusion or not. Participants are not trying to form an opinion: they already have one. Their goal is
argumentative rather than epistemic, and ends up being pursued at the expense of epistemic
soundness. That participants engage in this biased search for arguments even when their task is to
evaluate an argument has been demonstrated by the experiments we now describe.
Several other experiments have studied the way people evaluate arguments depending on whether
they agree or disagree with the conclusions. When people disagree with the conclusion of an
argument, they often spend more time evaluating it (Edwards & Smith, 1996). This asymmetry arises
from the trivial fact that rejecting what we are told generally requires some justification, whereas
accepting it does not. Moreover, the time spent on these arguments is mostly devoted to finding
counterarguments (Edwards & Smith, 1996); see also (Brock, 1967; Cacioppo & Petty, 1979; Eagly,
Kulesa, Brannon, Shaw, & Hutson-Comeaux, 2000). Participants tend to comb through arguments for
flaws, and end up finding some, whether they are problems with the design of a scientific study
(Klaczynski & Gordon, 1996b; Klaczynski & Narasimham, 1998; Klaczynski & Robinson, 2000), issues
with a piece of statistical reasoning (Klaczynski & Gordon, 1996a; Klaczynski, Gordon, & Fauth, 1997;
Klaczynski & Lavallee, 2005), or argumentative fallacies (Klaczynski, 1997). In all these cases,
motivated reasoning leads to a biased assessment: arguments with unfavoured conclusions are rated
as less sound and less persuasive than arguments with favoured conclusions.
Sometimes the evaluation of an argument is biased to the point where it has an opposite effect to
the one intended by the arguer: on reading an argument with a counter-attitudinal conclusion (one
that goes against their own beliefs or preferences), interlocutors may find so many flaws and
counter-arguments that their initial unfavourable attitude is in fact strengthened. This is the
phenomenon of attitude polarization, which has been extensively studied since it was first
demonstrated by (Lord et al., 1979 see also Greenwald, 1969; Pomerantz, Chaiken, & Tordesillas,
34
1995). 10 Taber and Lodge have demonstrated that, in the domain of politics, attitude polarization is
most easily observed in participants who are most knowledgeable (Taber & Lodge, 2006), see also
(Braman, 2009; Redlawsk, 2002). Their knowledge makes it possible for these participants to find
more counter-arguments, leading to more biased evaluations.
4.1.2 Polarization, bolstering and overconfidence
Attitude polarization can also occur in simpler circumstances. Merely thinking about an object may
be enough to strengthen attitudes towards it (polarization). This phenomenon has been repeatedly
demonstrated. Sadler and Tesser (1973) had participants listen to a recording of a very pleasant or
unpleasant-sounding individual. They then had to give their opinion of this individual, either after
having to think about him or her, or after performing a distraction task. As expected, the opinions
were more extreme (in both directions) when participants had to think about the individual. Tesser
and Conlee (1975) showed that polarization increases with the time spent thinking about an item,
and Jellison and Mills (1969) showed that it increases with the motivation to think. As in the case of
polarization following biased evaluation, such polarization occurs only when participant are
knowledgeable (Tesser & Leone, 1977, see also Millar & Tesser, 1986). And the effect can be
mitigated by providing a ‘reality check’: the simple presence of the target object will dramatically
decrease polarization (Tesser, 1976).
Some later experiments used a slightly different methodology (Chaiken & Yates, 1985; Liberman &
Chaiken, 1991). Instead of simply thinking about the target object, participants had to write a small
essay about it. Not only was polarization observed in this case, but it was correlated with the
direction and number of the arguments put forward in the essay. These results demonstrate that
reasoning contributes to attitude polarization and strongly suggest that it may be its main factor.
When people are asked to think about a given item towards which they intuitively have a positive or
35
negative attitude, what happens, we suggest, is that they reflect less on the item itself than on how
to defend their initial attitude. Many other experiments have shown that once people have formed
an attitude to a target, they will look for information that supports this attitude (a phenomenon
known as selective exposure, see Hart et al., In press; S. M. Smith, Fabrigar, & Norris, 2008) and try to
put any information they are given to the same use (Bond, Carlson, Meloy, Russo, & Tanner, 2007;
Brownstein, 2003), which leads them to choose inferior alternatives (Russo, Carlson, & Meloy, 2006).
According to the argumentative theory, reasoning should be even more biased once the reasoner has
already stated her opinion, thereby increasing the pressure on her to justify it rather than moving
away from it. This phenomenon is called bolstering (W. J. McGuire, 1964). Thus, when participants
are committed to an opinion, thinking about it will lead to a much stronger polarization (Lambert,
Cronen, Chasteen, & Lickel, 1996; Millar & Tesser, 1986). Accountability (the need to justify one’s
decisions) will also increase bolstering (Tetlock, Skitka, & Boettger, 1989, see Lerner & Tetlock, 1999,
for review).
Finally, motivated reasoning should also have effects on confidence. When participants think of an
answer to a given question, they will be spontaneously tempted to generate reasons supporting that
answer. This may then cause them to be overconfident in the answer. Koriat and his colleagues
(Koriat, Lichtenstein, & Fischhoff, 1980) have tested this hypothesis using general knowledge
questions such as “the Sabines were part of (a) ancient India or (b) ancient Rome.” After answering
the question, participants had to produce reasons relevant to their answers. Some participants were
asked to generate reasons supporting their answer, while others were asked for reasons against it.
The results for people who were explicitly asked to generate reasons supporting their answer were
no different from those in a control condition where no reasons were asked for. This suggests that
thinking of reasons to support their answer is what people spontaneously do anyhow when they
regard their answer not as an obvious piece of knowledge but as an opinion that might be
36
challenged. By contrast, participants in the other group were much less overconfident. Having to
think of arguments against their answer allowed them to see its limitations, something they would
not do on their own (see Arkes, Guilmette, Faust, & Hart, 1988; Davies, 1992; Griffin & Dunning,
1990; Hirt & Markman, 1995; Hoch, 1985; Yates, Lee, & Shinotsuka, 1992) for replications and
extensions to the phenomenon of hindsight bias and the fundamental attribution error). It is then
easy to see that overconfidence would also be reduced by having participants discuss their answers
with people who favour different conclusions.
4.1.3 Belief perseverance
Motivated reasoning can also be used to hang on to beliefs even when they have been proved to be
ill-founded. This phenomenon, known as belief perseverance, is “one of social psychology’s most
reliable phenomena” (Guenther & Alicke, 2008, p.706, see L. Ross, Lepper, & Hubbard, 1975, for an
early demonstration). The involvement of motivated reasoning in this effect can be demonstrated by
providing participants with evidence both for and against a favoured belief. If belief perseverance
were a simple result of some degree of psychological inertia, then the first evidence presented
should be the most influential, whether it supported or disconfirmed the favoured belief. On the
other hand, if evidence can be used selectively, then only evidence supporting the favoured belief
should be retained, regardless of the order of presentation. (Guenther & Alicke, 2008) tested this
hypothesis in the following way. Participants first had to perform a simple perceptual task. This task,
however, was described as testing for ‘mental acuity’, a made-up construct that was supposed to be
related to general intelligence, making the results of the test highly relevant to participant’s self-
esteem. Participants were then given positive or negative feedback, but a few minutes later they
were told that the feedback was actually bogus and the real aim of the experiment was explained. At
three different points, the participants also had to evaluate their performance: right after the task,
37
after the feedback, and after the debriefing. In line with previous results, the participants who had
received positive feedback showed a classic belief perseverance effect and discounted the debriefing,
which allowed them to preserve a positive view of their performance. By contrast, those who had
received negative feedback did the opposite: they took the debriefing fully into account, which
allowed them to reject the negative feedback and restore a positive view of themselves. This strongly
suggests that belief perseverance of the type just described is an instance of motivated reasoning
(see (Prasad et al., 2009) and (Nyhan & Reifler, In prep.) for applications to the domain of political
beliefs).11
4.1.4 Violation of moral norms
The results reviewed so far have shown that motivated reasoning can lead to poor epistemic
outcomes. We will now see that our ability to “find or make a reason for everything one has a mind
to do” (Franklin, 1799) can also allow us to violate our moral intuitions and behave in unfair ways. In
a recent experiment, Valdesolo and DeSteno (2008) have demonstrated the role reasoning can play
in maintaining moral hypocrisy (when we judge someone else’s action using tougher moral criteria
than we use to judge our own actions). Here is the basic setup. On arriving at the laboratory,
participants were told that they would be performing one of two tasks: a short and fun task or a long
and hard task. Moreover, they were given the possibility of choosing which task they would be
performing, knowing that the other task would be assigned to another participant. They also had the
option of letting a computer choose at random how the tasks would be distributed. Once they were
done assigning the tasks, participants had to rate how fair they had been. Other participants, instead
of having to make the assignment themselves, were at the receiving end of the allocation and had no
choice whatsoever; they had to rate the fairness of the participant who had done the allocation,
knowing the exact conditions under which this had been done. It is then possible to compare the
fairness ratings of participants who have assigned themselves the easy task with the ratings of those
38
who have been assigned the hard task. The difference between these two ratings is a mark of moral
hypocrisy. The authors then hypothesized that reasoning, since it allows participants to find excuses
for their behaviour, was responsible for this hypocrisy. They tested this hypothesis by replicating the
above conditions with a twist: the fairness judgments were made under cognitive load, which made
reasoning close to impossible. This had the predicted result: without the opportunity to reason, the
ratings were identical, and showed no hint of hypocrisy.
This experiment is just one illustration of a more general phenomenon. Reasoning is often used to
find justifications for performing actions that are otherwise felt to be unfair or immoral (Bandura,
1990; Bandura, Barbaranelli, Caprara, & Pastorelli, 1996; Bersoff, 1999; Crandall & Eshleman, 2003;
Dana, Weber, & Kuang, 2007; Diekmann, Samuels, Ross, & Bazerman, 1997; Haidt, 2001; Mazar,
Amir, & Ariely, In prep; Moore, Clark, & Kane, 2008; Snyder, Kleck, Strenta, & Mentzer, 1979; and
Gummerum, Keller, Takezawa, & Mata, 2008, for children). Such uses of reasoning can have dire
consequences. Perpetrators of crimes will be tempted to ‘blame the victim’, or find other excuses in
order to mitigate the effects of violating their moral intuitions (Ryan, 1971; see Hafer & Begue, 2005,
for a review), which can in turn make it easier to commit new crimes (Baumeister, 1997). This view of
reasoning dovetails with recent theories of moral reasoning that see it mostly as a tool for
communication and persuasion (Gibbard, 1990; Haidt, 2001; Haidt & Bjorklund, 2007).
These results raise a problem for the classical view of reasoning. In all these cases, reasoning does
not lead to more accurate beliefs about an object, to better estimates of the correctness of one’s
answer, or to superior moral judgments. Instead, by looking only for supporting arguments,
reasoning strengthens people’s opinions, distorts their estimates, and allows them to get away with
violations of their own moral intuitions. In these cases, epistemic or moral goals are not well served
by reasoning. By contrast, argumentative goals are: people are better able to support their positions
or to justify their moral judgments.
39
5 Proactive reasoning in decision making
In the previous section, we have argued that much reasoning is done in anticipation of situations
where an opinion might have to be defended, and we have suggested that work on motivated
reasoning can be fruitfully reinterpreted in this light. It is not just opinions that may have to be
defended: people may also have to put forward arguments to defend their decisions and actions, and
they may reason proactively to that end. We want to argue that this is the main role of reasoning in
decision making. This claim stands in sharp contrast to the classical view that reasoning about
possible options and weighing up their pros and cons is the most reliable way—if not the only
reliable way—to arrive at sound decisions (Janis & Mann, 1977; Kahneman, 2003; Simon, 1955). This
classical view has in any case been vigorously challenged in much recent research. Some argue that
the best decisions are based on intuition and made in split seconds (see for instance (Klein, 1998), a
view rendered popular by Gladwell (2005). Others maintain that the solution lies with the
unconscious, and advise us to ‘sleep on it’ (Claxton, 1997; Dijksterhuis, 2004; Dijksterhuis, Bos,
Nordgren, & van Baaren, 2006; Dijksterhuis & van Olden, 2006). We briefly review these challenges
to the classical view before considering the substantial literature on reason-based choice and
interpreting it in the light of the argumentative theory of reasoning.
5.1 To what extent does reasoning help in deciding?
In an initial series of studies, Wilson and his colleagues looked at the effect of reasoning on the
consistency between attitudes and behaviour (Wilson, Dunn, Bybee, Hyman, & Rotondo, 1984;
Wilson, Kraft, & Dunn, 1989; Wilson & LaFleur, 1995; see also Koole, Dijksterhuis, & Van
Knippenberg, 2001; Millar & Tesser, 1989; Sengupta & Fitzsimons, 2000; Sengupta & Fitzsimons,
40
2004; and Wilson, Dunn, Kraft, & Lisle, 1989, for review). The basic paradigm is as follows.
Participants are asked to state their attitude to a given object. In one condition, they have to provide
reasons for these attitudes. It has been consistently observed that attitudes based on reasons were
much less predictive of future behaviours (and often not predictive at all) than attitudes stated
without recourse to reasons. This lack of correlation between attitude and behaviour resulting from
too much reasoning can even lead participants to form intransitive preferences (Lee, Amir, & Ariely,
2008).
Using similar paradigms in which some participants are asked for reasons, it was found that providing
reasons led participants to choose items that they were later less satisfied with (Wilson et al., 1993)
or that were less in line with the ratings of experts (McMackin & Slovic, 2000; Wilson & Schooler,
1991). Participants got worse at predicting the results of basketball games (Halberstadt & Levine,
1999). People who think too much are also less likely to understand other people’s behaviour
(Albrechtsen, Meissner, & Susa, 2009; Ambady, Bernieri, & Richeson, 2000; Ambady & Gray, 2002).
This stream of experiments was later followed up by Dijksterhuis and his colleagues, who introduced
a modified paradigm. Here, participants are given lists of features describing different items (such as
flats, cars, etc.) designed in such a way that some items have more positive features. In the baseline
condition, participants had to say which item they preferred immediately after they had been
exposed to these features. In the conscious thought condition, they were left to think about the
items for a few minutes. Finally, in the unconscious thought condition, participants spent the same
amount of time doing a distraction task. Across several experiments it was found that the best
performance was obtained in this last condition: unconscious thought was superior to conscious
thought (and to immediate decision) (Dijksterhuis, 2004; Dijksterhuis et al., 2006; Dijksterhuis, Bos,
van der Leij, & van Baaren, 2009; Dijksterhuis & van Olden, 2006).
41
However, some of Dijksterhuis’ results have proven hard to replicate (Acker, 2008; Newell, Wong,
Cheung, & Rakow, In Press; Thorsteinson & Withrow, 2009), and alternative interpretations have
been proposed in some cases (Lassiter, Lindberg, Gonzalez-Vallejo, Bellezza, & Phillips, 2009). In a
meta-analysis of this literature, Acker observed that only in a few experiments was unconscious
thought significantly superior to conscious thought (Acker, 2008), amounting to a null result when all
the experiments were taken into account. Even so, there was no significant advantage of conscious
thought over immediate choice. This is typically the kind of situation where, according to classical
theories, reasoning should help: a new choice has to be made, with the options well delimited and
the pros and cons exposed. It is therefore quite striking that reasoning (at least for a few minutes)
does not bring any advantage and is sometimes inferior to intuitive, unconscious processes. Finally,
studies of decision making in natural environments converge on similar conclusions: not only are
most decisions made intuitively, but when conscious decision making strategies are used, they often
result in poor outcomes (Klein, 1998). In the next sub-section, we will explore a framework designed
to explain such findings by showing that reasoning pushes people not towards the best decisions but
towards decisions that are easier to justify.
5.2 Reason-based choice
Starting in the late eighties, a group of leading researchers in decision making developed the
framework of reason-based choice (Shafir, Simonson, & Tversky, 1993) provides an early review).
According to this theory, people often make decisions because they can find reasons to support
them. These reasons will not favour the best decisions, or decisions that satisfy some criterion of
rationality, but decisions that can be easily justified and are less at risk of being criticized. According
to the argumentative theory, this is what should happen when people are faced with decisions where
they only have weak intuitions. In this case, reasoning can be used to tip the scales in favour of the
42
choice for which reasons are most easily available. One will then at least be able to defend the
decision if its outcome proves unsatisfactory.
Reason based choice is well illustrated in a landmark article by Simonson (Simonson, 1989) in which
he studied, in particular, the attraction effect (Huber, Payne, & Puto, 1982, see Briley, Morris, &
Simonson, 2000, for a cross-cultural variation). The attraction effect occurs when, given a set of two
equally valuable alternatives, a third alternative is added that is just as good as another one of the
first alternatives on one trait, but inferior on the second trait. This addition tends to increase the rate
of choice of the dominating option, in a manner not warranted by rational models. Here is one
example used in Simonson’s experiments. Participants had to choose between packs of beer that
varied along the two dimensions of price and quality. Beer A was of lower quality than beer B, but
was also cheaper, and the two attributes balanced in such a way that both beers were regularly
chosen in a direct comparison. However, some participants had to choose between these two beers
plus beer C, which was more expensive than beer B but not better. When this beer was introduced,
participants tended to pick beer B more often. It is easy to account for this finding within the
framework of reason-based choice: the poorer alternative makes the choice of the dominating one
easy to justify (“Beer B is of the same quality as but cheaper than this other beer!”). To confirm this
intuition, Simonson made and tested the three following predictions: (i) a choice based on reasons
should be reinforced when participants have to justify themselves; (ii) a choice based on reasons will
be perceived as easier to justify and less likely to be criticized; and (iii) a choice based on reasons
should give rise to more elaborate explanations. The results of three experiments supported these
predictions. Moreover, these results also showed that participants who made choices based on
reasons tended to make choices that fitted less well with their own preferences as stated before the
choice was made. Finally, another set of experiments demonstrated that when participants were
able to use their intuitions more, because they were familiar with the alternatives or because the
descriptions of these alternatives were more detailed, they were less prone to the attraction effect
43
(Ratneshwar, Shocker, & Stewart, 1987). Several well known challenges to the view of humans as
making rational decision thanks to their reasoning abilities have been, or can be, reinterpreted as
cases of reason-based choice.
5.3 What reason-based choice can explain
5.3.1 Disjunction effect
The ‘sure thing principle’ (Savage, 1954) states that when someone favours A over B if event E
happens, and keeps the same preference ordering if E does not happen, then her choices should not
be influenced by any uncertainty about the occurrence of E. Shafir and Tversky have recorded several
violations of this principle (Shafir & Tversky, 1992; Tversky & Shafir, 1992). For instance, we can
compare the reaction of participants to the following problems (Tversky & Shafir, 1992):
Win / lose versions
Imagine that you have just played a game of chance that gave you a 50% chance to win $200
and a 50% chance to lose $100. The coin was tossed and you have either won $200 or lost
$100. You are now offered a second identical gamble: 50% chance to win $200 and 50%
chance to lose $100. Would you?: (a) accept the second gamble. (b) reject the second
gamble.
Whether they have won or lost in the first gamble, a majority of participants accept the second
gamble. However, they are likely to do so for different reasons: in the win scenario, they reason that
they can easily risk losing half of the $200 they have just won; in the lose scenario, however, they
might take the second gamble as an opportunity to make up for their previous loss. In these two
cases, while the choice is the same, the reasons for making it are incompatible. Thus, when
44
participants do not know what is going to be the outcome of the first bet, they have more trouble
justifying the decision to accept the second gamble: the reasons seem to contradict each other. As a
result, a majority of participants who do not know the result of the first gamble reject the second
gamble, even though they would have accepted it whatever the result of the first gamble. The
authors further tested this explanation by devising a comparison that had the same properties as the
one above, except for the fact that the reasons for making the ‘accept’ decision were the same
irrespective of the outcome of the first gamble. In this case, participants made exactly the same
choices whether or not they knew the result of the first gamble (see Croson, 1999, for a similar
experiment with a variant of the prisoner’s dilemma).
5.3.2 Sunk costs fallacy
The sunk cost fallacy is the “greater tendency to continue an endeavor once an investment in money,
effort, or time has been made” (Arkes & Blumer, 1985, p.124). A well known real life example is that
of the Concorde: the British and French governments decided to keep paying for a plane that they
knew would never turn a profit. Arkes and Ayton have argued that such mistakes result from an
unsatisfactory use of explicit reasons such as ‘do not waste’ (Arkes & Ayton, 1999). We will briefly
review the evidence they presented, and add some more.
First of all, Arkes and Ayton contrast the robust sunk cost effects observed in humans (Arkes &
Blumer, 1985; Garland, 1990; Staw, 1981) with the absence of such mistakes among animals.12 They
also point out that children do not seem prone to this error (see Klaczynski & Cottrell, 2004;
Morsanyi & Handley, 2008, for more recent, convergent evidence). If reasoning were not the cause
of this phenomenon but the cure for it, the opposite would be expected. Finally, some experiments
have varied the availability of justifications—a factor that should not be relevant for standard models
of decision making. Thus, when participants can justify the waste, they are less likely to be trapped
45
by sunk costs (Soman & Cheema, 2001). By contrast, when participants find it harder to justify
changing their course of actions, they are more likely to commit the fallacy (J. D. Bragger, Hantula,
Bragger, Kirnan, & Kutcher, 2003; J. L. Bragger, Bragger, Hantula, & Kirnan, 1998).
5.3.3 Framing
Framing effects occur when people give different answers to structurally similar problems depending
on their wording—their ‘frame’ (Tversky & Kahneman, 1981). Our intuitions are generally blamed for
these effects (Kahneman, 2003). Another explanation that can be seen as either complementary or
alternative to this one is that different frames make some reasons more or less available, thus
modifying the way reasoning affects our decisions. Several results support this interpretation (see
also (McKenzie, 2004; McKenzie & Nelson, 2003). First, as mentioned above, participants who reason
more about the tasks are more influenced by framing effects (Igou & Bless, 2007). Second, when
groups make decisions on framed problems, the groups tend to converge on the answer that is
supported by the strongest reasons (T. W. McGuire, Kiesler, & Siegel, 1987; Milch, Weber, Appelt,
Handgraaf, & Krantz, 2009; Paese, Bieser, & Tubbs, 1993). If the participants’ answers were truly
based on their intuitions, the answer proposed by the group would tend to be the mean of these
different intuitions (Allport, 1924; Farnsworth & Behner, 1931). Instead, these findings have to be
explained within the framework of the Persuasive Argument Theory (Vinokur, 1971; Vinokur &
Burnstein, 1978), showing that the decisions are based on reasons.
5.3.4 Preference inversion
The ability to evaluate preferences correctly is necessary for economic models of decision making.
But preferences can vary dramatically depending on the way they are measured. Someone may rate
A higher than B and still choose B over A (Bazerman, Loewenstein, & White, 1992; Irwin, Slovic,
46
Lichtenstein, & McClelland, 1993; Kahneman & Ritov, 1994; Slovic, 1975; Tversky, Sattath, & Slovic,
1988). For instance, the relative rating of two objects can vary, or even be reversed, depending on
whether they are rated separately or jointly (Hsee, 1996b, 1998; Hsee, Loewenstein, Blount, &
Bazerman, 1999). Thus, when the following two objects are presented in isolation—a music
dictionary with 10.000 entries that is ‘like new’, and one with 20.000 entries and a torn cover—,
people rate the one with 10.000 entries more highly. However, when people have to choose
between the two, they favour the one that has more entries, despite the torn cover (Hsee, 1996b).
Such effects fit perfectly in the current framework: people choose an alternative because they can
provide “a compelling argument for choice that can be used to justify the decision to oneself as well
as to others” (Tversky et al., 1988, p.372). In the example above, people lack reliable intuitions—they
cannot tell how many entries a good music dictionary should have. Lacking such intuitions, they fall
back on reasoning and let their judgments be guided by ease of justification—in this case, the
condition of the dictionary that easily justifies a high or low price. On the other hand, dimensions
with numerical values will often provide compelling justifications when options are presented jointly.
This bias can lead to suboptimal decisions (Hsee & Zhang, 2004).
More generally, “decision-makers have a tendency to resist affective influence, and to rely on
rationalistic attributes to make their decisions” (Hsee, Zhang, Yu, & Xi, 2003, p.16, see also E. M.
Okada, 2005). Indeed, ‘rationalistic’ attributes make for easy justifications. For instance, in one
experiment participants had either to choose between the following two options or to rate them: a
roach shaped chocolate weighing two ounces and worth two dollars, and a heart shaped chocolate
weighing half an ounce and worth 50 cents (Hsee, 1999). A majority (68%) of participants choose the
roach shaped chocolate, even though more than half (54%) thought they would enjoy the other
more. The participants who chose the bigger, roach shaped chocolate did it because the feeling of
disgust, being ‘irrational’, was hard to justify, especially compared to the difference in price and size.
47
However, in light of the results from the psychology of disgust (e.g., Rozin, Millman, & Nemeroff
1986), we can tell that their choice was certainly the wrong one.
5.3.5 Other inappropriate uses of reasons
Many other inappropriate uses of reasons have been empirically demonstrated. Investors’ decisions
are guided by reasons that seem good but are unrelated to real performance (Barber, Heath, &
Odean, 2003). People will use a rule such as ‘more variety is better’, or ‘don’t pick the same things as
others’ to guide their decisions, even when less variety or more conformity would actually be more in
line with their preferences (Ariely & Levav, 2000; Berger & Heath, 2007; Simonson, 1990). Use of a
rule such as ‘don’t pay for delays’ will lead to behaviours that go against one’s own interest (Amir &
Ariely, 2003). When forecasting their affective states, people rely on explicit lay theories (Igou, 2004),
theories that will often lead them astray (Hsee & Hastie, 2006). Because ‘it’s better to keep options
open’, people will be reluctant to make an unalterable decision even when they would be better off
making it (Gilbert & Ebert, 2002). When indulging in a hedonic act, people feel they need a reason for
such indulgence, even though this does not actually change the quality of the experience (Xu &
Schwarz, In press). Reason-based choice has also been used to explain effects related to loss
aversion (Simonson & Nowlis, 2000), the effect of attribute balance (Chernev, 2005), the tendency to
be overwhelmed by too much choice (Scheibehenne, Greifeneder, & Todd, 2009; Sela, Berger, & Liu,
In press), the feature creep effect (D. V. Thompson, Hamilton, & Rust, 2005), the endowment effect
(E. J. Johnson, Haubl, & Keinan, 2007), aspects of time discounting (Weber et al., 2007) and several
other departures from the norms of rationality (Shafir et al., 1993).
Another sign that reason-based choice can lead to non-normative outcomes is that sometimes
reasons that are not relevant to the decision will nonetheless play a role. For instance, the same
irrelevant attribute will sometimes be used as a reason for choosing an item (Carpenter, Glazer, &
48
Nakamoto, 1994), and sometimes as a reason for rejecting it (Simonson, Carmon, & O'Curry, 1994;
Simonson, Nowlis, & Simonson, 1993), depending on what decision it makes easier to justify (C. L.
Brown & Carpenter, 2000). People will also be influenced by irrelevant pieces of information because
they find it hard to justify ignoring them (Tetlock & Boettger, 1989; Tetlock, Lerner, & Boettger,
1996).
All of these experiments demonstrate cognitively unsound uses of reasoning. There are two ways to
explain these findings. One could argue that these are instances of a mechanism designed for
individual cognition, and in particular for decision making, that sometimes gets misused. According
to the argumentative theory, however, the function of reasoning is primarily social: in particular it
allows people to anticipate the need to justify their decisions to others. This predicts that the use of
reasoning in decision making should increase the more likely one is to have to justify oneself. This
prediction has been borne out by experiments showing that people will rely more on reasons when
they know that their decisions will later be made public (D. V. Thompson & Norton, 2008), or when
they are giving advice (in which case one has to be able to justify oneself, see Kray & Gonzalez, 1999).
By contrast, when they are choosing for others rather than for themselves, they are less prone to
these effects, because there is then less need for a utilitarian, justifiable decision (Hamilton &
Thompson, 2007). Finally, it should be stressed that the picture of reasoning painted in these studies
may be overly bleak: demonstrations that reasoning leads to errors are much more publishable than
reports of its successes (Christensen-Szalanski & Beach, 1984). Indeed, in most cases reasoning is
likely to drive us towards good decisions. This, we would suggest, is mostly because better decisions
tend to be easier to justify. The reasons we use to justify our decisions have often been transmitted
culturally and are likely to point in the right direction—as when people justify their avoidance of sunk
costs mistakes by using the rule they have learned in class (Simonson & Nye, 1992). In such cases, the
predictions of the argumentative theory coincide with those of more classical theories. However,
what the results reviewed above show is that when a more easily justifiable decision is not a good
49
one, reasoning still drives us in the direction of ease of justification. Even if they are rare, such cases
are crucial to comparing the present theory (reasoning drives us to justifiable decisions) with more
classical ones (reasoning drives us to good decisions).
6 Conclusion: reasoning and rationality
Reasoning contributes to the effectiveness and reliability of communication by allowing
communicators to argue for their claim and by allowing addressees to assess these arguments. It
thus increases both in quantity and in epistemic quality the information humans are able to share.
We view the evolution of reasoning as linked to that of human communication. Reasoning, we have
argued, allows communicators to produce arguments in order convince addressees who would not
accept what they say on trust; it allows addressees to evaluate the soundness of these arguments
and to accept valuable information that they would be suspicious of otherwise. Thus, thanks to
reasoning, human communication is made more reliable and more potent. From the hypothesis that
the main function of reasoning is argumentative, we derived a number of predictions that, we tried
to show, are confirmed by existing evidence. True, most of these predictions can be derived from
other theories. We would argue however that the argumentative hypothesis provides a more
principled of the empirical evidence (in the case of the confirmation bias for instance). In our
discussion of motivated reasoning and of reason-based choice, not only did we converge in our
prediction with existing theories, we also extensively borrowed from them. Even in these cases
however, we would argue that our approach has the distinctive advantage of providing clear answers
to the why-questions: Why do humans have a confirmation bias? Why do they engage in motivated
reasoning? Why do they base their decisions on the availability of justificatory reasons? Moreover,
the argumentative theory of reasoning offers a unique integrative perspective: it explain wide
swaths of the psychological literature within a single overarching framework.
50
Some of the evidence reviewed here shows not only that reasoning falls short of reliably delivering
rational beliefs and rational decisions, but also that in a variety of cases, it may even be detrimental
to rationality. Reasoning can lead to poor outcomes not because humans are bad at it but because
they systematically look for arguments to justify their beliefs or their actions. The argumentative
theory however puts such well-known demonstrations of ‘irrationality’ in a novel perspective.
Human reasoning is not a profoundly flawed general mechanism; it is a remarkably efficient
specialized device adapted to a certain type of social and cognitive interaction at which it excels.
Even from a strictly epistemic point of view, the argumentative theory of reasoning does not paint a
wholly disheartening picture. It maintains that there is an asymmetry between the production of
arguments, which involves an intrinsic bias in favour of the opinions or decisions of the arguer
whether or not they are sound, and the evaluation of arguments, which aims at differentiating good
arguments from bad ones and hence genuine information from misinformation. This asymmetry is
often obscured in a debate situation (or in a situation where a debate is anticipated). People who
have an opinion to defend don't really evaluate the arguments of their interlocutors in a search for
genuine information, but rather consider them from the start as counter-arguments to be rebutted.
Still, as shown by the evidence reviewed in section 2, people are good at assessing arguments, and
are quite able to do so in an unbiased way, provided they have no particular axe to grind. In group
reasoning experiments where participants share an interest in discovering the right answer, it has
been shown that truth wins (Laughlin & Ellis, 1986; Moshman & Geil, 1998). While participants in
collective experimental tasks typically produce arguments in favour of a variety of hypotheses, most
or even all of which are false, they concur in recognizing sound arguments. Since these tasks have a
demonstrably valid solution, truth does indeed win. If we generalize to problems that do not have a
provable solution, we should at least expect good arguments to win, even if this is not always
sufficient for truth to win (and in section 2 we have reviewed evidence that this is indeed the case).
51
This may sound trivial, but it is not. It demonstrates that, contrary to common bleak assessments of
human reasoning abilities, people are quite capable of reasoning in an unbiased manner, at least
when they are evaluating arguments rather than producing them, and when they are after the truth
rather than trying to win a debate.
Couldn't the same type of situation that favours sound evaluation favour comparable soundness in
the production of arguments? Note, first, that situations where a shared interest in truth leads
participants in a group task to evaluate arguments correctly are not enough to make them produce
correct arguments. In these group tasks, individual participants come up with and propose to the
group the same inappropriate answers that they come up with in individual testing. The group
success is due first and foremost to the filtering of a variety of solutions, achieved through
evaluation. When different answers are initially proposed and all of them are incorrect, then all of
them are likely to be rejected, and wholly or partly new hypotheses are likely to be proposed and
filtered in turn, thus explaining how groups may do better than any of their individual members.
Individuals thinking on their own without benefiting from the input of others can only assess their
own hypotheses, but in doing so, they are both judge and party, or rather judge and advocate, and
this is not an optimal stance for pursuing the truth. Wouldn't it be possible, in principle, for an
individual to decide to generate a variety of hypotheses in answer to some question and then
evaluate them one by one, on the model of Sherlock Holmes? What makes Holmes such a fascinating
character is precisely his preternatural turn of mind operating in a world rigged by Conan Doyle,
where what should be inductive problems in fact have deductive solutions. More realistically,
individuals may develop some limited ability to distance themselves from their own opinion, to
consider alternatives and thereby become more objective. Presumably this is what the 10% or so of
people who pass the standard Wason selection task do. But this is an acquired skill, and involves
52
exercising some imperfect control over a natural disposition that spontaneously pulls in a different
direction.
Here, one might be tempted to point out that, after all, reasoning is responsible for some of the
greatest achievements of human thought in the epistemic and moral domains. This is undeniably
true, but the achievements involved are all collective and result from interactions over many
generations (on the importance of social interactions for creativity, including scientific creativity see
(Csikszentmihalyi & Sawyer, 1995; K. Dunbar, 1997; John-Steiner, 2000; T. Okada & Simon, 1997). The
whole scientific enterprise has always been structured around groups, from the Lincean Academy
down to the Large Hadron Collider. In the moral domain, moral achievements such as the abolition of
slavery are the outcome of intense public arguments. We have pointed out that, in group settings,
reasoning biases can become a positive force, and contribute to a kind of division of cognitive labour.
Still, to excel in such groups it may be necessary to anticipate how one’s own arguments might be
evaluated by others, and to adjust these arguments accordingly. Showing one’s ability to anticipate
objections may be a valuable culturally acquired skill, as in medieval disputationes (see Novaes,
2005). By anticipating objections, one may even be able to recognize flaws in one’s own hypotheses
and go on to revise them. We have suggested that this depends on a painstakingly acquired ability to
exert some limited control over one's own biases. Even among scientists, this ability may be
uncommon, but those who have it may have a great influence on the development of scientific ideas.
It would be a mistake, however, to treat their highly visible, almost freakish, contributions as
paradigmatic examples of human reasoning. In most discussions, rather than looking for flaws in our
own arguments, it is easier to let the other person find them, and only then adjust our arguments if
necessary.
In general, one should be cautious about using the striking accomplishments of reasoning as proof of
its overall efficiency, since its failures are often much less visible (see Ormerod, 2005; Taleb, 2007).
53
Epistemic success may depend to a significant extent on what philosophers have dubbed ‘epistemic
luck’ (Pritchard, 2005 ), that is, chance factors that happen to put one on the right track. When one
happens to be on the right track and ‘more right’ than one could initially have guessed, some of the
distorting effects of motivated reasoning and polarization may turn into blessings. For instance,
motivated reasoning may have pushed Darwin to focus obsessively on the idea of natural selection
and explore all possible supporting arguments and consequences. But for one Darwin, how many
Paleys?
To conclude, we note that the argumentative theory of reasoning should be congenial to those of us
who enjoy spending endless hours debating ideas—but this, of course, is not an argument for (or
against) the theory.
54
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1
Recently, ‘reasoning’ has been used simply as a synonym of ‘inference’, and is then unproblematically attributed to infants
(Spelke & Kinzler, 2007) or to non-human animals (Blaisdell, Sawa, Leising, & Waldmann, 2006). In this article, however, we
use ‘reasoning’ in its more common and narrower sense. The content of the article should make it clear why we see this as
a principled terminological choice.
2
Our functional hypothesis will be tested without reference to specific mechanisms (as is common in evolutionary biology).
Even if one can ask to what extent attributing an argumentative function to reasoning suggests or favours a specific
algorithmic account, this will not be the focus of this article. There is, in any case, no obvious clash between our functional
account and various algorithmic accounts that have been offered for instance by Evans (2007), Johnson-Laird (2006), or
Rips (1994).
3
In the psychology of reasoning, some tasks can be described as ‘production tasks’ because participants have to produce a
logically valid conclusion from a set of premises. However, these tasks are very different from the production of arguments
in a debate. In a dialogic context, one starts from the conclusion and tries to find premises that will convince one’s
interlocutor. It is this meaning of ‘production’ that is relevant here.
4
It should be noted that this spotty record may be partly explained by very artificial conditions: in the vast majority of
group experiments, participants are asked to interact with people they don’t know and will never meet again, and to
perform tasks that have no bearing on their lives outside the laboratory. When any of these factors is made more natural,
performance improves. Debates about political matters between laypeople often lead to epistemic improvement
(Landemore, In press; Mercier & Landemore, submitted). Groups that are used to working together are much more efficient
(Michaelsen, Watson, & Black, 1989a). And collaborative learning is hugely successful in schools (Slavin, 1995).
5
Other, slightly weaker results, are obtained for inductive tasks (Laughlin, Bonner, & Miner, 2002; Laughlin, Hatch, Silver, &
Boh, 2006; Laughlin, VanderStoep, & Hollingshead, 1991; Laughlin, Zander, Knievel, & Tan, 2003). Debates are also a well
known way of improving comprehension in many domains; see for instance (T. Anderson, Howe, Soden, Halliday, & Low,
2001; T. Anderson, Howe, & Tolmie, 1996; Foot, Howe, Anderson, Tolmie, & Warden, 1994; Howe, 1990; D. W. Johnson &
Johnson, 2007; D. W. Johnson & Johnson, 2009; Lao & Kuhn, 2002; Nussbaum, 2008; Nussbaum & Sinatra, 2003; Slavin,
1995; M. K. Smith et al., 2009; Tolmie, Howe, Mackenzie, & Greer, 1993; van Boxtel, van der Linden, & Kanselaar, 2000;
Webb & Palinscar, 1996).
6
Incidentally, another advantage of the theory suggested here is that it makes testable predictions about the contexts that
should motivate the use of reasoning, namely contexts in which real or anticipated argumentation takes place. This
contrasts with standard dual-process theories, which do not have a principled and testable way of predicting when system
2 reasoning should be triggered.
7
It may be worth mentioning that what general motivation fails to bring about is efficient, or unbiased reasoning rather
than reasoning per se. If you pay people to get the right answer in, say, the Wason selection task, they may reason more,
but they will still be as biased, and their answer will still be wrong.
8
The Delphi technique is a method of forecasting that can be seen as trying to make the best of the confirmation bias by
having different experts critique each other’s predictions and justify their own predictions. Its effectiveness shows that in
an appropriate context, the confirmation bias can be conducive to very good performance (Green, Armstrong, & Graefe,
2007; Keeney, Hasson, & McKenna, 2001; Powell, 2003; Rowe & Wright, 1999; Tichy, 2004).
71
9
Note that ‘motivated’, or ‘motivation’ as used here do not refer to conscious motivation based on reasons, as in ‘I’m going
to think of arguments supporting this opinion of mine in case someone questions me later’. Instead, it refers to processes
that influence either the direction or the triggering of reasoning in a mostly unconscious manner. Even though a lawyer, for
instance, can consciously trigger reasoning and influence its direction, this is the exception and not the rule. Generally
people (including lawyers) have limited control over the triggering of reasoning or the direction it takes.
10
Attitude polarization is most likely to occur in individuals who hold very strong attitude with a high degree of confidence.
The problem is then that these individuals will tend to fall at one end of the attitude scale before reading the arguments,
which makes it close to impossible to detect any movement towards a more extreme attitude. This can explain, at least in
part, the failed replications of (Kuhn & Lao, 1996; Miller, Michoskey, Bane, & Dowd, 1993).
11
Incidentally, this does not explain all forms of belief perseverance: other mechanisms may be involved in some instances
(e.g. (C. A. Anderson, Lepper, & Ross, 1980), but the availability of arguments supporting the discredited belief may still be
crucial (see C. A. Anderson, New, & Speer, 1985)
12
It has recently been shown that pigeons fall prey to the fallacy, but only when no indication was given that they were in
such a situation (Navarro & Fantino, 2005). The instructions received by human participants always make this point clear, so
these experiments confirm Arkes and Ayton’s point.
72

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This is a non copy-edited version of a paper accepted for publication in Behavioral and Brain Sciences

Why do humans reason?

Arguments for an argumentative theory

Philosophy, Politics and Economics Program

313 Cohen Hall

249 South 36th Street

Institut Jean Nicod (EHESS-ENS-CNRS)

29, rue d’Ulm

Central European University, Budapest

Motivated reasoning, Reason-based choice, Reasoning.

Short abstract (98 words).

reasoning is argumentative. It is to devise and evaluate arguments intended to persuade. Reasoning

so conceived is adaptive given humans’ exceptional dependence on communication and vulnerability

be reinterpreted and better explained in the light of this hypothesis.

Long abstract (247 words).

reasoning is argumentative. It is to devise and evaluate arguments intended to persuade. Reasoning

vulnerability to misinformation. A wide range of evidence in the psychology of reasoning and

conclusion, and favour conclusions for which arguments can be found.

animals or in pre-verbal children.1

evaluation of arguments in communication. In sections 2 to 5, we consider some of the main themes

1 Reasoning: Mechanism and function

1.1 Intuitive inference and argument

A process of inference is a process the representational output of which necessarily or

that is beliefs held without awareness of reasons to hold them.

representations of representations). The capacity to represent representations, and to draw

inference is a process the output of which is a representation. An argument is a complex

be followed in any inference for it to be an argument.

been hotly debating the issue (e.g., Katz, 1986 ).

intuitions about premise-conclusion relationships. Intuitions about arguments have an evaluative

and preferences are ultimately grounded in intuition.

acceptance is an epistemic decision that we take at a personal level. If we construct a complex

function of reasoning puts it on display in human communication.

1.2 The function of reasoning

should therefore be seen as its main function.

& Wason, 1976).

signature effects of that function in the very way reasoning works.

framework of the evolution of human communication. Reasoning allows people to exchange

The interpretation of communicated information involves activating a context of previously held

communicated and in revising our beliefs.

Reasoning contributes to the effectiveness and reliability of communication by allowing

2.1 Understanding and evaluating arguments

of valid modus tollens arguments in argumentative contexts is also “surprisingly common”

(Pennington & Hastie, 1993, p.155).

fallacies of argumentation. Unlike logical fallacies, fallacies of argumentation come in degrees:

inferences and at the level of whole discussions.

2.2 Producing arguments

very quickly (Shaw, 1996).

2.3 Group reasoning

task in groups they reached the level of 80% of correct answers.

initially—can facilitate learning and comprehension of a wide variety of problems (Mercier,

have a motivational power to which reasoning responds specifically.7

1974; Isenberg, 1986; Kaplan & Miller, 1977; Madsen, 1978).

3 The confirmation bias: A flaw of reasoning or a feature of argument production?

used for the production of arguments.

characterized by a failure to look for counter-evidence or counter-arguments to an existing belief,

Klayman & Ha, 1987).

in favour of disconfirming opposing claims and counterarguments.

3.1 Hypothesis testing: No reasoning, no reasoning bias