Psychological Inquiry Intuition in Judgment and Decision Making

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Psychological Inquiry
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Intuition in Judgment and Decision Making: Extensive Thinking Without
Tilmann Betscha; Andreas Glöcknerb
Department of Psychology, University of Erfurt, Erfurt, Germany b Max Planck Institute for Research
on Collective Goods, Bonn, Germany
Online publication date: 03 December 2010
To cite this Article Betsch, Tilmann and Glöckner, Andreas(2010) 'Intuition in Judgment and Decision Making: Extensive
Thinking Without Effort', Psychological Inquiry, 21: 4, 279 — 294
To link to this Article: DOI: 10.1080/1047840X.2010.517737
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Psychological Inquiry, 21: 279–294, 2010
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Copyright ISSN: 1047-840X print / 1532-7965 online
DOI: 10.1080/1047840X.2010.517737
Intuition in Judgment and Decision Making: Extensive Thinking
Without Effort
Tilmann Betsch
Department of Psychology, University of Erfurt, Erfurt, Germany
Andreas Glöckner
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Max Planck Institute for Research on Collective Goods, Bonn, Germany
We claim that intuition is capable of quickly processing multiple pieces of information without noticeable cognitive effort. We advocate a component view stating
that intuitive processes in judgment and decision making are responsible for information integration and output formation (e.g., preference, choice), whereas analytic
thinking mainly guides input formation such as search, generation, and change of
information. We present empirical evidence corroborating this notion and show that
integration of information and preference formation works without cognitive control
and is unconstrained by the amount of encoded information and cognitive capacity.
We discuss the implications of our findings for the bounded rationality perspective and
the multiple strategy approach to judgment and decision making. Finally we outline
a connectionist framework for integrating intuitive and analytic thought processes.
Seymour Epstein (2008) recently noted, “Intuition
has been given so many different meanings . . . that it
makes one wonder whether the term has any meaning at all” (p. 23). Even within psychology different
concepts coexist. Intuition is conceived as a source of
knowledge, a particular process or even a structure of
the brain (Winerman, 2005). The field of judgment and
decision making mainly resides with the process view
yet proposes quite different mechanisms for the underlying process (Glöckner & Witteman, 2010b; Plessner, Betsch, & Betsch, 2008). Prominent approaches
equate intuition with heuristic processing (Gigerenzer,
2007; Gilovich, Griffin, & Kahneman, 2002). According to this notion, individuals often make judgments
and decisions by applying simple rules for information
search and/or output-formation. These heuristics are
assumed to rely on only a subset of available information (sometimes even only one piece). For example,
people might base probability judgments mainly on
the representativeness of an object for a certain category (representativeness heuristic: Tversky & Kahneman, 1974), or when deciding between two objects,
they might choose the one that they recognize over the
one they do not, while ignoring all other information
(recognition heuristic: D. G. Goldstein & Gigerenzer,
2002). Compared to normative rules, for example, utility theory’s maximization principle or formal rules of
probability theory, such heuristics substantially reduce
cognitive effort. In some situations, however, they may
lead to biased judgments and decisions (Kahneman,
Slovic, & Tversky, 1982). In spite of this, they often
help the actor quite well to maintain a relatively high
level of accuracy in different choice and judgment tasks
(Gigerenzer, Todd, & the ABC Research Group, 1999;
see also Kahneman & Klein, 2009).
The heuristic approach emphasizes effort reduction
and selective information processing as key features of
intuitive thought. For example, the lexicographic rule
(LEX) is a simple analytic strategy that can be used
in decision making. It says, Do not consider all information, rather start by comparing alternatives on the
most important dimension. If they differ, choose the
best and terminate further information search. There
is ample evidence, both anecdotal and empirical, that
people can successfully employ such a strategy of reduced information search.
In this article we advocate a markedly different
view on intuition. Note that we do not wish to discuss the usefulness of the heuristic approach. We do not
deny that individuals may apply processes described by
these heuristics, especially those that apply to search.
We doubt, however, whether heuristics really cover the
potentials of intuitive thought. Many of the heuristics
described in the judgment and decision making (JDM)
literature merely seem to be simplifications of analytic
thought (see Betsch, 2008, for a discussion). They cope
with cognitive limitations by leaving out effortful information processes, reducing the amount of information
considered or, in short, by reducing complex judgments
to simpler ones (cf. Tversky & Kahneman, 1973, p.
207). It is our conviction that there is much more to
intuition than simply a reduction in task complexity.
Our basic claim is that intuition is capable of dealing with complex tasks through extensive information
processing without noticeable effort.
We set out by explaining our concept of intuition in
more detail. Then we present some empirical evidence
substantiating our point of view. Finally, we discuss the
limitations of intuitive thought as well as implications
for bounded rationality and the multiple strategy approach in JDM. We close with a sketch of an integrative
model and suggestions for future research.
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The Nature of Intuition
Processes of thinking are often sorted into two
distinct categories. These categories have been given
many different names (see Chaiken & Trope, 1999;
Evans, 2008; Plessner, Betsch, & Betsch, 2008, for
overviews). Henceforth we call them intuitive and analytic, but we do not overly comply with any specific
label; more important is how we define them. In the following we present a sketch of intuition that is rooted
in our theoretical approach to judgment and decision
making (Betsch, 2005; Betsch, Plessner, Schwieren, &
Gütig, 2001; Glöckner & Betsch, 2008a, 2008b).
Intuitive processes operate autonomously and automatically, that is, they function without conscious
control and cannot easily be accessed by introspection. Moreover, they can process multiple pieces of information in parallel. Analytic processes, in contrast,
are performed step-by-step. The sequence and direction of these processes can be deliberatively controlled,
and the actor is consciously aware of performing these
processes. This definition overlaps conceptually with a
number of other well known definitions in the literature
(e.g., see Betsch, 2008; Glöckner & Witteman, 2010,
for discussions).
We assume that intuitive processes have the following main characteristics. First, in contrast to processes
of deliberative construction and control (Glöckner &
Betsch, 2008a), intuitive processes are only marginally
constrained by cognitive capacity. Second, intuitive
processes use all pieces of information that are momentarily activated from memory and salient in the
environment (Betsch, 2005). As such, intuition processes encoded information in an extensive fashion irrespective of its origin (memory or environment). An
important implication of this notion is that intuition
relies heavily on prior experience. The stronger prior
experience has been consolidated in memory, the more
likely it will be activated by situational cues and, hence,
feed input to intuition (Betsch, 2008). This is not to
say that intuitive processes can only operate on prior
knowledge, but prior knowledge will always be used if
it is activated.
Some (but not all) dual process models in cognition entertain an either–or view of mode application
(see Evans, 2008, for a recent review). Accordingly,
judgments and decisions are made in either an intuitive or analytic mode. We do not see much merit in
such a notion. Rather we believe that intuition and
analysis share labor by performing different types of
processes. We propose that intuitive processes handle
output formation, that is, they are responsible for integrating input information and producing a judgmental
or choice tendency (e.g., a preference). In turn, analytic processes are responsible for input formation,
involving controlled search, generation and temporary
change of information, which are only activated if necessary. We return to this component view later in the
How Intuition Works: An Example From
Outside JDM
According to our view, intuitive processes are characterized by extensive thinking without effort. This
notion is in opposition to mainstream views on intuition in the field of JDM. Cautiously speaking, such a
notion may seem unrealistic from the viewpoint of a
decision researcher raised in the tradition of bounded
rationality. Those who point to the powers of the unconscious in decision making (e.g., Dijksterhuis, 2004;
Dijksterhuis, Bos, Nordgren, & van Baaren, 2006) enter a field of harsh debate (e.g., Acker, 2008; GonzalezVallejo, Lassiter, Belleza, & Lindberg, 2008). In other
areas of cognition, however, it is quite common to
assume that extensive processing of information can
easily be performed by the human mind. For example, in speech comprehension and production as well
as pattern recognition and categorization, individuals
are found to be capable of rapidly processing multiple
pieces of information in an astoundingly narrow time
Consider, for instance, the case of understanding
irony and sarcasm. Like other figures of humorous
speech, the meaning of an ironic or sarcastic statement
diverges from its surface meaning. In his famous tale
“A Child’s Christmas in Wales,” Dylan Thomas (1968)
described the following incident he experienced as a
boy in his neighbor’s home: “Something was burning
all right; perhaps it was Mr Prothero. . . . But he was
standing in the middle of the room, saying, ‘A fine
Christmas!”’ (p. 6)
Understanding the latter statement properly requires
the recipient to decide that the literal meaning of the
sentence is not suitable to understand the intended
meaning. This task involves complex processes such
as encoding of the literal meaning, recognition and
categorization of the context, and detection of incongruence between utterance and context. Nevertheless,
individuals rapidly grasp the meaning of statements
like the one in this example.
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Research on the understanding of irony in speech
is evidencing that adult recipients simultaneously utilize even more stimuli such as prosodic-, mimic-,
and gesture-related information (see Gibbs & Colston,
2007, for overviews). Nevertheless, comprehension is
a very quick process in natural settings and can be performed in less than 600 to 800 ms (e.g., Schwoebel,
Dews, Winner, & Srinivas, 2000). Remarkebly, increasing the amount of information (e.g., by providing
contextual cues) can result in a faster detection of irony
(Amenta & Balconi, 2008). This finding is attributed to
parallel processing. Wang, Lee, Sigman, and Dapretto
(2006) compared accuracy and speed in identification
of irony in average and clinical patients. In the latter,
the ability of parallel activation of brain regions was
impaired. Whereas average participants showed simultaneous activation of different brain regions and were
very quick to understand irony, the clinical patients
had to engage in time-consuming deliberative thought
to grasp the ironic intention behind the message. These
and other findings led researchers to postulate that essential parts of speech comprehension are driven by
holistic processes operating automatically and in parallel (e.g., Long & Graesser, 1988).
We have chosen this example because speech comprehension and decision making share some important
properties. In both domains, the individual must cope
with uncertainty and utilize probabilistic cues to judge
a distal criterion (speech: intended meaning; decision
situation: future benefits of behavior). Speech and rational decisions are usually seen as manifestations of
cognitive capabilities that are genuinely human. Regularly, we do not expect animals to understand irony or
engage in anticipative waging of outcomes. Both domains involve conscious processes. In a standard communication situation, the recipient focuses her attention on the sender. Listening itself involves conscious
awareness of the situation and the content of the message. Similarly, in standard settings used in decision
research (e.g., the gambling paradigm), the individual
intends to make a decision, focuses on the information
given, and is consciously aware of alternatives as well
as their respective pros and cons.
It is our conviction that judgment and decision processes capitalize on the same potentials of the brain
as speech comprehension processes do. According to
our view, intuitive processes are devoted to information integration and the formation of judgmental or
choice tendencies. These processes will immediately
be instantiated upon activation or encoding of the information. They work in the background while analytic
processes can set in to control or change further information search. In the following sections, we substantiate our claim with empirical evidence. Specifically,
we show that extensive information processing without
conscious control also guides judgment and decision
Extensive and Uncontrolled Integration of
Information in Judgment
The experiments reviewed in this section show that
extensive information integration can occur even if
cognitive capacities are constrained by another task
and the individual does not intend to form a summary
evaluation of the target objects. In the experimental setting described next, participants were asked unexpectedly to evaluate shares. Prior to evaluation, they had
encoded outcome information about these shares in a
dual-task setting. As a distracting task (framed as the
primary one), participants were asked to memorize the
content of videotaped ads. Participants were told that
the (ostensible) goal of the study was to assess memory
performance under distracting conditions. In line with
this cover story, reading outcome information about
the shares was framed as a secondary task. Specifically, they were asked to read aloud information about
shares scrolling across the bottom of the screen. Outcomes were return values produced by fictitious shares
at the stock market over a period of several trading days
(see Figure 1). A single return value (e.g., 32) represented the gain in Euro cent achieved by a particular
share (e.g., “Pittler”) on a particular trading day. Return
values were shown at a quick pace. Participants were
asked to read all appearing return values and names
of shares aloud to ensure encoding. They viewed up
to 140 pieces of return values produced by up to eight
different shares on 20 trading days. The presentation
lasted up to 10 min. In sum, this procedure established
a high level of information overload, constrained deliberate processing resources with a dual task procedure,
and obscured the true intention of the study. Extensive pretesting showed that this procedure effectively
prevented participants from consciously integrating return values and forming deliberate judgments of the
The distribution of return values was manipulated as
a within-subjects factor. Specifically, the sum, average,
frequency, and dispersion of return values were varied.
In line with the cover story, participants were asked to
recall information about the ads after the presentation.
Subsequently, however, they were unexpectedly asked
to spontaneously evaluate each share by adjusting a
scroll bar representing an affective scale with endpoints
labelled good and bad.
Postexperimental interviews showed that participants indeed did not expect to evaluate the shares. Participants consistently reported that they had focused
their attention on the ads and did not attempt to form
evaluations of the shares. Moreover, a recall test following the judgments showed that participants were
unable to reliably reproduce characteristics of the distributions of the return values.
One key research question was whether return distributions covaried with evaluations in a systematic
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Figure 1. Screenshot of stimulus presentation used in studies on integration of
information without intention. Note. Attention-grabbing ads are shown in the
background. Return values of shares are shown in the running caption at the
fashion. If information integration occurred automatically during the encoding of return values, spontaneous
judgments of the shares should systematically reveal
influences of the properties of the given information.
Across a number of studies, the astounding finding
was that evaluative judgments consistently reflected the
sum of return values. Figure 2 shows the results of the
first experiment from this line of research (Betsch et
al., 2001, Exp.1). Participants were presented with five
target shares, each appearing with the same frequency.
The sum of their return values, however, differed between 300 and 700 Euro cents. Evaluative judgments
perfectly reproduced the actual variation of the sum of
values. Accordingly, the 300-share was rated least positively, whereas the 700-share received the most positive
rating. At the end of this study, participants were also
asked to think carefully and rehearse the sum and av-
erage of return values produced by each of the shares.
In contrast to their spontaneous evaluative judgments,
participants were unable to deliberatively reconstruct
sums and averages of the return distributions.
The results indicate that mere encoding of value
laden information (monetary outcomes) was a sufficient condition for instigating the integration of this
input information. Integration seemed to function without intention and deliberate control under conditions
of information overload (dual task situation) and while
participants’ attention was focused on another task
(memorizing details of ads).
A series of subsequent studies further substantiated
the validity of these conclusions und ruled out alternative explanations of the results. For instance, we
showed that the results could not be explained in terms
of simple heuristics such as numerosity, peak-and-end
Figure 2. Results from Betsch, Plessner, Schwieren, and Gütig (2001, Exp.1).
Note. Evaluative judgments of five target shares differing with regard to sum
of return values. Higher values indicate a more intense liking.
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(Betsch et al., 2001), familiarity and ease of recognition (Betsch, Hoffmann, Hoffrage, & Plessner, 2003).
Moreover, Plessner, Betsch, Schallies, and Schwieren
(2008) demonstrated that the findings could be replicated in nonmonetary domains (e.g., political judgment
and voting behavior).
Of interest, the efficiency of extensive integration
was impaired if participants attempted to control the
process by deliberation. Intention and deliberation
changed the pattern of results systematically. In contrast to intuitive integration, analytic inspection of the
given information decreased the amount of information
considered (Betsch et al., 2001, Exp. 3) and caused participants to consider other aspects such as the size of
information sample as weighting factors. Specifically,
the given information was weighted by the size of the
sample (Betsch, Kaufmann, Lindow, Plessner, & Hoffmann, 2006) and its reliability (Kaufmann & Betsch,
2009). The latter results show that analysis can change
the weighting of information in a fashion distinct to
intuitive processing. We return to this issue later in the
article when we discuss our connectionist model.
Altogether, this brief review of studies provides evidence in support of the notion that information integration can be extensive and effortless at the same
time. The underlying mental processes operate autonomously in the background while the individual focuses on another capacity-consuming task. Integration
resulted in systematic preference formation. The only
precondition for instantiating extensive accounting of
information was that the information was encoded, that
is, fed into the processing system (cf. Betsch, Plessner,
& Schallies, 2004, for a discussion).
Altogether, the results corroborate our claim that
intuitive processes are characterized by autonomous
(uncontrolled, unintentional) operation and extensive
consideration of information. Thus far we have considered judgments and not decisions. Moreover, the
experimental setting was designed in such a way that
analytic processes were directed to another task. Recall, however, that we claimed that intuitive and analytic processes regularly interact in decision making
(but are devoted to different types of processes). In
the next sections, we present results from studies on
choice among options using a standard paradigm from
decision research. We show that extensive integration
of information will also occur in deliberate decision
making. In the experiments reviewed next, individuals use and integrate all available information within a
remarkably narrow time frame when encoding is easy.
Extensive and Effortless Integration of
Information in Decision Making
One of the most widely used tools for process tracing in deliberate decisions is the Mouselab (Johnson,
Payne, Schkade, & Bettman, 1986). The Mouselab
presents information in a covered Option × Attribute
matrix. Individuals use the computer mouse to uncover information hidden in the fields, during which
the computer program protocols search behavior, decision times, and choices. The introduction of the
Mouselab was a significant advance in process tracing research. With help of this tool, decision strategies
were identified on the basis of search behavior (e.g.,
Payne, Bettman, & Johnson, 1988). In Mouselab experiments it has been consistently observed that, under
severe task constraints (e.g., time pressure), individuals
change from complex to simple strategies that consider
only subsets of information. These and corresponding results were interpreted as strong corroborations
of the key assumptions of the bounded rationality approach and its offspring the multiple strategy approach
to judgment and decision making (e.g., Gigerenzer,
2004; Gigerenzer & Selten, 2001; Payne, Bettman, &
Johnson, 1993).
The Mouselab, however, places constraints on information acquisition. It requires motor behavior (mouse
movements) to uncover information hidden in the matrix. Moreover, if only one box can be open at a time
and closes after another is opened, the tool places constraints on working memory, because pieces of information must be remembered until the search process is
terminated. In the Mouselab, external constraints (e.g.,
time pressure) enhance the costs of information acquisition, making it reasonable to reduce search depth.
As such, the observation of adaptive strategy change
shows that the costs of information search and retention
in working memory are indeed boundaries to information acquisition. This finding does not provide conclusive evidence, however, for the notion that information
integration and calculation capacities are constrained
by external factors.
In a series of studies (Glöckner & Betsch, 2008b),
we compared information consideration and integration under time constraints in hidden and open versions
of the information board tool (Figure 3). In one condition we used the standard version of the Mouselab.
Accordingly, participants were presented with a hidden
information board so that they could access values in
the matrix only serially by using the computer mouse.
In another condition, we employed an open information board. All entries to the matrix were shown on
the screen so that motor behavior was not necessary
to access information and the entire stimuli set could
be inspected directly and easily. In all conditions, the
columns of the matrix contained three options representing different food producers. The rows contained
binary predictions from three testers concerning the
quality of products (values) represented by the symbols
+ (good) and – (bad). The probability of being correct (cue validity) differed between the testers. Their
cue validities ranged between .60 and .95 and were
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Figure 3. Information board with hidden and open cells used in the studies by Glöckner and Betsch (2008b).
randomly assigned to the position of the testers (cues)
in the matrix. The choice task was to select the best
producer by clicking on the option’s number with the
computer mouse.
We varied the patterns of values systematically to
allow for a choice-based measure of strategy identification (see Glöckner & Betsch, 2008b, p. 1060; for
discussions of the procedure; cf. Glöckner, 2009). In
a first step, we assessed whether findings typically observed in Mouselab experiments could be replicated in
our decision domain. In line with results reported in
the literature, the majority of our participants (90%)
used lexicographic strategies when making decisions
in the standard Mouselab (hidden information) under
time pressure. Participant choices across the different
patterns of values indicated that they considered only
a few pieces of information on the most important dimension (the cue with the highest validity) and ignored
the others. Therefore, they seem to rely on only a subset
of the given information.
The pattern of results dramatically changed when
information was presented in an open information
board. Even in a very narrow time frame (less than 1.5
s), more than 70% of participants’ choices reflected
an extensive consideration and integration of information. Specifically, their choices indicated that all cue
validities and all cue values were integrated in line with
a weighted summation model. In a further study, we
instructed participants to make their decision by deliberatively integrating information according to such
a weighted additive rule. It took these participants almost 20 s on average to perform the task.
The results add to the existing evidence. They
demonstrate that integration of information can happen in an extensive fashion. Most important, integration is performed in substantially less time compared to conditions in which participants were asked
to deliberatively perform the same operations (weighting and adding). Thus, we concluded that information integration can be performed intuitively without
requiring conscious, rule-based operations of weighting and adding. The findings corroborate our notion
that intuitive processes can integrate information with284
out considerable mental effort. Of interest, these processes seem to operate in the background of conscious
thought, because all participants deliberatively dealt
with the decision problems.
In the next section, we stretch our argument to an
extreme. If it is true that intuitive processes can integrate information without considerable mental effort,
processing time should be unaffected by the amount of
information to be integrated.
The Power of Intuition: When More
Information Is Processed Faster Than Less
Before we present the study (Glöckner & Betsch,
2010), we need to go into more detail regarding our theoretical framework for modelling intuition. Further, we
also outline a connectionist model of information integration. According to this model, intuitive processes
consider all information contained in a working network in parallel. They attempt to find a solution (e.g.,
a preference) that coheres to constraints imposed by
the entire pattern of information, especially their relations among each other. In line with this reasoning, we
propose that intuitive processes have another important
characteristic. They are sensitive to the holistic aspect
of the information sample, namely, the coherence in
the pattern (note that this assumption dovetails with
Gestalt theory).
In coherent patterns, judgments and decisions are
easier to make than in incoherent patterns. For example, if all arguments favor one option and speak
against another, the choice is easy. However, if pros
and cons are associated with both options, the choice
is more difficult, even if normatively one option dominates the other. There is ample evidence indicating
that the degree of coherence covaries with the time
needed to arrive at a judgment or decision (Glöckner
& Betsch, 2008b; Glöckner, Betsch, & Schindler,
in press; Glöckner & Bröder, in press; Glöckner &
Hodges, in press; Hilbig & Pohl, 2009; Hochman, Ayal,
& Glöckner, 2010). Taking this evidence into account,
Table 1. Decision Tasks Used by Glöckner and Betsch
Cue Patterns
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Regular: Complete decision tasks
Reduced I: Decrease in coherence
Reduced II: Increase in coherence
Note. A, B = options; v = cue validity.
we predict that decision time should vary as function
of coherence but not as a function of the amount of
information. Specifically, if additional information increases coherence of the entire pattern, than decision
time should decrease (despite the fact that there is
more information to be processed). Conversely, if additional information decreases overall coherence, then
decision time should increase (despite the fact that
there is less information to be processed). We tested
this hypothesized interaction effect in the following
We used an open information board similar to the
one just introduced, with four cues of different validity
(55–80%) and two options (Glöckner & Betsch, 2010).
The most valid cue always differentiated between the
two options. Accordingly, a consistent application of
a simple lexicographic strategy would result in constant decision time on average. Moreover, one option
was always better than the other with regard to their
weighted-additive value. As in the previous studies, we
used different value patterns within subjects in order
to allow for choice-based identification of strategies
(see Table 1). As another within-subjects factor, we
varied the amount and coherence of the given information pattern. In the control tasks, participants received
the entire set of eight values with a moderate level
of coherence. In two other conditions, two pieces of
information (values on the least valid cue) were removed from the matrix (reduction of amount of information). In one of these conditions, the reduction
increased coherence (a piece of contradicting information was removed), whereas in the other the reduction resulted in an decrease in coherence (a piece of
supporting information was removed). We instructed
participants to make decisions as quickly as possible
and measured decision time as the main dependent
How should these manipulations affect decision
time? First, a null effect might occur if participants tend
to use a simple search strategy such as LEX (i.e., focus
only on the most valid cue) in reaction to the speed
instruction. Accordingly, reaction times should not be
affected by variations of amount and coherence of information because the most valid cues always differed
between options. Consequently, such a simple strategy
would relieve individuals from considering information on the other cues. Second, if individuals applied
a more complex strategy, a main effect for amount of
information could occur. This prediction follows from
multiple strategy models, which assume that processing of more information consumes more cognitive resources. Accordingly, decisions should take longer if
participants must weight eight pieces of values with
the corresponding cue validity compared to six values.
Third, according to our prediction, an interaction effect should occur. Specifically, reaction times should
decrease with increasing information if coherence increases as well. Conversely, reaction times should increase with increasing information if this entails a decrease in coherence.
Indeed, we found the predicted interaction effect: A
reduction of the amount of information resulted in an
increase in decision time when coherence decreased.
When the reduction of information was accompanied
by a decrease of coherence, less information was processed more slowly than in the control condition containing the entire set of information. Thus, the findings
support our prediction that more information can be
processed more quickly than less information.
The reported study provides further evidence for
the advocated notion of intuition. Information integration and preference formation seem to be largely
unaffected by the amount of encoded information and
cognitive capacity. This conclusion seems to be in opposition to the dominant view in JDM research, holding that capacity constraints require decision makers to
use simple strategies for judgment and choice. In the
remainder of this article, we present some suggestion
on the conceptual and theoretical level to solve this
puzzle. We begin by reconsidering the fundaments of
simple strategies (heuristics), the bounded rationality
The Notion of Cognitive Effort in the
Bounded-Rationality Approach
The bounded-rationality approach focuses on analytic or deliberate processes. As A. Newell and Simon
(1972) stated, “We are observing intentionally rational
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behavior” (p. 55). This focus of research is deeply
rooted in Western philosophy and Christian religion.
In accordance with the Cartesian view, decision making has been considered one of the supreme disciplines
of conscious thought. Even believing in God was conceived as an act of rational choice (see Blaise Pascal’s
“wager”). The maximization principle of utility theory
pushes to the extreme the notion that decisions require
(intense) deliberation.
Simon doubted, however, whether the maximization principle could serve as a descriptive model of
human decision making. He suspected that individuals must have supernatural computational powers to
implement this principle. He referred to utility theory as an “Olympic model” (H. A. Simon, 1982) and
questioned its descriptive validity: “My first empirical
proposition is that there is a complete lack of evidence
that, in actual choice situations of any complexity, these
computations can be, or are in fact, performed” (H. A.
Simon, 1955, p. 104).
In developing his bounded rationality approach, Simon suggested that individuals do not strive to make
the best decision but rather employ simple rules or
strategies that allow them to achieve a satisficing level
of accuracy by minimizing computational or cognitive effort. This notion exerted a strong impact on the
field of psychological decision research. It stipulated
the rise of multiple strategy models (e.g., Beach &
Mitchell, 1978; Gigerenzer & Goldstein, 1996; Payne
et al., 1993; see Betsch, Haberstroh, & Höhle, 2002,
for an overview). These models converge in assuming that individuals can choose among a variety of
strategies (or heuristics) for judgment and decision
making. These strategies differ with regard to effort
and accuracy. Individuals are assumed to select strategies contingent upon environmental, task, and personal
factors. This approach still dominates the field of judgment and decision making (e.g., Gigerenzer & Selten,
One guiding research question is how individuals
make an adaptive compromise between the cognitive
effort and the (potential) benefits of deliberation and
accuracy of a decision. Therefore, it is essential for this
approach to quantify cognitive effort (Payne, Bettman,
& Johnson, 1992, p. 112ff). A. Newell and Simon
(1972) suggested that decision processes can be decomposed into units, the so-called elementary information processes (EIP). Examples of EIP are “reading information”; “comparing values”; “eliminating values”;
and processes of calculation such as “subtracting,”
“adding,” and “multiplying.” Over the past decades,
research in this domain has identified a plethora of
decision rules or strategies that reduce the number
of EIP compared to utility theory’s weighted-additive
rule. These strategies, such as the LEX, elimination by
aspect, and equal weight, allow individuals to achieve
or sustain a moderate to high level of decision ac286
curacy even under processing constraints (e.g., time
pressure, fatigue, distraction). Most notably, however,
these strategies are shortcuts to deliberation. The idea
of cognitive effort is still central and undisputed simply because other processes are rarely considered at
the level of specific strategies (although they are sometimes considered on a higher level of strategy selection,
e.g., Payne et al., 1993).
According to the notion that human judgment and
decision making is constrained by cognitive capacity,
one must predict that increasing the amount of information to be processed will result in an increase in
cognitive effort. In other words, extensive thinking is
only possible to the degree that cognitive resources are
available. The empirical evidence presented in this article seems to run counter to this major tenet of the
bounded rationality approach. We demonstrated that
individuals can integrate multiple pieces of encoded
information very quickly and even under cognitive constraints. On the other hand, we must acknowledge the
bulk of evidence corroborating the bounded rationality
approach. It is a robust and well-replicated finding that
individuals can and do employ strategies that use only
subsets of the given information, especially in situations that constrain cognitive capacity (Dawes, 1998;
Gigerenzer, 2004).
Solving the Puzzle of Contradicting Evidence:
Three Assumptions to Start With
Our approach rests on three main assumptions. The
first states that intuitive and analytic processes are distinct. They obey to markedly different cognitive principles and, hence, should be considered to have emergent
properties. One far-fetching implication of this proposition is that intuition is not a shortcut to deliberation
but something different. According to our view, most of
the strategies identified in research on multiple strategy
models are not intuitive strategies but rather simplification of analysis (e.g., LEX, Peak-And-End Heuristic;
see Betsch, 2008, for a discussion). They circumvent
effortful processing by reducing the amount of information and avoiding weighting and integration procedures. As such they are still informed by the spirit of
rationalism, which assumes that decision making is a
deliberate process. However, focusing on the conscious
side only may lead us to neglect the fact that thinking
also involves genuinely different processes. These are
not subject to introspection; function without cognitive control; and, most important, exploit the powers
of the brain to process information automatically and
in parallel.
Second, due to their different nature, intuitive and
analytic processes have different potentials and suffer from different constraints. The “two blades of the
scissor” view of the bounded rationality approach is
an appropriate metaphor to describe the bottleneck for
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analytic processes. Because these are performed in a
step-by-step fashion, they consume processing time
and, hence, are under the governance of working memory constraints and factors of the environment that
strain cognitive resources (the two “blades”).
With respect to intuitive processes, however, other
boundaries seem to apply. Whereas processes of information integration and preference formation seem to
be quite immune to variations in amount of information and task constraints, pattern characteristics of the
given information do matter. In our studies, the ease
and speed of information integration was bounded by
coherence in the pattern of encoded information. We
also briefly mentioned another factor. If the individual
attempts to control and deliberately guide these intuitive processes, their efficiency can be impaired (e.g.,
Betsch, Plessner, et al., 2004). Similar results can be
found in the literature on human error (e.g., Reason,
1992). As such, the notion of boundaries is an important and valid one. Acknowledging that boundaries are
process specific, however, we must extend our catalogues by those types of boundaries that apply to intuitive processes.
Third, and most important, we assume that intuition
and analyses are component processes rather than different modes of thinking. According to this view, a
judgment or decision is not made in either the intuitive
or analytic mode. Rather, intuition and analyses are assumed to be responsible for guiding different types of
subprocesses (components). This view converges with
recent developments in the literature on dual process
models. Granting substantial empirical evidence, Ferreira, Garcia-Marques, Sherman, and Sherman (2006)
concluded that “judgment under uncertainty is neither
an automatic nor a controlled process but . . . reflects
both processes, with each making independent contributions” (p. 798).
We suggested that information integration and output formation (e.g., preference, choice) is performed
by intuitive processes, whereas forming the input
to integration often requires analysis. These analytic
processes may involve directed search for information (e.g., looking for the most valid cues, asking
an expert for advice), making sense of given information (e.g., assessing how given consequences
affect personal goals), anticipating future events,
making inferences to generate information, and so
As such, we conceive a judgment or decision as
the product of the collaboration of intuition and analyses. Whereas the depth of analysis can vary, we assume that intuitive processes always work in the mental
background even if the individual deliberatively faces
a judgment or decision task (see also the notion of
default-interventionist models by Evans, 2008). Therefore, these intuitive processes are inevitable. Preferences evolve even if the individual does not intend to
form a preference (an old notion indeed, cf. Wundt,
1907; Zajonc, 1980).
Applying the Component Approach to
Understanding Thinking Under Boundaries
One of the key assumptions of the bounded rationality approach and multiple strategy models states that
strategies or heuristics are tools of adaptation. They allow individuals to maintain mastery in changing contexts by making satisfactory or even good compromises
between the costs of thinking and desired accuracy in
judgment or choice (Beach & Mitchell, 1978; Gigerenzer et al., 1999; Payne et al., 1988). First, let us reconsider the goals the individual is assumed to pursue in
strategy selection. The bounded rationality approach
assumes that constraints foster application of strategies that reduce the number of EIPs needed to make a
decision. A straight means to achieve this goal is selective search. Accordingly, simple strategies (e.g., LEX)
focus on only a few important pieces of information
(e.g., outcomes on the most important attribute or most
valid cue). Selective search reduces not only the number of EIPs needed to form the input to the choice but
also the number of EIPs needed to transform the input
to an output (e.g., a choice).
In our component approach we assume that input
and output formation is guided by genuinely different
processes, analyses and intuition, respectively. In line
with the bounded rationality approach, we also assume
that the costs for analysis covary with the number of
deliberate processing steps (EIPs) because these processes are performed step-by-step. In addition, we postulate that the costs for intuition do not systematically
covary with the number of processing steps, because
they can be performed in parallel. Moreover, we postulate that intuitive processes are governed by different
types of constraints than analytic processes, such as
holistic aspects of the information pattern (e.g., coherence).
The bounded rationality approach and multiple
strategy models predict commensurate effects of constraints on processing. They converge in assuming that
constraining processing time or cognitive resources
(e.g., via load manipulation) would both bound input
and output formation. For instance, severe time pressure should increase the likelihood that strategies are
employed, which reduce the amount of information
and/or complexity of the decision rule. Because constraints have commensurate effects on all steps, it is
possible to allocate resources in a cost-compensating
fashion. For instance, the individual may first attempt
to search as much information as possible, but then
apply a very simple rule that considers only a small
subset of the encoded information when time runs out
(or vice versa).
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In contrast, our component approach predicts orthogonal effects for different types of constraints. Accordingly, distinct types of factors independently constrain input and output formation. Therefore, compensation of costs between input and output processes is
neither possible nor necessary. Limiting processing resources (time, capacity) will constrain only input but
not output formation. Consequently, we predict that
only input formation will covary as a function of limited resources.
One puzzling finding in the area of multiple strategy research is that individuals use simple strategies
(such as LEX) far less often than expected theoretically. Even in environments that ideally favor the application of such simple strategies (e.g., time pressure
in a Mouselab decision task), there is still a substantial
proportion of participants integrating information in a
complex fashion (Bröder, 2003; Glöckner & Hodges,
in press; B. R. Newell & Shanks, 2003). This finding
is not surprising from the viewpoint of our component
model. Once information has been fed into the processing system, intuition will use this sample of information irrespective of amount and capacity. Individuals
seem to have an intuitive understanding of the potentials of their intuition. They do not spare their mind
but often seem to get as much information as possible.
Applying measures for strategy identification based on
choices, decision times, and confidence show that they
regularly use and integrate the entire set of encoded information (e.g., Glöckner, 2009; Glöckner & Bröder,
in press).
What happens if we release boundaries on input
formation so that information acquisition consumes
only minimal resources? Recall that multiple strategy
models still predict that costs will increase with increasing amounts of information, because integrating
involves costly EIPs as well. In our decision studies, we created such an easy-access situation using
an open information board. Outcome information was
binary and depicted in symbols (+, –). Only cue validities were shown as numbers. For simplicity, assume that the costs for input formation are minimal
in this case. Thus, processing time (as an indicator
of cognitive effort) should vary only with effort invested in output formation. In contrast to this prediction of the multiple strategy models, we found
no main effect for amount of information. Rather we
found that processing time covaried with holistic pattern characteristics. This finding nicely corroborates
the component model predicting that different factors constrain analyses and intuition in an orthogonal
This brief discussion may illustrate how the
bounded rationality approach can be fruitfully advanced by a component view assuming that intuition
and analysis refer to different components rather than
to different types of strategies.
Toward an Integrative Model of Judgment and
Decision Making
The literature on judgment and decision making
hosts descriptions of a plethora of strategies and heuristics. Each strategy is a combination of processes, consisting of different rules for information search, comparison, integration, termination, and choice (so-called
building blocks; Gigerenzer et al., 1999). The model
outlined in the following starts with the assumption that
there is only one all-purpose rule for integration and
choice (for similar assertions of single strategy models,
see Lee & Cummins, 2004; B. R. Newell, 2005) but
that there are different rules for search, generation, and
change of information. In other words, we assume that
individuals use different strategies for input formation
but that they apply a general, multifunctional mechanism for output formation. This mechanism is assumed
to obey to the principles we described as the characteristics of intuition. It operates unintentionally and
automatically, and it processes information in parallel.
We suggest that this mechanism can be understood and
formally described in terms of a connectionist model.
This assertion, however, does not preclude that there
might also be other automatic mechanisms involved in
decision making (see Glöckner & Witteman, 2010a,
for a discussion).
Connectionist models have been applied to a variety of areas in cognition such as reading (McClelland
& Rumelhart, 1981), explanatory coherence (Thagard,
1989), attribution (Read & Marcus-Newhall, 1993),
stereotyping (Kunda & Thagard, 1996), and decision
making (e.g., Glöckner et al., in press; Holyoak & Simon, 1999; Thagard & Millgram, 1995). In recent articles, we advanced a specific version of the connectionist approach, the parallel-constraint-satisfaction (PCS)
model, to apply to multiattribute decision making
(Betsch, 2005; Glöckner & Betsch, 2008a). In the following sections, we sketch the major assumptions of
our advanced PCS model and discuss some implications and empirical predictions.
Input: The Working Network
The connectionist approach uses a network
metaphor to model how information is represented
in the mind (Holyoak & Spellman, 1993). Elements
(nodes) and weights on the connections between the
elements form the structure of the network. A working
network represents the decision at stake. It contains features of the situation, options, goals, and outcome information (see symbolic connectionism; Holyoak, 1991).
It may also contain other pieces of information that
have been recently encoded or activated from memory. Connections between the elements can represent
their learned relation reflecting, for instance, covariation, rates of reinforcement and punishment, logical
relations such as negation or causation, and so forth.
Formally, these relations are expressed by positive or
negative weights on the connections. When activation
is passed through the network, a positive weight causes
activation and a negative weight causes deactivation of
a linked element.
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Output: Application of the PCS Rule
A decision is made by applying a PCS rule (see
Read, Vanman, & Miller, 1997, for an excellent introduction). Information is passed in parallel through the
network. The activations of the elements are simultaneously updated and constrained by the given structure
of weights in the working network. By iterating this
updating process, the PCS rule strives for a solution
in which all elements optimally cohere with all other
elements taking into account the constraints in the network in a parallel manner. This occurs in an iterative
process of updating node activations. In most simple
networks, activation of all nodes reaches an asymptotic
level after a small number of iterations and the process
is terminated. Activations of elements are used as the
basis for judgments or decisions. In the case of decisions, the options with the highest activation will be
chosen (see Betsch, 2005, for a variant of this rule). In
the case of making a judgment about a single object
on a certain criterion dimension, estimations reflect the
strength of the element’s activation.
Bottlenecks to the PCS Process
Because the PCS rule attempts to maximize coherence, the effectiveness and speed of this process is
mainly dependent on the holistic characteristics of the
information pattern but not on the number of elements.
If there is already a high level of coherence, the PCS
rule needs only a few iterations to arrive at a solution.
Accordingly, those decisions can be made very quickly
regardless of the amount of information contained in
the working network. On the other hand, if initial coherence in the pattern is low (e.g., because there is
contradicting evidence activating and deactivating options at the same time), it will take the PCS rule more
iterations to change the pattern in order to achieve
coherence. On the phenomenological level, incoherence will manifest itself in extension of time needed to
make a decision. Iterations in a PCS network have been
proven to be a good predictor for decision times even
on an individual level (Glöckner & Bröder, in press;
Glöckner & Hodges, in press). In a network structure
with a high degree of contradiction and/or high desired
level of coherence (threshold θ ; Glöckner & Betsch,
2008a), PCS can fail to reach a solution of acceptable
coherence. Under such condition the network must be
restructured (see next).
There are other bottlenecks that are due to analytic
thinking. We noted earlier that trying to supervise intuition may decrease its effectiveness. In the experiments
on implicit formation of preferences (Betsch, Plessner,
& Schallies, 2004, for an overview), we found that
trying to explicitly form a preference hindered participants from reproducing their intuitively formed pattern
of preferences that so closely mirrored their actual experiences.
Recall the assumed functioning of PCS. By parallel passing of activation, the activation of elements is
changed to satisfy the characteristics of the entire pattern of interrelations among elements (the structure of
weights in the network). The resulting activation of an
element emerges within the network as a result of an iterative updating process. Analytic thinking is a process
by which the focus of attention is directed to elements
of the network. In terms of connectionist modeling, the
focused element receives a dramatic change in activation from outside the network. These changes may help
or hinder PCS to reach a solution. If the individual focuses pieces of information that, for instance, promote
the dominating option, than PCS can jump toward termination. However, if the focused elements are those
that need to be deactivated to achieve coherence, analytic thinking bugs PCS. In the case of our studies,
individuals could not systematically access relevant
characteristics of prior outcome distributions. Therefore, attentional focus (explicitly considering certain
outcomes) may have varied randomly and diluted or
even destroyed appropriate differences in the activations of the options.
The Algebra of Intuitive Processing
PCS employs a nonlinear rule for cue activation (i.e.,
a sigmoid activation function; McClelland & Rumelhart, 1981). Nevertheless, simulated distributions of
the output of this process converge with those produced by a weighted summation (or weighted additive
rule; cf. Glöckner & Herbold, in press; Glöckner &
Hodges, in press). The results from simulation converge with empirical findings. For judgments it has
been shown that they can be approximated well by a
weighted linear model (see Brehmer, 1994; Hammond,
Hamm, Grassia, & Pearson, 1987; Karelaia & Hogarth, 2008). We also found that evaluations reflect the
sum of all values of prior experiences with the target
object and not only their numerosity (e.g., Betsch et al.,
2001). In our decision studies with an open information board, even decisions under time pressure showed
that the pattern of choices mapped on the differences
between the alternatives in terms of their weighted
sum of values (Glöckner & Betsch, 2008a, 2008b,
2010). Furthermore, it should be mentioned that, in
strong support of the PCS perspective, a coherence effect, that is, a systematic reevaluation of evidence in
favor of the leading option in the decision process,
has been demonstrated in many kinds of decision tasks
(e.g., DeKay, Patino-Echeverri, & Fischbeck, 2009a,
2009b; Glöckner et al., in press; Holyoak & Simon,
1999; Russo, Carlson, Meloy, & Yong, 2008; Russo,
Meloy, & Medvec, 1998; Russo, Medvec , & Meloy,
1996; D. Simon, Krawczyk, & Holyoak, 2004; D. Simon, Pham, Le, & Holyoak, 2001; D. Simon, Snow &
Read, 2004, for similar earlier theoretical approaches;
see also Montgomery, 1989; Svenson, 1992). Furthermore, recent findings indicate that arousal does indeed
increase with decreasing coherence in decision tasks
as suggested by PCS models (Glöckner & Hochman,
in press; Hochman et al., 2010).
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The Interplay of Analytic and Intuitive
PCS processes are assumed to function by default,
regardless of whether analytic processes are used additionally (for an overview of other recent defaultinterventionist models, see Evans, 2008). According to
the component view, intuitive and analytic processes
serve different functions. We described the interplay
between these processes in detail in a previous paper (Glöckner & Betsch, 2008a). We referred to analytic operations as processes of deliberate construction
(DC). DC involves input formation such as active information search, generation, and change. In our PCS
model, we assumed that DC is not a necessary condition for reaching a decision. Sometimes, salient information in the environment and accessible information
from memory will provide an input sample of information that suffices to reach the necessary level of
coherence for a judgment or decision to be made. This
may be primarily the case for many routine decision
situations that typically do not require additional information search and generation. In other situations, DC
will always be involved. For example, in the Mouselab
used in decision research, the options are unfamiliar
to the participant and information is hidden in a matrix. Under such conditions, a working network must
be actively formed prior to PCS processing. In the remainder of this section we outline two examples of
how intuitive and analytic processes interact.
An important potential of DC is to change information temporarily in an intended direction. Intuition,
on the contrary, does not have such powers. Specifically, PCS processes can change only activations, not
the structure of the network. Many aspects of the network stem from prior learning. For example, a strong
positive weight on a relation between an option and a
goal may reflect reinforcement history. Such weights
provide the given constraints under which PCS must
find a solution. If decisions were solely guided by PCS,
individuals would have difficulty instantly adapting to
changes in the world that render prior behavior obso290
lete. However, weights can be temporarily changed by
DC (whereas lasting changes might require feedback
learning). Assume an individual consciously realizes
that a routine behavior is inappropriate under the given
decision situation. This change in perceived instrumentality can be modeled as a change of the weights on
the association between the goals and the behavior.
Maintaining this change, however, requires cognitive
control and, hence, cognitive effort. Factors that additionally consume cognitive resources reduce the likelihood that changes can be maintained during a decision. The lower the capacity for deliberative control
over the representation of the decision problem, the
higher the likelihood that weights will reach their prior
state. Hence, one would predict that deviation from
a decision routine under altered situational conditions
requires DC processes to function effectively, a prediction that is strongly corroborated by the evidence (e.g.,
Betsch, Fiedler, & Brinkmann, 1998; Betsch, Haberstroh, & Höhle, 2004). This example shows how intuitive and analytic processes both have their specific
powers. Their interaction and collaboration is necessary to promote adaptive decision making.
By now, we have considered only situations in
which the input (working network) allows for effective
application and termination of PCS processes. What
happens, however, if PCS fails to achieve an acceptable level of coherence in the network (i.e., coherence
is below the aspired threshold)? In our PCS model, we
assumed that failure to achieve coherence is an endogenous factor that instigates DC operations (Glöckner &
Betsch, 2008a). We assumed that upon failure a secondary working network is constituted containing DC
operations and strategies as options. Again, the PCS
rule is applied to this secondary network to select a DC
operation, which is then applied to change the primary
network representing the original decision problem.
Although these assumptions are not yet formally implemented in the model, they provide a starting point
for theoretically modeling the selection of strategies
for information search and change.
Directions for Future Research
The majority of studies on JDM still employ written descriptions of relevant information. For instance,
the gambling paradigm (the “drosophila” of decision
research, cf. W. M. Goldstein & Hogarth, 1997) provides individuals with stated probabilities and stated
values. In this paradigm, encoding of information systematically involves only one channel. Moreover, the
individual can rely only on the (new) stimulus information from the environment and not on prior knowledge.
Conversely, in natural settings, decision information
stems from multiple sources and can be simultaneously
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encoded via multiple channels (e.g., visual, auditory,
For example, assume you visit a restaurant with
friends. The menu contains pictures of the different
dishes and written description of their ingredients.
Moreover, your friends comment on the menu, utter
their own preferences, and may evoke examples of their
prior experiences. You can additionally capitalize on
your experiences with eating similar dishes that come
to your mind easily in form of cognitive and affective
representations. In such a situation, input information
to the decision is fed through multiple channels (oral,
visual, memory). Different types of information are
encoded simultaneously. In a relatively effortless fashion, you are provided with a rich informational base for
your decision. In these and similar situations that are
encountered frequently in everyday life, intuition can
reveal its powers. Integration and preference formation
happens en passant. Unfortunately, multimodal situations are as of yet very rarely considered in decision
research. The naturalistic decision-making approach
must be mentioned as an exception (Klein, 1999; Klein,
Orasanu, Calderwood, & Zsambock, 1993). Studies in
this area are often conducted in the field yet suffer from
insufficient experimental control. Change and development of research tools toward multimodal stimulus
presentation is both a challenging and promising task
for the future.
The processes of intuitive thinking do not apply
only to judgment and decision making. The connectionist approach views these processes as fundamental
to many areas of cognition including, for instance, perception and speech comprehension. Accordingly, one
should expect that extensive integration of information
can be performed by decision makers from an early
stage on. For example, preschool children who already
do well in speech comprehension should be capable of
applying the PCS rule to decisions without effort and
instruction. Unfortunately, we still know little about
how children make decisions. Most decision research
with children is conducted and published in the field
of developmental psychology (e.g., Davidson & Hudson, 1988; Gao, Li, Bai, Lin, & Wei, 2009; Knight,
Berning, Wilson, & Chao, 1987; Schlottmann, 2001).
Members of the JDM community only occasionally
study child decision making (e.g., Levin, Weller, Pederson & Harshman, 2007; Reyna & Ellis, 1994). We
see much merit in extending this line of research with
a stronger focus on how children intuitively integrate
information. We believe that valuable lessons about
intuitions and analysis can be learned in this domain
(cf. Jacobs & Klaczynski, 2005). The few studies that
use functional measurement in nonadult participants
provide first evidence in favor of our notion assuming that integration of multiple pieces of information
can be easily performed even from an early age (e.g.,
Ebersbach, 2009; Schlottmann, 2001). For example,
Ebersbach (2009) demonstrated that kindergarten children show remarkabe capabilities of spontaneously integrating three orthogonally varying stimulus dimensions in their judgments of volume. To drive this argument to the extreme, many studies show that even
monkeys (Platt & Glimcher, 1999) and sticklebacks
(Künzler & Bakker, 2001) make choices indicating
an integration of information in a weighted compensatory manner. Research and theorizing in the field
of judgment and decision making might benefit from
acknowledging these findings.
According to the advocated approach to intuition,
not all processes of thinking are constrained by limits
of computational capabilities. We suggested that intuition can capitalize on the powers of autonomous,
automatic, nonintentional processes that are characterized by parallel processing. These parallel processes
are much less constrained by the amount of information. Extensive consideration of multiple pieces of information can happen in an astoundingly narrow time
frame. We suggested that intuitive processes are responsible for transforming a sample of input information into an output (judgment, choice). We claimed that
there are not two modes of thinking that can be used
alternatively. Rather, we propose that intuitive and analytic processes are components that operate together
to form judgment and decisions. Such a component
view can be integrated in a connectionist model to decision making. We showed that such a model helps to
advance the bounded rationality approach as well as
our understanding of strategic adaptation to situational
Address correspondence to Tilmann Betsch, Department of Psychology, University of Erfurt, Nordhauser Strasse 63, D-99089 Erfurt, Germany. E-mail:
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