IS SIMPLER BETTER? TESTING THE RECOGNITION HEURISTIC
Zachariah Basehore
A Thesis
Submitted to the Graduate College of Bowling Green
State University in partial fulfillment of
the requirements for the degree of:
Master of Arts
August 2015
Committee:
Richard B. Anderson, Advisor
Mary Hare
Sherona Garrett-Ruffin
© 2015
Zachariah Basehore
All Rights Reserved
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ABSTRACT
Richard B. Anderson, Advisor
There is debate over whether or not the recognition heuristic is a good model for
participants’ behavior on a binary forced-choice task. Some literature shows that the recognition
heuristic models 90% of participants’ decisions on average; other studies demonstrate that people
incorporate cues other than recognition before making a decision. There is also evidence that
people adjust their reliance on the recognition heuristic, based on its performance in a given
environment.
To ascertain if the recognition heuristic is truly used as a decision strategy, Experiments 1
and 2 provided a pure test of the recognition heuristic. In these experiments, participants chose
which of two fictitious cities they thought was more populous—one of the city names was
presented earlier in the experiment, and the other was completely novel. Participants in
Experiment 1 made an average of 65% recognition heuristic-consistent decisions; participants in
Experiment 2 made 59% recognition heuristic-consistent decisions, on average. Both proportions
were significantly greater than chance.
Experiment 3 tested learning with feedback as an explanation for participants' apparent taskbased adjustment of their reliance on recognition as a decision cue. First, participants underwent
a training phase in which they chose which of two cities they believed was more populous. They
received immediate feedback on their accuracy, as well as the actual populations of each city.
Participants then completed an experimental judgment task (similar to the training phase, but
with no feedback). Participants in a low-recognition-validity training condition subsequently
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made significantly fewer recognition-consistent decisions than those in the medium- and highrecognition validity training conditions.
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This thesis is dedicated to my advisor, Dr. Richard B. Anderson,
my parents, David and Crystal Basehore,
and all my friends (you know who you are).
Ad beneficium hominis et Dei gloriam
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ACKNOWLEDGMENTS
Thanks to my advisor, Dr. Richard B. Anderson, in gratitude for his extensive assistance
in the development of this idea. Thanks also go to Dr. Sherona Garrett-Ruffin and Dr. Mary Hare,
members of my thesis committee, for providing their time, insight and valuable perspectives on
this research. Many thanks to my parents and to all my friends, for their undying support and
encouragement.
Special thanks also to Adam Billman, whose observations challenged me to address a
couple of the questions I tackled in this thesis, and to Jared Branch for his help in running
participants. Thanks to Aaron Nemoyer for his assistance with properly formatting the
dedication, and to Brent Lang and Chris Arnold for engaging me in constructive discussion
about the topic.
Thanks to Dr. Michael Kitchens, Dr. Lou Manza, Dr. Michelle Niculescu, and the faculty
and staff at Lebanon Valley College, for their invaluable expertise and guidance as I embarked
on my academic career and grew from a boy into a man.
Finally, I wish to acknowledge the work of Gerd Gigerenzer and his numerous colleagues
at the Max Planck Institute's Center for Adaptive Behavior and Cognition, for showing me a new
way of thinking about thinking.
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TABLE OF CONTENTS
Page
CHAPTER I. INTRODUCTION …………………………………………………………..
1
Historical Background……………………………………………………………….
1
Heuristics…………………………………………………………………………….
2
A New Approach…………………………………………………………………….
3
The Recognition Heuristic…………………………………………………………...
5
The Debate…………………………………………………………………………...
6
The Problem…………………………………………………………………………
10
CHAPTER II. METHODOLOGY………………………………………………………….
13
Experiment 1………………………………………………………………………… 13
Overview…………………………………………………………………….. 13
Procedure……………………………………………………………………. 13
Experiment 2………………………………………………………………………… 14
Overview…………………………………………………………………….. 14
Procedure……………………………………………………………………. 14
Experiment 3………………………………………………………………………… 14
Overview…………………………………………………………………….. 14
Procedure……………………………………………………………………. 15
CHAPTER III. RESULTS………………………………………………………………….
17
Experiments 1 and 2…………………………………………………………………
17
CHAPTER IV. DISCUSSION……………………………………………………………… 25
Experiments 1 and 2…………………………………………………………………
25
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CHAPTER V. RESULTS…………………………………………………………………… 31
Experiment 3………………………………………………………………………… 31
CHAPTER VI. DISCUSSION………………………………………………………………
35
Experiment 3………………………………………………………………………… 35
CHAPTER VII. GENERAL DISCUSSION AND IMPLICATIONS………………………
37
CHAPTER VIII. LIMITATIONS AND FUTURE DIRECTIONS…………………………
39
CHAPTER IX. CONCLUSION……………….……….……………………….…….…….
41
REFERENCES………………………………………………………………………………. 43
APPENDIX A. LIST OF FICTITIOUS CITY NAMES …………………………………. 49
APPENDIX B. PILOT STIMULI………………………………………………………… 50
APPENDIX C. LOW-VALIDITY (0.2) TRAINING CONDITION STIMULI…………… 53
APPENDIX D. MEDIUM-VALIDITY (0.5) TRAINING CONDITION STIMULI……… 54
APPENDIX E. HIGH-VALIDITY (0.8) TRAINING CONDITION STIMULI…………… 55
APPENDIX F. EXPERIMENTAL JUDGMENT TASK STIMULI………………………. 56
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LIST OF FIGURES
Figure
Page
1
Frequency of RH-consistent decisions in Experiment 1 ............................................
18
2
Frequency of RH-consistent decisions in Experiment 2............................................
19
3
Mean proportion of RH-consistent decisions in Experiment 1 vs. Experiment 2......
20
4
Mean reaction times for recognition-consistent and recognition-inconsistent decisions
(in Experiment 1).......................................................................................................
5
21
Mean reaction times for recognition-consistent and recognition-inconsistent decisions
(in Experiment 2)........................................................................................................
22
6
Individual RH adherence rates in Experiment 1 ........................................................
26
7
Individual RH adherence rates in Experiment 2........................................................
27
8
Individual RH adherence rates in Experiments 1 and 2.............................................
28
9
Mean proportion of recognition-consistent decisions by training condition
(in Experiment 3)........................................................................................................
10
32
Mean reaction times for recognition-consistent and recognition-inconsistent decisions
(in Experiment 3)........................................................................................................
34
1
CHAPTER I. INTRODUCTION
Historical Background
The modern study of human decision-making began as the purview of economists in the first
half of the 20th century, beginning with Lionel Robbins’ original publication of a tract in 1932
that was meant to establish economics as a more precisely defined science (Robbins, 1945). In
order to simplify the process of creating mathematical models to describe decision-making,
classical economists built their models based on the assumption that people maximize their
expected utility. That is, economists assumed that people will make the best decision possible,
regardless of the amount of time and information required to make such a decision.
In the 1950s, economist Herbert Simon argued that such an assumption was erroneous
(Simon, 1955). He instead suggested bounded rationality, based on the notion that people
“satisfice,” or select the first option that meets certain criteria. According to Simon, the criteria
can vary, based on an individual’s expertise, the attributes of the task itself, and the
characteristics of the environment. This theory aimed to show that people make reasonable
decisions, without having to behave in a perfectly logical manner.
Simon—who would go on to win a Nobel Prize in Economics for his work—ultimately left
quite an impact on the study of decision-making, in his arguments that: 1) researchers should
empirically investigate actual decisions in the lab and in the field; 2) researchers need to consider
the task and the environment, as well as the cognitive process; and 3) formal models need to be
based on, and further tested by, empirical data (Campitelli & Gobet, 2010).
In the 1970s, Daniel Kahneman and Amos Tversky published a series of articles on
experiments that tested people’s decision outcomes. Kahneman has written that in the early
1970s, when he first heard about the assumption of classic economics (that people are perfectly
2
rational agents), he found it ridiculous (Kahneman, 2003)! His subsequent work aimed to
demonstrate that this assumption does not hold. Kahneman and Tversky’s line of research
became watershed studies, triggering the growth and development of the field of
decision-making research within psychology. Much of this research was based on the idea that
people are irrational.
In fact, the decision-making patterns observed in the lab aren’t always consistent on
variations of the same task! The classic Wason card-selection task demonstrated that less than
10% of people make selections that conform to the rules of classic logic (Wason, 1966; Wason,
1977). This effect is remarkably consistent, even when remedial procedures (such as
experimental instructions, or prior experience in the lab) are implemented with the intent to get
people to avoid making these errors (Wason, 1968; Wason & Shapiro, 1971).
However, when a task with the same logic is presented in a naturalistic manner, people
exhibit much higher adherence to the rules of logic (Wason & Shapiro, 1971; Cox & Griggs,
1982). Almor & Sloman (1996) showed that the causal structure of the task (whether or not two
variables are causally related) will also impact participants’ performance.
The findings of Wason and Shapiro (1971), Cox and Griggs (1982), and Almor and Sloman
(1996) all demonstrate that reasoning is not an isolated process; rather, it is highly dependent on
the environmental context. Thus, Simon’s approach is empirically supported: Researchers need
to consider both the decision-maker’s cognitive structure, and the environment’s structure.
Heuristics
Tversky and Kahneman’s groundbreaking research (e.g. Tversky & Kahneman, 1973;
Tversky & Kahneman, 1974) led to the discovery and identification of several “heuristics,” or
shortcuts to rational thinking. These heuristics include availability, representativeness, and
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anchoring-and-adjustment (Tversky & Kahneman, 1974). Because this research used heuristics
to explain why people’s decisions were incorrect, “heuristics” became shorthand for “wrong
thinking” to decision-making researchers (Goldstein & Gigerenzer, 2002).
Gintis (2007) notes the limits of many psychology textbooks when it comes to describing
human decision making. Of those psychology textbooks that even address decision-making
research, the view they frequently communicate is that people are fundamentally irrational. This
implies that people are globally poor decision-makers, because they are subject to biases such as
the heuristics listed above.
This implication is a serious overextension of the existing data, which shows that people
make predictable errors under certain circumstances. Kahneman and Tversky would agree,
considering the penultimate sentence in their seminal 1974 paper: “These heuristics are highly
economical and usually effective, but they lead to systematic and predictable errors” (Tversky &
Kahneman, 1974, p. 1131). Although Simon’s work is echoed in that sentence, the idea persists
nonetheless that humans are irrational creatures who make self-defeating decisions.
A New Approach
Gerd Gigerenzer established his scientific reputation by ferociously attacking this notion,
arguing that decision-making researchers have missed the point. They hold people to a standard
of rationality that is too narrow, according to Gigerenzer, and they have also ignored important
concepts in probability (Gigerenzer, 1991; Gigerenzer & Todd, 1999). The major thrust of his
argument is that problems like the Wason card-selection task (in its original form) or Tversky
and Kahneman’s famous “Linda problem” are not good tests of reasoning.
According to Gigerenzer, they are abstract, unrepresentative tasks that fail to properly
account for people’s perceptions of statistical concepts such as frequency distribution. These
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tasks essentially trick people—hence the “systematic and predictable errors” noted by Tversky
and Kahneman, and other researchers after them (Gigerenzer, 1991).
Gigerenzer proposes that researchers should compare behavior not to a standard of logic, but
to the standard of how well a strategy fares in real-world environments (Gigerenzer & Todd,
1999). This argument draws on Brunswick (1955), who advanced the idea that tightly controlled
laboratory situations can destroy the kinds of covariation found in nature, which could lead to the
biases described by Kahneman and Tversky, and other researchers. Dreyfus (1997) pointed out
that this is a limitation of many prior studies, whose contrived nature forced participants to
behave in what he termed “non-skillful ways” (p. 23).
Since laypeople could be considered ‘expert’ decision-makers in many everyday areas, by
virtue of their life experience, it is crucial for researchers to create tasks that are more
representative of everyday decision environments. This view is consistent with Klein’s
Naturalistic Decision Making paradigm (Klein & Klinger, 1991; Zsambok, 1997) and is
emphasized by Gigerenzer in multiple articles.
Gigerenzer’s answer, then, is to promote an approach he calls “ecological rationality”
(Gigerenzer & Todd, 1999; Goldstein & Gigerenzer, 2002). Arguably, his biggest contribution is
the notion that heuristics are adaptive and useful methods for people to generate predictions in
daily life, because heuristics are fast, frugal, and accurate.
That is, heuristics are fast because they enable people to make decisions more quickly than
by deliberative thinking, and they are frugal because implementing heuristics requires little
effort. In many circumstances, simple heuristics are also accurate, because they take advantage
of environmental structures in such a way that their predictive accuracy is at least as good as
traditional mathematical models, such as multiple linear regression or weighted linear models
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(Gigerenzer & Goldstein, 1996; Czerlinski, Gigerenzer, & Goldstein, 1999).
This counterintuitive occurrence is called the “less-is-more effect,” because less knowledge
sometimes leads to increased accuracy (Goldstein & Gigerenzer, 1999)! This is precisely why
Simon’s principle of considering the task environment is so critical to Gigerenzer’s approach:
People rely on strategies that take advantage of the task environment’s structure to make fast,
frugal, and accurate decisions.
Since different heuristics are theorized to work well in different environments, Gigerenzer
proposes that humans use an “adaptive toolbox.” This toolbox is filled with a variety of simple
strategies that lead to quick, accurate, and relatively effortless decision-making (Gigerenzer,
2001). Depending on the nature of the problem at hand, people select whichever heuristic would
be appropriate to solve that particular problem.
The Recognition Heuristic
One of the most frugal heuristics proposed by Gigerenzer and colleagues is the recognition
heuristic (Goldstein & Gigerenzer, 1999; Goldstein & Gigerenzer, 2002). It is defined: “If one of
two objects is recognized and the other is not, then infer that the recognized object has the higher
value” (Goldstein & Gigerenzer, 1999, p. 41). Use of this heuristic will lead to incorrect
predictions in cases where the recognized object has a smaller criterion value (Goldstein &
Gigerenzer, 2002). Note that recognition is the only cue considered: thus, this heuristic is often
described in existing literature as “noncompensatory.” To avoid confusion over this word, I will
use “single-cue” to describe the original formulation of the heuristic, and “multiple-cue” when
cues other than recognition are incorporated into the decision.
This heuristic was clearly demarcated to apply only to binary choices, and only when one of
the two objects is recognized. This heuristic is most useful when differential recognition, across
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the two objects, is actually correlated with which object has the higher criterion value
(Gigerenzer & Goldstein, 2011; Goldstein & Gigerenzer, 2002).
In their seminal example, judging which of two cities has a larger population, this
relationship is pretty clear. If a participant (who we’ll refer to as “Bob”) recognizes Seoul, South
Korea but not Daegu, South Korea, then Bob will select Seoul as being more populous. Indeed,
as a general rule, Bob is more likely to have heard of large, populous cities than smaller cities.
Thus, the recognition heuristic exploits the correlation between the criterion and the environment
(hereafter referred to as the “ecological correlation,” after Gigerenzer and colleagues), in order to
be a useful tool for making the correct decision.
Goldstein and Gigerenzer (1999) were noncommittal as to how people learn this ecological
correlation. Animals such as rats have been observed to use similarly simple rules when deciding
whether to eat some piece of food. In this instance, Goldstein and Gigerenzer speculated that the
direction of ecological correlation is genetically coded. No evidence was offered to support this
supposition, other than that this behavior is observed even in young rats. Since trial-and-error
here would necessarily result in sickness or death, experiential learning could not occur very
well! In other circumstances, however, Goldstein and Gigerenzer proposed that experiential
learning informs individuals of the ecological correlation.
The Debate
From the traditional perspective of optimization and logic, making decisions based on mere
recognition seems like a silly, suboptimal approach (Goldstein & Gigerenzer, 2002). It’s entirely
possible—even plausible—that Bob does not recognize a large city like Guangzhou, China
(population 18.3 million) but he recognizes a much smaller city near his home, like Columbus,
Ohio, USA (population 1.4 million).
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Various studies have evaluated the usefulness of recognition in predicting stock
performance, predicting winners in sporting events such as soccer and tennis, inferring lifetime
scoring among hockey players, and forecasting election results (summarized in Gigerenzer &
Goldstein, 2011). Such studies have supported the recognition heuristic as a useful strategy.
It is much tougher to demonstrate that people actually rely on the recognition heuristic. For
example, Gigerenzer & Goldstein’s 2011 reanalysis of data from a Richter & Späth article
showed that more than half of participants in one of the experiments showed 97-100% behavioral
consistency with the recognition heuristic (even though information presented in that experiment
conflicted with the recognition heuristic!). But this individual-level reanalysis led to a different
conclusion than the original paper, which analyzed the same data in aggregate. This demonstrates
that the way data is analyzed (aggregate vs. individual) makes a difference in the outcome, and
that the recognition heuristic can still be a useful explanation for people’s decisions, even when it
does not account for everyone’s behavior.
Indeed, Oppenheimer (2003) showed that people sometimes use recognition as one of
multiple cues. When Stanford students were asked to judge which city has a larger population—a
local city like Sausalito that participants would recognize (and know that it was small) or a
made-up (and hence unrecognized) city like Heingjing—participants chose the unknown city on
63% of the trials. This demonstrates that people’s decisions sometimes contradict Goldstein and
Gigerenzer’s formulation of the recognition heuristic. Hilbig (2010) reviewed this and other
evidence that the recognition heuristic is multiple-cue, not single-cue as Goldstein & Gigerenzer
(1999; 2002) proposed.
Other studies also suggest that people incorporate additional evidence aside from
recognition. Newell and Shanks (2004) showed that people consider both recognition and expert
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advice in a stock market simulation. Bröder and Eichler (2006) similarly found that participants
considered recognition as well as other cues, such as the presence of a soccer team or an intercity
train line, when deciding which of two cities is more populous. Glöckner and Bröder (2014)
asked Germans which of two medium-sized American cities is more populous. They determined
that a weighted, multiple-cue model called PCS (Parallel Constraint Satisfaction) was a better
predictor of decisions than the recognition heuristic or the take-the-best heuristic for the majority
of participants (Glöckner & Bröder, 2014).
However, some other studies suggest that people do rely on the recognition heuristic (e.g.
Marewski et. al., 2010, who found that recognition best modeled people’s predictions of election
outcomes). In another experiment, Pachur, Bröder, and Marewski (2008) found evidence that
participants often do not override recognition when making judgments. This occurred even when
the participants themselves judged a different cue (presence or absence of an international
airport) to be a more valid cue than recognition! This finding directly contradicts the earlier
findings of Bröder and Eichler (2006), Richter and Späth (2006), and Newell and Shanks (2004).
The explanation put forth by Pachur, Bröder, and Marewski (2008) is that participants
behave differently when they rely on naturally-occurring (rather than experimentally-induced)
knowledge. The thrust of this argument is that in the other studies, experimenters explicitly
provided participants with cues other than recognition, which strongly influenced participants to
consider those highly salient cue values when making a decision.
Furthermore, Pachur and Hertwig (2006) found that in environments in which the ecological
correlation of recognition is low, people tended not to make a decision consistent with the
recognition heuristic. Responses that did conform to the predictions of the recognition heuristic
in Pachur and Hertwig’s experiment were notably faster, which supports Shah and
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Oppenheimer’s (2008) effort-reduction framework for understanding heuristics.
Pachur and Hertwig (2006, p. 999) suggested that people “evaluate” or “filter” the
recognition heuristic before applying it, particularly when object-specific knowledge conflicts
with the heuristic. Newell and Shanks (2004) and Newell (2011) also advanced the idea of an
evaluative stage.
According to Pachur and Hertwig’s (2006) findings, people are able to tell with surprising
effectiveness when recognition is a poor predictor of the correct answer. This evidence forced
Gigerenzer and Goldstein (2011) to admit that the single-cue recognition heuristic is suspended
under certain circumstances, such as when the ecological correlation is not high.
Several researchers have tried to account for the conflicting findings; Marewski, Pohl, and
Vitouch (2011) did an excellent job of summarizing and explaining these attempts. They note
Erdfelder and colleagues’ proposal of a “memory state heuristic” (as cited in Marewski et. al.,
2011, p. 361) that includes a person’s degree of confidence in his or her recognition. However,
this proposition seems similar to the fluency heuristic as described by Hertwig, Herzog,
Schooler, and Reimer in 2008 (first identified by Jacoby and Dallas, 1981).
Marewski, Pohl, and Vitouch (2011) go on to extol the virtues of a cognitive architecture
such as Anderson’s ACT-R, in order to better integrate experimental findings, and link them with
other disciplines. Consistent with that position, I argue that the study of heuristics as cognitive
mechanisms fits into the different modules of the ACT-R architecture as described by Anderson
et. al. (2004). Nellen (2003) was the first to use ACT-R as an architectural basis for heuristics;
Marewski and Schooler (2011) used the ACT-R architecture to model selection strategies for
different heuristics whose domains may overlap.
Also within the ACT-R architecture, Marewski and Mehlhorn (2011) concluded that
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behavioral data is best accounted for in so-called “race models” (p. 442) in a dual-process model:
that is, single-cue and multiple-cue processes ‘race’ to a cognitive bottleneck. This finding makes
sense in light of research showing that people tend to rely on faster and simpler strategies when
they are put under time pressure (e.g. Rieskamp & Hoffrage, 1999).
Finally, Stanovich, West, and Toplak (2011) integrated the results of a variety of studies to
argue for a dual-process, default-interventionist model of reasoning. This model accounts for
existing findings by assuming that humans have two different ways of thinking. “Type 1”
processing is automatic, fast, and requires relatively low resources; “Type 2” processing is
deliberative, slow, and computationally intensive. According to Stanovich, West, and Toplak’s
model, the Type 1 process is the default because of its automaticity and low resource
requirements. Type 2 processing, then, is required to override Type 1 processing on many tasks
that are commonly encountered in the lab, as well as for more intricate real-world tasks such as
financial decisions and legal judgments (Stanovich, West, & Toplak, 2011).
Historically, psychologists like Kahneman and Tversky have spent more time researching
and writing about errors and biases than strengths and virtues, as noted by Sheldon and King
(2001). The aim of positive psychology is to address this imbalance and use scientific methods to
better understand human well-being (Seligman & Csikszentmihalyi, 2000). This perspective
underscores the importance of addressing how, why, and when heuristics work well.
The Problem
Since Kahneman and Tversky’s groundbreaking research in the 1970’s, the study of
heuristics has been established as an approach to how people make decisions in an uncertain
world. However, Gigerenzer’s fast-and-frugal program is less than 20 years old, so much is
unknown about how and when people use heuristics adaptively. These problems include: how a
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given strategy is selected, why some people seem to rely on heuristics more often than others,
what kinds of situations are appropriate for the use of heuristics, how ecological correlations and
learning interact, and if Goldstein and Gigerenzer’s original (1999) formulation of the
recognition heuristic can truly account for choice behavior.
The present research addresses the latter two questions. The first two experiments
provided a pure experimental test to determine whether the recognition heuristic can truly
describe behavior in the simplest possible manner; a separate experiment tested whether or
not people learn to adjust their use of the recognition heuristic based on its appropriateness
for a given task.
A “pure” test of the recognition heuristic has not yet been conducted, though Oppenheimer
(2003) came closest. The first experiment in Oppenheimer's study paired small, local cities with
unknown, foreign-sounding cities [unbeknownst to participants, these foreign cities were
fictitious]. On about 60% of trials, participants guessed that fictitious cities like Rhavadran and
Heingjing were more populous than known, local cities like Sausalito and Fremont. His second
experiment found similar results: Participants picked the fictitious cities as more populous than
well-known cities such as New Haven, Timbuktu, Cupertino, or Nantucket.
Such judgments still may not properly represent participants' everyday decision-making
process. Again, this procedure confounded recognition with the further knowledge that the
recognized city was a small, local one (Experiment 1) or was known for a reason unrelated to
size (Experiment 2). Though Oppenheimer's study achieved its goal of shedding doubt on the
recognition heuristic, it left open the question of how much of a role recognition plays when
people make decisions.
All other previous studies on the recognition heuristic have confounded recognition with
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knowledge of further cues. If we are to know whether or not the recognition heuristic is actually
used as a decision mechanism, it is important to simply and directly test mere recognition,
without confounding it with further knowledge.
There is evidence that people may incorporate knowledge other than recognition (Newell &
Shanks, 2004; Oppenheimer, 2003; Bröder & Eichler, 2006), and that they adjust their use of the
recognition heuristic based on its usefulness in a given environment (see Pachur & Hertwig,
2006), yet no literature examines how people learn to do this. This lack of evidence warrants a
direct test of training on subsequent decisions.
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CHAPTER II. METHODOLOGY
All experiments used Qualtrics web-based survey software. A recognition survey consisting
of 100 cities was presented in ten different blocks of ten randomly presented cities. Participants
were asked only to indicate which cities they have heard of before. Cities were selected in
multiples of two from a given country; the pairs with the greatest rates of differential recognition
were used in Experiment 3.
Experiment 1
Overview. Experiment 1 attempted to induce the recognition heuristic according to
Goldstein & Gigerenzer’s original (1999) definition. Previous experiments on this heuristic
confounded recognition with related knowledge of other cues. This experiment presented
context-free stimuli, in order to remove this confound and directly test the original formulation of
the recognition heuristic. Due to this design, Experiment 1 represents a “pure” test of whether or
not recognition is used as a single-cue decision strategy. Participants in Experiment 1, and the
methodologically identical Experiment 2, were expected to make recognition-consistent
decisions on significantly more than 50% of the trials.
Procedure. For this experiment, participants were asked whether or not they recognize a
fictitious city, and to indicate their degree of confidence in their judgment. This procedure was
repeated ten times, with ten different fictitious cities (for a complete list of stimuli used in
Experiment 1, please see Appendix A). This was intended to encourage effortful
processing—and hence better encoding—of these city names. The order in which the stimuli
appeared was randomized.
Next, participants had 60 seconds to memorize a list of 25 words, and another 60 seconds to
recall as many of those words as they could. This was intended to serve as a distractor task, to
14
divorce the sense of recognition from the source of the recognition.
Finally, in the judgment task, participants selected which of two cities they thought was
more populous, on ten sequential trials. On each trial, one city was among the ten presented in
the first part of this experiment; the other was a novel fictitious city. The cities were matched for
purported country of origin, to eliminate the possibility that people would choose the city from
the more populous country. The order of response options in each pair was randomized, and the
order of the trials themselves were randomized as well. Reaction time data was collected.
Experiment 2
Overview. Experiment 2 served as a replication of Experiment 1. Because the cities were
fictitious, it is possible that some city names were more believable than others, or that some other
property of the stimuli would make people more likely to choose one over the other, for reasons
unrelated to recognition.
To eliminate this possibility, Experiment 2 induced recognition for the fictitious cities that
weren't used to induce recognition in Experiment 1. That is, Experiment 2 switched which city
from the judgment task was presented earlier in the experiment—therefore manipulating which
city was recognized.
Procedure. The procedure was the same as Experiment 1. The only difference was in the
first step, when recognition was induced for the ten cities that were not presented in the first step
of Experiment 1 (please see Appendix A). The rest of the experiment proceeded in exactly the
same manner as described above, with a new set of participants.
Experiment 3
Overview. Experiment 3 provided a more naturalistic decision environment than the
first two, because the judgment task used real cities. This was meant to address the
15
artificial nature of Experiments 1 and 2, and to fulfill the directive (Goldstein & Gigerenzer,
1999, 2002; Gigerenzer & Goldstein, 2011) that the recognition heuristic be tested in a way that
allows participants to exploit the natural structure of an environment.
This experiment was also intended to provide crucial information about how learning with
feedback affects subsequent use of a heuristic. Participants were expected to make fewer
recognition-consistent decisions in the low-recognition validity training condition than in the
medium- and high-recognition validity training conditions.
Procedure. Participants were randomly assigned to one of three training conditions. In this
training, they were asked to judge which of two real cities (matched for country of origin) is
more populous. Pairs of cities were presented in random order; response options were also
randomized. Participants received immediate feedback on their accuracy, as well as the actual
population of each city, after each of the ten sequential trials.
Each training condition used selectively sampled pairs of real cities, with the intent to
teach participants whether or not recognition is a good cue for a city's population.
In the high-recognition validity condition, the most well-known city (as determined by a
pilot survey—please see Appendix B for more detail) was the larger city on 8 of the 10 trials,
for a recognition validity of 0.8. In the medium-recognition validity condition, the most wellknown city was the correct answer on half of the trials, for a recognition validity of 0.5. In the
low-recognition validity condition, recognition validity was only 0.2. These training
conditions manipulated the validity of recognition as a cue to population size.
Participants in each of these training conditions then had the same experimental judgment
task: similar to the training phase, they judged which of two real cities is more populous. This
phase, however, did not involve feedback. In this task, the participants were shown ten pairs of
cities, one at a time. The order of the pairs was randomized, as were the response options.
16
Response times were measured, to help determine if recognition-consistent judgments were
made more quickly, as would be expected if participants were using a heuristic strategy.
Finally, participants in Experiment 3 were asked to complete a recognition survey, in order
to determine what proportion of each individual's decisions conformed to the recognition
heuristic. Participants were shown a list of all twenty cities from the experimental judgment task,
and were asked to identify which ones they recognized from before the experiment. This is
justified by Pachur & Hertwig’s (2006) finding that there was no difference whether participants
took such a recognition survey before or after completing a recognition judgment task.
For a complete list of the stimuli used in Experiment 3, please see Appendices B-F.
17
CHAPTER III. RESULTS
Experiments 1 and 2
The participants in Experiment 1 were 26 undergraduate students enrolled in an introductory
psychology course at Bowling Green State University. The sample included 24 women and 2
men, ages 18-23 (M = 18.73 years, SD = 1.28).
For Experiment 1, a one-sample t test indicated that the mean proportion of
recognition-consistent decisions was significantly greater than 0.5 (M = 0.654, SD = 0.139, t(25)
= 5.63, p < .001). Cohen’s d indicates a large effect size (d = 1.11). As seen in Figure 1 below,
the vast majority of participants made recognition-consistent decisions on at least 60% of the
trials.
18
Figure 1. Frequency of RH-consistent decisions in Experiment 1.
Proportion of recognition-consistent decisions, out of a possible 10 trials. The height of each bar
indicates the number of participants who made a certain proportion of recognition-consistent
decisions.
The participants in Experiment 2 were 28 undergraduate students enrolled in an introductory
psychology course at Bowling Green State University. This sample included 20 women and 8
men, ages 18-23 (M = 18.79 years, SD = 1.26).
A one-sample t test indicated that, similar to Experiment 1, the mean proportion of
recognition-consistent decisions was significantly greater than 0.5 (M = 0.593, SD = 0.170, t(27)
= 2.89, p = .007). Cohen’s d indicates a medium effect (d = 0.55). Figure 2 below shows that the
majority of participants again made recognition-consistent decisions on at least 60% of trials,
19
though the distribution more closely approximates a normal curve than Experiment 1.
Figure 2. Frequency of RH-consistent decisions in Experiment 2.
Proportion of recognition-consistent decisions, out of a possible 10 trials. The height of each bar
indicates the number of participants who made a certain proportion of recognition-consistent
decisions.
An analysis of the data of Experiments 1 and 2 together yielded similar results (M = 0.622,
SD = 0.157, t(53) = 5.71, p < .001, Cohen's d = 0.78).
Furthermore, as Figure 3 shows, there was no significant difference between the proportion
of recognition-consistent decisions in Experiment 1 and Experiment 2 (t(52) = 1.437, p = .157).
Cohen's d indicates a small effect size (d = .3875). This is evidence that the observed effect is
truly due to recognition, rather than other factors.
20
Figure 3. Mean proportion of RH-consistent decisions in Experiment 1 vs. Experiment 2.
Taken in combination, Experiments 1 and 2 provide evidence that participants make more
recognition-consistent choices than would be expected by chance. These results clearly
demonstrate that recognition is important to the decision-making process.
A look at the data for each individual participant reveals that of the 56 participants in
Experiments 1 or 2, only one made recognition-consistent decisions on all 10 trials. Furthermore,
only 11 out of the 56 participants made at least 8 recognition-consistent decisions—if
participants were using the recognition heuristic, few were doing so consistently!
Reaction time was used as a further test of whether or not participants were using the
recognition heuristic. If recognition-consistent decisions were made significantly more quickly
21
than recognition-inconsistent decisions, then this would further support the idea that participants
were using a heuristic process.
In Experiment 1, there was no significant difference between the reaction times for
recognition-consistent decisions (M = 4.59s, SD = 1.83) and recognition-inconsistent decisions
(M = 4.79s, SD = 2.31), t(25) = -0.711, p = .484. Cohen's d indicates a trivial effect (d = 0.10).
The mean reaction times for RH-consistent and RH-inconsistent decisions are displayed in
Figure 4 below.
Figure 4. Mean reaction times for recognition-consistent and recognition-inconsistent decisions
(in Experiment 1).
22
Likewise, in Experiment 2, reaction times were not significantly different for recognitionconsistent decisions (M = 4.31s, SD = 1.53) and recognition-inconsistent decisions (M = 4.66s,
SD = 1.43), (t(26) = -1.240, p = .226). Cohen's d shows a small effect size (d = 0.24). See Figure
5 for this comparison.
Figure 5. Mean reaction times for recognition-consistent and recognition-inconsistent decisions
(in Experiment 2).
An analysis of the reaction times for Experiments 1 and 2 combined still did not result in a
statistically significant difference between recognition-consistent (M = 4.45s, SD = 1.67) and
recognition-inconsistent decisions (M = 4.73s, SD = 1.90), t(52) = -1.393, p = .17. Cohen’s d
indicates a trivial effect size (d = 0.16).
23
Thus, these reaction time data do not provide convincing evidence that a heuristic decision
process was used. It should be noted that one explanation for the long response times and large
variance in response times is that choices were submitted by asking participants to click a radio
button using the computer’s mouse, rather than by pushing predetermined keys on the keyboard.
Furthermore, participants were allowed to take as much time as they needed before
answering. Rieskamp and Hoffrage (1999) showed that when people are put under time pressure,
they unsurprisingly tend to use faster and simpler strategies. But considering that Goldstein and
Gigerenzer are adamant that heuristics should be tested in a naturalistic environment, it makes
sense to allow people to take a few seconds to reflect on their decision, if they so desire, as they
would presumably do outside of the lab.
Another possibility is that participants may have guessed on the recognition-inconsistent
trials, which may not take any longer than picking the recognized option would.
There was no correlation between the number of words recalled on the distractor task and
the number of recognition-consistent decisions that each participant made in Experiment 1
(Pearson’s r(24) = -0.03, p = .89), even after removing the data from two participants who
accidentally submitted their responses after typing a single word (Pearson’s r(22) = .05, p = .82,
M = 11.875 out of 25 words).
The correlation was also insignificant for the participants in Experiment 2 (Pearson’s r(26) =
.28, p = .14), and this correlation greatly decreased after removing four participants who
accidentally submitted their responses after one word (Pearson’s r(22) = .13, p = .55, M = 10.583
words). Based on these results, a person’s degree of reliance on the recognition heuristic does not
appear to be based on that person’s working memory capacity.
A Pearson correlation also revealed no relationship between confidence ratings on the initial
24
task [“How confident are you that you recognize this city?” on a 0-100 scale] and the proportion
of recognition-consistent decisions for Experiment 1 (Pearson’s r(24) = -0.219, p = .282).
Likewise, there was no relationship for confidence ratings vs. proportion of recognitionconsistent decisions in Experiment 2 (Pearson’s r(26) = -0.266, p = .171).
25
CHAPTER IV. DISCUSSION
Experiments 1 and 2
Considering the highly significant results for Experiments 1 and 2, it is safe to say that
people reliably made more recognition-consistent decisions than would be expected if they were
just guessing. Cohen’s d indicates that this effect is a medium-to-large one. These results initially
appear to support the idea that recognition forms the basis of a decision mechanism.
Before we declare a resounding victory for Gigerenzer’s vision of fast-and-frugal heuristics,
it is important to look at the individual-level data, as Gigerenzer and Goldstein (2011) advocated.
This analysis warrants less optimism for the fast-and-frugal approach.
In Experiment 1, 22 of 26 participants (85%) made recognition-consistent decisions on at
least half of trials (please see Figure 6 below). Experiment 2 yielded a similar adherence rate: 23
out of 28 participants, or 82%, made recognition-consistent decisions on at least half of trials
(please see Figure 7 below).
However, the same data also show that only a small proportion of participants routinely
made recognition-consistent decisions. In Experiment 1, about a quarter of participants (7 of 26;
27%) made recognition-consistent decisions on at least 8 of the 10 trials. In Experiment 2, 4 of
28 (about 14%) of participants made recognition-consistent decisions on at least 8 trials.
Even worse for proponents of the recognition heuristic, only one out of the 54 total
participants made recognition-consistent decisions on every trial (please see Figure 8 below).
These results are quite different from Goldstein and Gigerenzer (2002), in which 19 of 22
participants (86%), made recognition-consistent decisions on at least 80% of trials! The
results reported here exhibit a much lower mean proportion of recognition-consistent
decisions than Goldstein and Gigerenzer's original studies, and a wider range as well.
26
Figure 6. Individual RH adherence rates in Experiment 1.
Proportion of recognition-consistent decisions for each participant, out of a possible 10 trials.
Height of the bar indicates the proportion of recognition-consistent decisions. Results are
displayed in descending order, based on each participant's proportion of recognition-consistent
decisions.
27
Figure 7. Individual RH adherence rates in Experiment 2.
Proportion of recognition-consistent decisions for each participant, out of a possible 10 trials.
Height of the bar indicates the proportion of recognition-consistent decisions. Results are
displayed in descending order, based on each participant's proportion of recognition-consistent
decisions.
28
Figure 8. Individual RH adherence rates in Experiments 1 and 2.
Proportion of recognition-consistent decisions for each participant, out of a possible 10 trials.
Results are displayed in descending order, based on each participant's proportion of
recognition-consistent decisions. Graph compiles data from participants in Experiments 1 and 2.
The present experiments were designed to artificially induce recognition. If people truly use
the recognition heuristic as originally formulated, then people would inevitably base their
decisions on which city they saw earlier in the experiment. Due to the absence of any further
cues (e.g. sports teams or international airports) that might contradict recognition, it would be
reasonable to expect near-100% accordance with the recognition heuristic, especially in light of
the roughly 90% mean accordance rate in the original experiments (Goldstein & Gigerenzer
1999; 2002).
This was not the case, though. People only made the decisions predicted by the recognition
heuristic about 60-65% of the time, even though recognition was presumably the only systematic
29
cue available to them. Therefore, the recognition heuristic evidently is not used in a single-cue
manner. The present data demonstrate that human decision-making relies on a procedure that is
more complex than a simple knee-jerk reaction to recognition.
In light of the present results, a reasonable explanation for Goldstein and Gigerenzer’s initial
findings in 1999 and 2002 is that people relied not only on recognition, but on a confluence of
cues when deciding which city is more populous. If there is no reason to believe that the
recognized city is smaller, people will probably assume that they have heard of the larger city.
But, as several studies have suggested, people will incorporate further information if they believe
that it applies to a given situation.
For an example of this, let’s return to our fictional participant, “Bob.” If Bob is asked
whether Berlin, Germany or Bremen, Germany is more populous, he may pick Berlin: not only
because he recognizes it, but because he also realizes that Berlin is the capital of Germany, is
home to a major soccer team and several major universities, is an economic and political hub of
Europe, and is a popular tourist destination. These cues all point to Berlin likely being larger
than another German city that he does not recognize.
Bob is not as likely to pick Chernobyl over an unrecognized Ukranian city, however,
because he also knows that Chernobyl was bathed in deadly radiation due to a nuclear
powerplant meltdown in the 1980s. That's not exactly conducive to a large population!
Though Bob recognizes Chernobyl, his further knowledge tells him that Chernobyl is
probably not more populous than another city that was not affected by a nuclear accident. Or, if
Bob is asked about a city that he recognizes and he knows that it’s small, then he is more likely
to pick the unrecognized city (Oppenheimer, 2003).
In 1996, Gigerenzer and Goldstein first proposed recognition as a principle underlying
30
decision-making. In light of their results from experiments published in 1999 and again in 2002,
recognition was promoted to a decision strategy that is not affected by further information.
Experiments such as those conducted by Oppenheimer (2003), Newell and Shanks (2004), and
Bröder and Eichler (2006) shed doubt on the robustness of the recognition heuristic. In
combination with such prior studies, the present study suggests that recognition is more properly
defined as a principle than a heuristic.
This is not to say that the recognition heuristic is useless as a description of behavior. As
Goldstein and Gigerenzer (1999; 2002) showed, recognition can account for a remarkably high
proportion of people's decisions in certain environments. The present results demonstrate that if
recognition is the only information available, people are likely to pick the recognized option
more often than not—even if that pattern is not always consistent! Together with prior studies,
however, Experiments 1 and 2 indicate that the recognition heuristic is a little too simplistic to
explain human decision-making.
However, Pachur and Hertwig (2006) suggest that recognition is the first, and one of the
most salient, cues underpinning a decision. Determining whether or not an object is recognized
requires remarkably little time or cognitive effort, so it is certainly capable of biasing a decision
in a certain direction. The present results further support the idea that decisions are biased—but
not determined—by recognition.
31
CHAPTER V. RESULTS
Experiment 3
So, how do people know whether or not to rely on recognition? After all, it is unlikely that
people have direct knowledge about such information as the population of an arbitrary city.
Experiment 3 tested learning-with-feedback as a possible mechanism to evaluate the
effectiveness of recognition as a decision strategy in a particular environment.
The participants in Experiment 3 were 77 undergraduate students enrolled in psychology
courses at Bowling Green State University. Six participants were excluded from the analysis.
Two reported that they did not recognize any cities—since RH couldn't apply to the data from
these participants, there was nothing to evaluate. For the other four excluded participants, there
was only a single trial on which RH was applicable. Since the binary nature of the possible
results (either 0% RH-consistent or 100% RH-consistent decisions) violates the assumptions of
the test, it is appropriate to exclude these participants as well. The final sample of 71 participants
included 50 women, 20 men, and 1 participant who identified as "other," ages 18 to 42 (M =
19.49 years, SD = 3.08).
On the experimental judgment task, each participant produced a proportion of recognitionconsistent decisions. The mean percentage (across participants in each training condition) was
analyzed with a one-way ANOVA, which revealed a significant main effect, F(2,68) = 4.264, p =
.018).
Fisher's LSD was run as a planned-comparison test of the differences between groups. As
Figure 9 illustrates, the initial hypothesis was supported: The mean RH-adherence of people in
the 0.2 training condition (M = 0.734, SD = 0.199) was significantly lower than the 0.5 training
condition (M = 0.871, SD = 0.135) and the 0.8 training condition (M = 0.864, SD = 0.199), p =
32
.012 and .016, respectively. The proportion of recognition-consistent decisions did not differ
significantly across the 0.5 and 0.8 training conditions (p = .901).
Figure 9. Mean proportion of recognition-consistent decisions by training condition (in
Experiment 3).
Tukey’s HSD, a post-hoc analysis that includes a correction for conducting multiple tests on
the data, found a similar pattern of results, yielding a significant difference in the mean
proportion of RH-consistent decisions between participants in the 0.2 and 0.5 training conditions
(p = .031) and between participants in the 0.2 and 0.8 training conditions (p = .041) but not
between the 0.5 and 0.8 conditions (p = .992).
Because the data appeared non-normally distributed, it is fair to question the use of an
33
ANOVA to evaluate these results. However, the Kruskal-Wallis H test, a non-parametric
analogue to the ANOVA, found a main effect similar to the ANOVA (χ2(2) = 8.501, p = .014),
which supports the traditional presumption that the ANOVA is a robust test, given a reasonably
large sample size.
Cohen’s d was used as a measure of effect size for the differences in the mean proportions
among the three training conditions. The effect size for the comparison between the 0.2 and 0.5
training conditions was large (d = .80), and the effect size for the comparison between the 0.2
and 0.8 training conditions was medium (d = .65). The effect size for the comparison between the
0.5 and 0.8 training conditions was trivial (d = .04).
Levene’s test for homogeneity of variance revealed no significant difference in variance
among the three training conditions (F(2, 68) = 1.180, p = .313).
Measures of reaction-time showed that on average, participants made RH-consistent
decisions significantly more quickly (M = 4.61s, SD = 1.78) than RH-inconsistent decisions (M =
6.16s, SD = 3.81), as evaluated by a paired-samples t-test, t(44) = 2.609, p = .012. Cohen's d
reveals a medium effect size (d = .524). Figure 10 (below) shows the mean reaction times for
RH-consistent vs. RH-inconsistent decisions.
34
Figure 10. Mean reaction times for recognition-consistent and recognition-inconsistent decisions
(in Experiment 3).
Note that the degrees of freedom were so low for this analysis because 26 participants
made only RH-consistent decisions, so those individuals had no mean RH-inconsistent reaction
time to evaluate.
35
CHAPTER VI. DISCUSSION
Experiment 3
On the experimental judgment task, people who had been assigned to the 0.2 training
condition picked the recognized city less often than people in the other training conditions.
The experimental hypothesis was therefore supported: Feedback on prior trials influenced
participants' decision strategy on subsequent judgments. The present study confirms the
common-sense insight that people adjust their use of a particular strategy, based on past
experience with similar tasks.
One important point to note is that the results from the 0.5 and 0.8 conditions replicated
Goldstein and Gigerenzer's original (1999; 2002) results almost exactly. People who were
assigned to those training conditions made recognition-consistent decisions on over 85% of
trials—a higher RH-adherence rate than the results of any of the training conditions would
warrant.
And even though the RH-adherence rate was lower for participants in the 0.2 condition than
in the other two conditions, it was still considerably higher than the RH-adherence rate in
Experiments 1 and 2. This provides evidence to support Gigerenzer and colleagues' objections to
tasks that aren't "naturalistic." That is, people behave differently with real-world stimuli than
they do with artificial stimuli created for use in a lab setting, just as Brunswick (1955) originally
argued. This is important to note for future decision-making research that makes use of fictitious
stimuli!
Furthermore, the similarity in the proportion of recognition-consistent decisions made by
participants in the 0.5 and 0.8 training conditions indicate that mixed feedback is evidently not
enough to make people override their default decision strategy (at least not on a task like
36
Experiment 3). These results suggest that people will often make RH-consistent decisions, unless
they have an overriding reason not to do so. The high rate of RH-consistent decisions suggests
that recognition provides the basis for a default decision strategy.
This supports Stanovich, West, and Toplak's (2011) default-interventionist model. As they
proposed, Type 1 (heuristic) processes are the default strategy, due to their automaticity and
low resource requirements. As Czerlinski, Gigerenzer, and Goldstein (1999) showed, heuristic
processes can be remarkably accurate as well. Judging by the data gathered for this
experiment, the Type 1 processes are fairly robust for naturalistic stimuli—evidence has to be
rather strong to persuade people to decrease their use of a heuristic.
Finally, the reaction time data confirms that RH-consistent decisions were made more
quickly than RH-inconsistent decisions. This result provides evidence to indicate that a heuristic
process truly was being used when participants were making their selections.
37
CHAPTER VII. GENERAL DISCUSSION AND IMPLICATIONS
Without sufficient incentive to scrutinize their decision criterion, it seems that people tend to
use a simple decision rule, unless they have clear evidence that the simple rule is usually wrong.
Such evidence may come from a remark from a friend: "Hey, did you know that Dhaka,
Bangladesh has a bigger population than Rome? Or that Kinshasa, Congo has more people than
Hong Kong?" Or perhaps a geography teacher surprises students by informing them that
lesser-known cities like Jakarta, Delhi, Manila, and Karachi are all more populous than New
York City.
Perhaps a better everyday example would be the decision to eat at a particular restaurant
based on the recommendation of a friend, or to buy a particular product because it's popular.
Advertising presumably works on such a principle: People are more likely to pick something that
they've heard of before, even if they don't know anything else about it—because in many cases,
it's probably not a bad choice!
However, as Experiments 1 and 2 show, this is only true to a certain extent. It is not
unreasonable to assume that people exhibit higher rates of adherence to RH on naturalistic tasks
(such as Experiment 3, or in the original experiments by Goldstein and Gigerenzer) because they
know that the familiar city is a fairly large one. Even if a person does not know for certain how
two cities compare, he or she can feel confident that the familiar city is the correct answer,
because well-known cities are typically large.
Without confirmatory knowledge that the familiar city is large, however, people choose the
recognized option far less often. People's use of RH, though higher than chance, is apparently
tempered by the lack of further, contextual knowledge.
Again, a real-life example might help to illustrate this point. Many people have heard of
38
Enron. As originally defined, RH predicts that people would pick Enron—a very well-known
company—as a better investment than a little-known company. But that approach strips
recognition of the very context that makes it useful for real-world decisions!
The context in this case is that Enron collapsed under the weight of a scandal in which they
overstated their profits and hid their debt. So, Enron isn't just recognized—it’s recognized for
causing a scandal that ruined the retirement funds of many Americans. Even though many people
would recognize Enron, virtually none of them would invest in Enron if given the chance
(though, since Enron went bankrupt in 2001, such an investment would be impossible).
Relying on context-free recognition could be disastrous in a situation like the one described
above. But the present study does not support the notion that people ignore context. From the
evolutionary perspective advocated by Gigerenzer and colleagues, it only makes sense that
memory usually involves some trace of its source.
After all, our fictional participant Bob doesn't just want to know that he recognizes a
location—he wants to remember whether it is a place he can go to find food, or a place he should
avoid because he was chased by a bear! He doesn't just want to know whether or not he
recognizes someone—he wants to know if it's someone who rescued him when he was in trouble,
or someone who tried to cheat him.
It is not sensible to treat recognition memory as if it were context-free. And according to the
data presented here, context does matter. These results show that recognition is an important cue
that helps to guide the decision process, but it is not the only cue people consider.
The present study shows that the recognition heuristic is too simplistic to explain human
behavior. However, taken together, the present experiments provide evidence that people do use
a weighted, recognition-based Type 1 strategy to make decisions.
39
CHAPTER VIII. LIMITATIONS AND FUTURE DIRECTIONS
As with any study, there are limitations to this research. A primary limitation was revealed
in a query at the end of Experiments 1 and 2. Many participants reported that they picked the city
that they saw at the beginning of the experiment, which indicates that they knew not just whether
or not they recognized a city, but also why they recognized it.
Knowing the source of the recognition, rather than just having a sense that one of the two
cities is familiar, may have led participants to discount their sense of recognition. This could
explain why participants in Experiments 1 and 2 made about 60-65% RH-consistent decisions,
but people who were assigned to the 0.5 and 0.8 training conditions in Experiment 3 chose the
recognized city on over 85% of trials. This could also explain the similar-sized discrepancy
between Experiments 1 and 2 vs. Goldstein and Gigerenzer's original findings.
If participants were unsure of the source of their recognition, perhaps they would make a
greater proportion of RH-consistent decisions. A study is planned to test this idea in the future,
by inducing recognition of fictitious stimuli in the form of an expository article. Such an
approach would provide a more naturalistic introduction to the stimuli than in Experiments 1 and
2 of the present study.
Another experiment will test the effect of having a longer and more cognitively intensive
distractor task as well. It is worth testing to see if this will impact the proportion of
RH-consistent decisions that participants make.
The query also revealed that some participants associated the names of some stimuli with the
name of cities that they knew (e.g. “Heingjing” is fairly similar to Beijing). This similarity to
other, known cities may have inadvertently provided another cue to participants. Since this
possibility was not controlled for, or even examined ex post facto, this factor represents a
40
possible confound to the present study. It would be difficult to manufacture believable city
names that are not similar to well-known city names from the purported country of origin.
Nonetheless, this confound should be considered in future studies.
Along the same lines, another potential limitation is that the length of a city’s name was not
controlled for. In 2000, Drösemeyer found that the length of a city’s name is negatively
correlated with the size of its population—so name brevity, rather than recognition, could
potentially be used as a cue to the size of a city’s population (unpublished manuscript;
summarized in Marewski, Pohl, & Vitouch, 2011).
Another interesting avenue for future research would be to test whether—and to what
degree—recognition forms the basis of people's choices in an environment with multiple options,
as in many consumer choice tasks. This could represent a more realistic approach to the
usefulness of recognition in everyday decisions.
41
CHAPTER IX. CONCLUSION
It is evident, based on Experiments 1 and 2, that people truly are more likely to pick a
recognized option over an unrecognized one. However, these results show that recognition alone
is not as powerful as Goldstein and Gigerenzer's original experiments (1999; 2002) suggested.
Contrasting the 62% mean combined recognition-consistent decision rate observed in
Experiments 1 and 2 with the much higher mean recognition-consistent decision rates (as high as
87%) in the various conditions of Experiment 3 serves to illustrate that recognition does not
govern the decision-making process, though it plays a significant role.
Rather than singlehandedly forming the basis of a decision strategy, recognition appears to
be one of multiple cues that people consider. The power of recognition lies not only in
recognition itself: it's in the confluence of recognition combined with further knowledge. If
further knowledge supports the idea that the recognized option is the better answer, then people
will almost always select the recognized option. But if further information clearly contradicts the
idea that the recognized option is the better one, then the rate of recognition-consistent decisions
drops precipitously.
Experiment 3 provides evidence that the past performance of a particular strategy impacts a
person's future reliance on that strategy. More broadly, it provides support for the
default-interventionist model of reasoning proposed by Stanovich, West, and Toplak (2011).
Experiment 3 provides more evidence that people tend to rely on heuristic strategies, unless they
have a clear and compelling reason not to do so. From an evolutionary psychological approach,
this makes sense—due to limited time, energy, and processing ability, people have to make
choices about how to allocate those resources. It makes sense to rely on a quick and relatively
effortless strategy, as long as that strategy leads to the right answer reasonably often. Heuristic
42
strategies are therefore overridden only when there is convincing evidence that the effectiveness
of such a strategy is not satisfactory.
_________
So, in light of this new experimental evidence, it is safe to say that recognition does bias
decisions—but it does not have the final say!
Rather than a heuristic—a decision strategy unto itself—recognition seems to be more of a
guiding principle. Taken in combination with past experiments on the recognition heuristic, it is
evident that people do incorporate further knowledge, including the past performance of a
particular strategy, before making the final decision.
And that approach is the most ecologically rational of all.
43
REFERENCES
Almor, A. & Sloman, S. A. (1996). Is deontic reasoning special? Psychological Review, 103,
374-380.
Anderson, J. R., Bothell, D., Byrne, M. D., Douglass, S., Lebiere, C., & Qin, Y. (2004). An
integrated theory of the mind. Psychological Review, 111, 1036-1060. doi:
10.1037/0033-295X.111.4.103
Bröder, A. & Eichler, A. (2006). The use of recognition information and additional cues in
inferences from memory. Acta Psychologica, 121, 275-284.
doi:10.1016/j.actpsy.2005.07.001
Brunswick, E. (1955). Representative design and probabilistic theory in a functional psychology.
Psychological Review, 62, 193-217.
Cox, J. R. & Griggs, R. A. (1982). The effects of experience on performance in Wason’s
selection task. Memory & Cognition, 10, 496-502.
Campitelli, G. & Gobet, F. (2010). Herbert Simon's decision-making approach: Investigation of
cognitive processes in experts. Review of General Psychology, 14, 354-364. doi:
10.1037/a0021256
Czerlinski, J., Gigerenzer, G. & Goldstein, D. G. (1999). How good are simple heuristics? In
Gigerenzer, Todd, and the ABC Research Group, Simple Heuristics That Make Us Smart (p.
97-118). New York, NY: Oxford.
Dreyfus, H. L. (1997). Intuitive, deliberative, and calculative models of expert performance. In
C.E. Zsambok and G. Klein (eds.), Naturalistic Decision Making (p. 17-28). Mahwah, New
Jersey: Lawrence Erlbaum Associates.
Gigerenzer, G. (1991). How to make cognitive illusions disappear: Beyond “heuristics and
44
biases.” In W. Stroebe & M. Hewstone (eds.), European Review of Social Psychology, Vol.
2 (p. 83-115). Chichester, UK: Wiley.
Gigerenzer, G. (2001). The adaptive toolbox. In G. Gigerenzer & R. Selten, eds. Bounded
Rationality: The Adaptive Toolbox (p. 37-50). Cambridge, MA: MIT Press.
Gigerenzer, G. & Brighton, H. (2009). Homo heuristicus: Why biased minds make better
inferences. Topics in Cognitive Science, 1, 107-143. doi: 10.1111/j.1756-8765.2008.01006.x
Gigerenzer, G. & Goldstein, D. G. (1996). Reasoning the fast and frugal way: Models of
bounded rationality. Psychological Review, 103, 650-669.
Gigerenzer, G. & Goldstein, D. G. (2011). The recognition heuristic: A decade of research.
Judgment and Decision Making, 6, 100-121.
Gigerenzer, G. & Todd, P. M. (1999). Fast and frugal heuristics: The adaptive toolbox. In
Gigerenzer, Todd, and the ABC Research Group, Simple Heuristics That Make Us Smart (p.
3-34). New York, NY: Oxford.
Gintis, H. (2007). A framework for the unification of the behavioral sciences. Behavioral and
Brain Sciences, 30, 1-61. doi: 10.1017/S0140525X07000581
Glöckner, A. & Bröder, A. (2014). Cognitive integration of recognition information and
additional cues in memory-based decisions. Judgment and Decision Making, 9, 35-50.
Goldstein, D. G. & Gigerenzer, G. (1999). The recognition heuristic: How ignorance makes us
smart. In Gigerenzer, Todd, and the ABC Research Group, Simple Heuristics That Make Us
Smart (p. 37-58). New York, NY: Oxford.
Goldstein, D. G. & Gigerenzer, G. (2002). Models of ecological rationality: The recognition
heuristic. Psychological Review, 109, 75-90. doi: 10.1037//0033-295X.109.1.75
Hertwig, R., Herzog, S. M., Schooler, L. J., & Reimer, T. (2008). Fluency heuristic: A model of
45
how the mind exploits a by-product of information retrieval. Journal of Experimental
Psychology: Learning, Memory, and Cognition, 34, 1191-1206. doi: 10.1037/a0013025
Hilbig, B. E. (2010). Reconsidering “evidence” for fast-and-frugal heuristics. Psychonomic
Bulletin & Review, 17, 923-930. doi:10.3758/PBR.17.6.923
Jacoby, L. L., & Dallas, M. (1981). On the relationship between autobiographical memory and
perceptual learning. Journal of Experimental Psychology: General, 110, 306-340.
Kahneman, D. (2003). A psychological perspective on economics. The American Economic
Review, 93(2), 162-168.
Klein, G. & Klinger, D. (1991). Naturalistic decision making. Human Systems IAC Gateway,
11(3), 16-19.
Marewski, J.N., & Mehlhorn, K. (2011). Using the ACT-R architecture to specify 39 quantitative
process models of decision making. Judgment and Decision Making, 6, 439-519.
Marewski, J. N., Gaissmaier, W., Schooler, L. J., Goldstein, D. G., & Gigerenzer, G. (2010).
From recognition to decisions: Extending and testing recognition-based models for
multialternative inference. Psychonomic Bulletin & Review, 17, 287-309.
doi:10.3758/PBR.17.3.287
Marewski, J. N., Pohl, R. F., & Vitouch, O. (2011). Recognition-based judgments and decisions:
What we have learned (so far). Judgment and Decision Making, 6, 359-380.
Marewski, J. N. & Schooler, L. J. (2011). Cognitive niches: An ecological model of strategy
selection. Psychological Review, 118, 393-437. doi: 10.1037/a0024143
Martignon, L. (2001). Comparing fast and frugal heuristics and optimal models. In G. Gigerenzer
& R. Selten (eds.), Bounded Rationality: The Adaptive Toolbox (p. 147-171). MIT Press:
Cambridge, MA.
46
Nellen, S. (2003). The use of the “Take the Best” heuristic under different conditions, modeled
with ACT-R. In Detje, Dörner, and Schaub (eds.), Proceedings of the fifth international
conference on cognitive modeling (p. 171-176). Bamberg, Germany: Universitätsverlag
Bamberg.
Newell, B. R. (2011). Recognising the recognition heuristic for what it is (and what it’s not).
Judgment and Decision Making, 6, 409-412.
Newell, B. R. & Shanks, D. R. (2004). On the role of recognition in decision making. Journal of
Experimental Psychology: Learning, Memory, and Cognition, 30, 923-935. doi:
10.1037/0278-7393.30.4.923
Oppenheimer, D. M. (2003). Not so fast! (and not so frugal!): Rethinking the recognition
heuristic. Cognition, 90, B1-B9. doi:10.1016/S0010-0277(03)00141-0
Pachur, T., Bröder, A., & Marewski, J. N. (2008). The recognition heuristic in memory-based
inference: Is recognition a non-compensatory cue? Journal of Behavioral Decision Making,
21, 183-210. doi: 10.1002/bdm.581
Pachur, T. & Hertwig, R. (2006). On the psychology of the recognition heuristic: Retrieval
primacy as a key determinant of its use. Journal of Experimental Psychology: Learning,
Memory, and Cognition, 32, 983-1002. doi: 10.1037/0278-7393.32.5.983
Richter, T. & Späth, P. (2006). Recognition is used as one cue among others in judgment and
decision making. Journal of Experimental Psychology: Learning, Memory, and Cognition,
32, 150-162. doi: 10.1037/0278-7393.32.1.150
Rieskamp, J. & Hoffrage, U. (1999). When do people use simple heuristics and how can we tell?
In Gigerenzer, Todd, and the ABC Research Group, Simple Heuristics That Make Us Smart
(p. 141-167). New York, NY: Oxford.
47
Robbins, L. (1945). An essay on the nature and significance of economic science (2nd ed., revised
and extended). London: Macmillan.
Seligman, M. E. P. & Csikszentmihalyi, M. (2000). Positive psychology: An introduction.
American Psychologist, 55, 5-14. doi: 10.1037//0003-066X.55.1.5
Shah, A. K., & Oppenheimer, D. M. (2008). Heuristics made easy: An effort-reduction
framework. Psychological Bulletin, 134, 207-222. doi: 10.1037/0033-2909.134.2.207
Sheldon, K. M. & King, L. (2001). Why positive psychology is necessary. American
Psychologist, 56, 216-217. doi: 10.1037//0003-066X.56.3.216
Simon, H. A. (1955). A behavioral model of rational choice. The Quarterly Journal of
Economics, 69, 99-118.
Stanovich, K. E., West, R. F., & Toplak, M. E. (2011). Individual differences as essential
components of heuristics and biases research. In K. Manktelow, D. Over, & S. Elqayam
(eds.), The science of reason: A festschrift for Jonathan St. B. T. Evans (p. 335-396). New
York: Psychology Press.
Trippas, D., Handley, S. J., & Verde, M. F. (2013). The SDT model of belief bias: Complexity,
time, and cognitive ability mediate the effects of believability. Journal of Experimental
Psychology: Learning, Memory, and Cognition, 39, 1393-1402. doi: 10.1037/a0032398
Tversky, A. & Kahneman, D. (1973). Availability: A heuristic for judging frequency and
probability. Cognitive Psychology, 5, 207-232.
Tversky, A. & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases.
Science, 185(4157), 1124-1131.
Wason, P. C. (1966). Reasoning. In B. M. Foss (ed). New Horizons in Psychology (Vol. 1) (p.
135-151). Harmondsworth, UK: Penguin Books.
48
Wason, P. C. (1968). Reasoning about a rule. Quarterly Journal of Experimental Psychology, 20,
273-281.
Wason, P. C. (1977). Self-contradictions. In P.N. Johnson-Laird and P.C. Wason (eds). Thinking:
Readings in Cognitive Science (p. 114-128). Cambridge, UK: Cambridge University Press.
Wason, P. C. & Shapiro, D. (1971). Natural and contrived experience in a reasoning problem.
Quarterly Journal of Experimental Psychology, 23, 63-71.
Zsambok, C. E. (1997). Naturalistic decision making: Where are we now? In C.E. Zsambok and
G. Klein (eds). Naturalistic Decision Making (p. 3-16). Mahwah, NJ: Lawrence Erlbaum
Associates.
49
APPENDIX A. LIST OF FICTITIOUS CITY NAMES
The cities marked with an asterisk (*) were used in the first part of Experiment 1, to induce
recognition. Those marked with a carat (^) were used in the first part of Experiment 2, to induce
recognition.
The cities with the same number were used as a pair for the final judgment task, in both
Experiments 1 and 2.
1. Heingjing, China*
1. Huanlizhen, China^
2. Nehaiva, Israel*
2. Gohaiza, Israel^
3. al-Ahbahib, United Arab Emirates*
3. al-Fashik, United Arab Emirates^
4. Papayito, Mexico*
4. Las Besas, Mexico^
5. Weingshe, China*
5. Meingzhao, China^
6. Rhavadran, India*
6. Vedharal, India^
7. Schretzburg, Austria*
7. Nyskbörg, Austria^
8. Ohigmoso, Slovakia*
8. Ramslinn, Slovakia^
9. Bórszki, Poland*
9. Waszlów, Poland^
10. Åventyrnísse, Iceland*
10. Thaskörvík, Iceland^
50
APPENDIX B. PILOT STIMULI
Stimuli used for pilot study, to determine which cities were most recognized. The pairs of cities
with the greatest differential recognition rates were selected for the various conditions in
Experiment 3.
List of urban areas
Jakarta, Indonesia
Bandung, Indonesia
Fairfield, California, USA
Sacramento, California, USA
Rome, Italy
Catania, Italy
Quito, Ecuador
Guayaquil, Ecuador
Bangalore, India
Chennai, India
Al Ain, United Arab Emirates
Dubai, United Arab Emirates
Tainan, Taiwan
Taipei, Taiwan
Maraba, Brazil
Recife, Brazil
Santiago, Chile
Valparaiso, Chile
Buenos Aires, Argentina
Salta, Argentina
Nairobi, Kenya
Mombasa, Kenya
Karachi, Pakistan
Multan, Pakistan
Istanbul, Turkey
Izmir, Turkey
Liege, Belgium
Ghent, Belgium
Port Said, Egypt
Suez, Egypt
Manila, Philippines
Davao City, Philippines
Boulder, Colorado, USA
Greeley, Colorado, USA
Aleppo, Syria
Damascus, Syria
Lagos, Nigeria
Abuja, Nigeria
Le Mans, France
Toulon, France
Marrakech, Morocco
Casablanca, Morocco
Population of urban area
29,959,000
5,764,000
134,000
1,837,000
3,798,000
720,000
1,714,000
2,416,000
9,330,000
9,435,000
375,000
3,395,000
1,274,000
7,317,000
150,000
3,327,000
6,243,000
887,000
13,913,000
525,000
4,652,000
1,096,000
21,585,000
1,924,000
13,187,000
3,008,000
550,000
350,000
550,000
500,000
22,710,000
1,526,000
115,000
118,000
3,333,000
2,807,000
12,549,000
2,471,000
208,000
559,000
951,000
3,154,000
51
Hong Kong, China
Shenzhen, China
Mecca, Saudi Arabia
Jiddah, Saudi Arabia
Queretero, Mexico
Cancun, Mexico
Edmonton, Canada
Quebec City, Canada
Durban, South Africa
Pretoria, South Africa
Melbourne, Australia
Sunshine Coast, Australia
Allahabad, India
Mumbai, India
Munich, Germany
Bremen, Germany
Le Havre, France
Paris, France
St. Petersburg, Russia
Ekaterinburg, Russia
Shanghai, China
Xi’an, China
Torreon, Mexico
Acapulco, Mexico
Nagoya, Japan
Fuji, Japan
Jerusalem, Israel
Haifa, Israel
Ipoh, Malaysia
Kuala Lumpur, Malaysia
Bristol, United Kingdom
Tyneside, United Kingdom
Rasht, Iran
Tehran, Iran
Hiroshima, Japan
Fukuoka, Japan
Milan, Italy
Palermo, Italy
Chongqing, China
Tianjin, China
Brasilia, Brazil
Rio de Janeiro, Brazil
Bogota, Colombia
Cucuta, Colombia
Hobart, Australia
Brisbane, Australia
Vancouver, Canada
Kelowna, Canada
Warsaw, Poland
Lodz, Poland
Basrah, Iraq
Kirkuk, Iraq
Athens, Greece
7,050,000
12,860,000
1,597,000
3,476,000
1,159,000
550,000
988,000
697,000
3,300,000
2,550,000
3,788,000
209,000
1,300,000
17,672,000
1,911,000
650,000
244,000
10,975,000
5,132,000
1,340,000
22,650,000
5,438,000
1,273,000
600,000
10,238,000
710,000
780,000
1,082,000
700,000
6,635,000
617,000
780,000
550,000
13,429,000
1,325,000
2,558,000
5,264,000
731,000
6,782,000
9,596,000
2,723,000
11,723,000
8,188,000
660,000
171,000
1,932,000
2,182,000
142,000
1,716,000
939,000
1,001,000
500,000
3,515,000
52
Thessaloniki, Greece
Turku, Finland
Lahti, Finland
Lumumbashi, Dem. Rep. of Congo
Goma, Dem. Rep. of Congo
840,000
253,000
117,000
1,771,000
630,000
Data courtesy http://demographia.com/db-worldua.pdf as of April 14, 2014. PDF.
53
APPENDIX C. LOW-VALIDITY (0.2) TRAINING CONDITION STIMULI
Stimuli used for the 0.2 training condition in Experiment 3. The cities marked with an asterisk
(*) had a larger were more frequently recognized in the pilot study. The cities listed in italics
were more populous, according to Demographia World Urban Areas, 10th Annual Edition, March
2014.
Paris, France*
Le Havre, France
Istanbul, Turkey*
Izmir, Turkey
Hong Kong, China*
Shenzhen, China
Cancun, Mexico*
Queretero, Mexico
Jerusalem, Israel*
Haifa, Israel
Bristol, United Kingdom*
Tyneside, United Kingdom
Fuji, Japan*
Nagoya, Japan
Acapulco, Mexico*
Torreon, Mexico
Damascus, Syria*
Aleppo, Syria
Quebec City, Canada*
Edmonton, Canada
54
APPENDIX D. MEDIUM-VALIDITY (0.5) TRAINING CONDITION STIMULI
Stimuli used for the 0.5 training condition in Experiment 3. The cities marked with an asterisk
(*) had a larger were more frequently recognized in the pilot study. The cities listed in italics
were more populous, according to Demographia World Urban Areas, 10th Annual Edition, March
2014.
Paris, France*
Le Havre, France
Istanbul, Turkey*
Izmir, Turkey
Dubai, United Arab Emirates*
al-Ain, United Arab Emirates
Rio de Janeiro, Brazil*
Maraba, Brazil
Sacramento, California, USA*
Fairfield, California, USA
Hong Kong, China*
Shenzhen, China
Cancun, Mexico*
Queretero, Mexico
Jerusalem, Israel*
Haifa, Israel
Bristol, United Kingdom*
Tyneside, United Kingdom
Fuji, Japan*
Nagoya, Japan
55
APPENDIX E. HIGH-VALIDITY (0.8) TRAINING CONDITION STIMULI
Stimuli used for the 0.8 training condition in Experiment 3. The cities marked with an asterisk
(*) had a larger were more frequently recognized in the pilot study. The cities listed in italics
were more populous, according to Demographia World Urban Areas, 10th Annual Edition, March
2014.
St. Petersburg, Russia*
Ekaterinburg, Russia
Rome, Italy*
Catania, Italy
Paris, France*
Le Havre, France
Dubai, United Arab Emirates*
al-Ain, United Arab Emirates
Rio de Janeiro, Brazil*
Maraba, Brazil
Sacramento, California, USA*
Fairfield, California, USA
Mumbai, India*
Allahabad, India
Melbourne, Australia*
Sunshine Coast, Australia
Hong Kong, China*
Shenzhen, China
Jerusalem, Israel*
Haifa, Israel
56
APPENDIX F. EXPERIMENTAL JUDGMENT TASK STIMULI
Stimuli used for the experimental judgment in Experiment 3. This was the final task for all
participants, regardless of training condition. The cities marked with an asterisk (*) were more
frequently recognized in the pilot study. The cities listed in italics were more populous,
according to Demographia World Urban Areas, 10th Annual Edition, March 2014.
Vancouver, Canada*
Kelowna, Canada
Buenos Aires, Argentina*
Salta, Argentina
Athens, Greece*
Thessaloniki, Greece
Munich, Germany*
Bremen, Germany
Taipei, Taiwan*
Tainan, Taiwan
Warsaw, Poland*
Lodz, Poland
Brisbane, Australia*
Hobart, Australia
Santiago, Chile*
Valparaiso, Chile
Milan, Italy*
Palermo, Italy
Tehran, Iran*
Rasht, Iran