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This work is scheduled to appear in
Acta Psychologica
2012 Elsevier
This is the final accepted version of the manuscript after peer-review. It will not exactlyreplicate the final version published in the journal. It is not the copy of record.
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Running Head: Antecedents of the Recognition Heuristic 1
A matter of time: Antecedents of one-reason decision making based on recognition.
Short title: Antecedents of the Recognition Heuristic
Benjamin E. Hilbig
University of Mannheim
and Max-Planck Institute for Research on Collective Goods
Edgar Erdfelder & Rdiger F. Pohl
University of Mannheim
Please address correspondence to:
Benjamin E. Hilbig, School of Social Sciences, University of Mannheim
Schloss Ehrenhof Ost, 68131 Mannheim, Germany
Phone: +49 621 181 3396, fax: +49 621 181 3997
Email: hilbig@psychologie.uni-mannheim.de
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Running Head: Antecedents of the Recognition Heuristic 2
ABSTRACT
The notion of adaptive decision making implies that strategy selection in both
inferences and preferences is driven by a trade-off between accuracy and effort. A strategy for
probabilistic inferences which is particularly attractive from this point of view is the
recognition heuristic (RH). It proposes that judgments rely on recognition in isolation
ignoring any further information that might be available and thereby allows for substantial
effort-reduction. Consequently, it is herein hypothesized that and tested whether increased
necessity of effort-reduction as implemented via time pressure fosters reliance on the RH.
Two experiments corroborated that this was the case, even with relatively mild time pressure.
In addition, this result held even when non-compliance with the response deadline did not
yield negative monetary consequences. The current investigations are among the first to tackle
the largely open question of whether effort-related factors influence the reliance on heuristics
in memory-based decisions.
Abstract word count: 146
Keywords: Adaptive decision making; effort-reduction; time pressure; fast and frugal
heuristics; recognition heuristic; multinomial processing tree models
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Running Head: Antecedents of the Recognition Heuristic 3
INTRODUCTION
One of the classic and least disputed assumptions about human cognition is that it is
adaptive with respect to the environment, task structures, and goals of the agent (Anderson,
1991; Brunswik, 1952; Chater & Oaksford, 2000). This view is widely mirrored in theory and
research on judgment and decision making (Weber & Johnson, 2009), in line with Simons
(1955, 1956) seminal notion that choice behavior depends both on the structure of the
environment and the limitations in terms of resources, motivation, and abilities of the
actor. A prominent theoretical framework that endorses and further specifies this view is the
adaptive decision maker approach introduced by Payne and colleagues roughly two decades
ago (e.g., Payne, Bettman, & Johnson, 1988, 1993). In a nutshell, it is assumed that decision
makers adaptively select from among a repertoire of strategies by means of an effort-accuracy
trade-off. Specifically, the more accurate a strategy, the more likely it should be selected
especially if the motivation to be accurate is high (thus depending on the importance of the
current choice situation). However, strategies also come with some degree of cognitive costs,
such that more accurate strategies are typically assumed to require more effort. Thus, factors
that reduce the effort one is willing or able to exert such as time pressure or high
information costs would result in selection of more simple, often non-compensatory
strategies, such as lexicographic rules (Payne, Bettman, & Luce, 1996; Rieskamp & Hoffrage,
2008).
One judgment strategy that has been proposed to substantially reduce effort while
actually often retaining high levels of accuracy is the recognition heuristic (RH; Gigerenzer
& Goldstein, 2011; Goldstein & Gigerenzer, 2002). The idea is to base probabilistic
inferences on whether or not options are recognized and on this cue alone. For example, a
decision maker may want to judge which of two infectious diseases is more prevalent (Pachur
& Hertwig, 2006), which of two mountains is higher (Pohl, 2006), which of two cities has
more inhabitants (e.g., Hilbig & Pohl, 2008), or which of two stocks will perform better in the
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Running Head: Antecedents of the Recognition Heuristic 4
near future (e.g., Andersson & Rakow, 2007; Erdfelder, Kpper-Tetzel, & Mattern, 2011).
The RH theory suggests that comparative judgments of this sort are based on which objects
(diseases, players, cities etc.) are recognized in the sense of subjectively classifying them as
previously encountered. Concerning accuracy, the RH is an ecologically rational strategy
whenever there is a substantial correlation between recognition and the judgment criterion.
Indeed, it has been shown that recognition is a valid cue in numerous judgment domains
including those mentioned above (Gigerenzer & Goldstein, 2011; Pohl, 2006).
Concerning the aspect of effort or simplicity, the RH comprises several essential
characteristics of an effort-reducing heuristic in Shah and Oppenheimers (2008) framework:
Firstly, it examines fewer cues as it is assumed that search terminates as soon as recognition
has been assessed for both objects. The decision is consequently based on only one piece of
information, recognition (Goldstein & Gigerenzer, 1999, p. 57). Secondly, the RH uses
easily accessible information (recognition) that is likely to be early on the mental stage
(Pachur & Hertwig, 2006, p. 986). Finally, this strategy also simplifies cue weighting: Since
the RH is a non-compensatory strategy, the recognition cue is assumed to outweigh all other
cues. In sum, the RH thus clearly qualifies as an effort-reducing strategy that will often
though clearly not always allow for substantial accuracy in judgments.
The RH has been widely studied and even kindled debate, at times (for recent
overviews see Gigerenzer & Goldstein, 2011; Hilbig, 2010b; Pohl, 2011). It is beyond the
scope of this manuscript to reiterate the many interesting contributions that have been made;
rather, we are concerned with what is probably the largest gap in research on the RH: The
question of whenit is actually used1. More specifically, despite some controversy on how
1Note that we employ the term RH-useto signify the degree to which the RH accounts for choice data. We
neither imply that (nor strictly test whether) this follows from a cognitive process which corresponds to the step-wise serial information processing assumed by the RH theory. More or less RH-use in the current sense may thus
just as well stem from more or less exclusive focus on the recognition cue in an automatic process of evidenceaccumulation (Hilbig & Pohl, 2009). Herein, we are not concerned with the exact nature of the underlyingprocess (see Glckner & Brder, 2011, for a recent study providing insights on such questions). Rather we
investigate the conditions that will foster or hamper the degree to which the RH-theorys assumption of single-
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Running Head: Antecedents of the Recognition Heuristic 5
oftenit is used in general and how this might be tested (e.g., Brighton & Gigerenzer, 2011;
Hilbig & Richter, 2011), the least common denominator is that sometimes the RH adequately
describes data. That is, most individuals seem to rely on the RH sometimes. However, the
exact determinants of RH-use are an understudied area even though [t]he proper question
is: Whendo people rely on the heuristic? (Gigerenzer & Goldstein, 2011, p. 108, emphasis
added). Given that the RH is proposed as an adaptive strategy (or one that is adaptively
selected), it is straightforward to predict two groups of determinants, namely those related to
(a) accuracy and (b) effort. Whereas the former have received some attention in prior research
as sketched below, relatively little is known about the latter. We aim to fill part of this gap.
When is the RH used?
Two potential determinants of RH-use have been studied repeatedly: (i) the validity of
the recognition cue and (ii) the availability of further knowledge. Concerning the recognition
validity (i), experimental manipulations clearly show that observable judgments are more in
line with RH-predictions whenever recognition is more strongly related to the criterion. This
holds both for artificially induced recognition (Brder & Eichler, 2006; Newell & Shanks,
2004) as well as for natural materials (Hilbig, Erdfelder, & Pohl, 2010; Pohl, 2006). Also,
correlations corroborate that choices are more in line with the RH with increasing recognition
validity which holds across datasets (Gigerenzer & Goldstein, 2011) and, at least in some
studies, on the individual level (Pohl, 2006, Experiments 2 and 3). In sum, the extant findings
are thus compatible with the notion of adaptive decision making: The more successful the RH
is in terms of predicting the criterion, the more strongly choices are aligned with its
predictions, especially across different judgment domains.
The availability of further knowledge (ii) is typically viewed as a bounding condition
of RH-use. Disconfirming the original RH-assumptions, studies have demonstrated that valid
cue reliance on recognition hold as most previous research on similar models has done (e.g., Brder &
Schiffer, 2003b, 2006a).
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Running Head: Antecedents of the Recognition Heuristic 6
and useful further cues will be integrated into judgments (Brder & Eichler, 2006; Glckner
& Brder, 2011; Newell & Fernandez, 2006; Newell & Shanks, 2004; Richter & Spth,
2006), in turn implying some degree of RH-non-use. However, practically all of these
investigations have attracted criticism in one way or another (Gigerenzer & Goldstein, 2011;
Pachur, Brder, & Marewski, 2008). For example, it has been argued that explicitly providing
participants with an additional cue might create some demand effects or that providing cue
values for unrecognized objects may not be a very natural situation. To remedy such
shortcomings, alternative approaches have relied on studying the RH in natural environments
without providing further cue information to participants. Nonetheless, it was consistently
found that single-cue reliance on recognition alone does not hold pervasively. Instead,
decision strategies that rely on information other than or in addition to recognition are applied,
at least by a majority of decision makers (Hilbig, Erdfelder, & Pohl, 2010; Hilbig & Pohl,
2008). However, as indicated above, little is known about the conditions under which this will
be more or less often the case (thus implying less or more RH-use).
Other potential determinants apart from the recognition validity and the availability
of further cues or information have received less attention. One important exception is the
observation that time pressure mayfoster RH-use (Pachur & Hertwig, 2006, Study 2). Indeed,
such an effect is directly predicted by the adaptive decision making framework (Payne et al.,
1996; Rieskamp & Hoffrage, 2008), thus attributing RH-use to its advantages in terms of
effort reduction. A considerable number of prior studies have corroborated the more general
assumption that constraining time will increase participants adherence to more simple, often
non-compensatory strategies (e.g., Bckenholt & Kroeger, 1993; Rothstein, 1986; Svenson &
Edland, 1987; Svenson, Edland, & Slovic, 1990). Specifically, time pressure leads to
considering fewer pieces of information and less attributes (Wallsten & Barton, 1982, Exp. 2;
Wright, 1974), terminating information search sooner (Janis, 1983), and relying more on
lexicographic rules (Payne et al., 1988; Payne et al., 1996).
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Running Head: Antecedents of the Recognition Heuristic 8
Hilbig & Pohl, 2009). Finally, decision makers in Pachur and Hertwig's study faced monetary
losses when taking too much time (more than 1200 ms). It is thus not clear whether time
pressure per se or rather the motivation to avoid losses drove the effects. In the remainder of
this manuscript, we will tackle these open questions, aiming to provide additional insight on
whether and how time pressure might foster RH-use. However, before doing so, it is vital to
consider some methodological issues concerning the question of how RH-use can be
measured.
Measuring RH-use
A central issue in research on the RH and indeed subject to some controversy has
been how to measure RH-use, that is, the degree to which judgments are based on the
recognition cue in isolation. In the most widely accepted paradigm for studying the RH,
participants are presented with pair-wise comparisons of objects (e.g., cities) and asked to
infer which scores higher on a criterion (e.g., population). In another task, they are
additionally asked to provide recognition judgments for each of the objects, that is, state
whether they have previously heard of an object. As materials, real-word objects known to
participants from outside the lab are used (for arguments favouring such procedures, see
Gigerenzer & Goldstein, 2011; Pachur et al., 2008). There is thus no experimental control
over participants knowledge beyond recognition.
Using this type of setup, earlier studies measured RH-use by means of the accordance
between RH-predictions and choices, also termed the adherence rate (Goldstein & Gigerenzer,
2002): This is simply the proportion of choices for recognized over unrecognized objects,
given that one is recognized and the other is not. However, as has been shown theoretically,
through simulations, and empirically, the adherence rate is not a useful metric to determine
RH-use (Hilbig, 2010b): Since other pieces of information or cues will typically correlate
with the recognition cue (the more populous a city, the more likely it will be recognized and
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Running Head: Antecedents of the Recognition Heuristic 9
the more additional knowledge cues will point to it being populous), choice of recognized
objects cannot be taken to imply the consideration of recognition in isolation. In essence,
further information that might have been considered would have led to largely the same
choice pattern. In fact, adherence rates can be substantial even if no participant ever used the
RH (Hilbig, 2010a). This clearly demonstrates the limited value of such a measure2.
To remedy this problem, measures differentiating between cases in which RH-
predictions imply a factually correct versus false prediction have been proposed (Hilbig &
Pohl, 2008; Pachur & Hertwig, 2006; Pohl, 2006). While these bear advantages over mere
adherence rates, they nonetheless continue to provide inadequate estimates of RH-use under
some conditions (Hilbig, 2010a). Fortunately, however, a recently developed formal
measurement model from the class of multinomial processing tree models (Batchelder &
Riefer, 1999; Erdfelder et al., 2009), can remedy the majority of problems and provide
robustly adequate measures of RH-use: The so-called r-model (Hilbig, Erdfelder, & Pohl,
2010). This model (described in more detail in the Appendix) comprises a model parameter r
which captures the probability of decision making based on the recognition cue in isolation,
that is, using the RH. The psychological meaning of this parameter has been validated
experimentally and the model has been shown to recover true underlying rates of RH-use well
in simulations (Hilbig, 2010a). In the experiments reported in what follows, we thus relied on
the r-model as the main statistical tool.
EXPERIMENT 1
The first experiment aimed to test whether time pressure increases RH-use. To this
end, we manipulated the time available for judgments between randomized groups of
participants. To render the results principally comparable to those of Pachur and Hertwig
(2006), we also used performance-contingent payment and induced opportunity costs of time
2Note that we do not dismiss the adherence rate per se. Rather, when combined with non-diagnostic tasks(Hilbig, 2008b; Jekel, Fiedler, & Glckner, 2011) in which multiple decision strategies mostly point to the samechoice options, this measure cannot provide adequate estimates of the degree to which one particular strategy
may have been used (Brder & Schiffer, 2003a; Moshagen & Hilbig, 2011).
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Running Head: Antecedents of the Recognition Heuristic 11
contingent on the accuracy of their judgments, but additionally faced opportunity costs of
time: For every correct judgment made within 2000 ms, they received 0.07 (otherwise
nothing), and for every false one (regardless of the time it took), 0.06 were lost. So, in this
condition, being accurate was only worthwhile if it required little time. Thus, a trade-off
between accuracy and effort was enforced which was hypothesized to foster RH-use.
A total of 69 participants (47 female), aged 18 to 30 years (M= 22, SD= 2.4 years),
were recruited at the University of Mannheim. They were randomly assigned to one of the
three conditions outlined above.
Results and Discussion
First, we checked whether the relevant descriptive statistics mirrored prior studies.
Across the three conditions, participants reported to recognizeM= 11.4 (SD= 2) out of the 18
cities, thus resulting in an average of 71 recognition cases, 60 knowledge cases, and 22
guessing cases per individual in the judgment task4. The mean recognition validity (M= .59,
SD= .09) was above chance level (t(67) = 8.9,p< .001, Cohen's d= 1.1). Participants chose
recognized over unrecognized cities often (M= .79, SD= .16) and their overall proportion of
correct judgments (M= .61, SD= .06) was above chance level (t(67) = 8.9,p< .001, Cohen's
d= 1.9). As all these results indicate, the current data were highly comparable to typical
investigations of the RH. In addition, the manipulation was successful as participants in the
time-pressure condition showed shorter median response latencies in the judgment task (M=
1289 ms, SD= 466 ms) than participants in the baseline (M= 1681 ms, SD= 476 ms) or no-
time-pressure condition (M= 1554 ms, SD= 323 ms), with t(46) = 2.9,p< .01, Cohen's d=
.80 and t(43) = 2.2,p< .05, Cohen's d= .67, respectively.
4One participant claimed to have recognized all 18 objects and therefore had no recognition or guessing cases.
This participant was excluded from all further analyses. Note, however, that this was inconsequential for the
main results.
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Running Head: Antecedents of the Recognition Heuristic 12
To test the main hypothesis that time pressure would increase RH-use we analysed
the data with the r-model. Specifically, we aggregated the observed category frequencies
across participants (per condition) and sought the parameter values of a (recognition validity),
b (knowledge validity), g(successful guessing) and r(probability of RH-use) that best
accounted for the data (see Appendix A for details), using multiTree (Moshagen, 2010). The
model fit the data, albeit almost reaching a conventional level of significance (G(3) = 7.6,p=
.054). All parameter estimates are reported in Table 1. As these reveal, there were substantial
differences in the probability of RH-use (r). Correspondingly, constraining r-estimates to
equality across all three conditions revealed a significant decrement in model fit (G(2) =
37.2,p< .001), corroborating the descriptive differences. More specifically, rwas
substantially higher in the time-pressure condition as compared to both the baseline and no-
time-pressure condition (G(1) = 31.7,p< .001 and G(1) = 23.0,p< .001, respectively).
Indeed, the observed differences in r(~.15) were comparable to those observed in a previous
experiment in which one group of participants was explicitly instructed to use the RH (Hilbig,
Erdfelder, & Pohl, 2010).
In addition, we also tested whether the effect of time pressure would hold specifically
for those individuals who, normatively speaking, should rely on their knowledge rather than
on recognition alone. Specifically, we identified all those participants whose individual
knowledge validity was greater than their recognition validity5. For these individuals, we re-
ran the aggregate analysis and obtained the same pattern of results as described above: The
probability of RH-use increased substantially once time-pressure was present. The estimates
of the r-parameter were .54 (SE= .02), .51 (SE= .03), and .67 (SE= .02) in the baseline, no-
time-pressure, and time-pressure conditions, respectively. Once more, constraining the r-
parameter to equality across conditions led to a substantial decrement in model fit, G(2) =
35.0,p< .001. As such, even for those individuals who would have maximized normative
5To do so, we estimated the parameters of the r-model separately for the data of for each individual participants,
thus obtaining individual estimates of aand b(the recognition and knowledge validity, respectively).
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Running Head: Antecedents of the Recognition Heuristic 13
accuracy through non-use of the RH there was a shift towards more RH-use under time
pressure. This finding more directly demonstrates the hypothesized trade-off between
accuracy and effort.
In line with the notion of adaptive decision making (Payne et al., 1993; Payne et al.,
1996) and the view that the RH is particularly attractive as a means of effort-reduction
(Hilbig, Scholl, & Pohl, 2010; Shah & Oppenheimer, 2008), we found that time pressure
indeed fosters RH-use. This is compatible with previous indications (Pachur & Hertwig,
2006). However, our methodological setup allows for more conclusive answers. Specifically,
we aimed for an experimental manipulation of time pressure (rather than a cross-experimental
comparison), measured RH-use with an appropriate statistical model, implemented a more
lenient response deadline in the time-pressure condition (thus designing a more conservative
test of the hypothesis), and added a baseline group without performance-contingent payment
which revealed that the possibility of gains and losses per se does not affect RH-use. In
summary, the effect of time pressure on RH-use stood this stricter test: Once faced with only
limited time in which to increase their gains through providing correct judgments, participants
were substantially more likely to rely on the RH.
However, despite the consistency in findings, it remains unclear what exactly drives
the effect of time pressure. That is, decision makers faced opportunity costs when taking too
long to perform judgments. Plausibly, it may thus not be the pressure exerted on decision
makers per se which leads to increased RH-use, but rather the motivation to avoid opportunity
costs (or both). Thus, it is an open question how RH-use will be affected if decision makers
are motivated to be accurate and pressurized to make fast judgments without actually losing
money if they take too much time. To answer this question, we designed a second experiment.
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Running Head: Antecedents of the Recognition Heuristic 14
EXPERIMENT 2
To trace more closely the mechanisms underlying the effects of time pressure on RH-
use, the second experiment varied both the presence of time pressure and its severity. Once
again assuming that the RH is a useful tool for effort-reduction, it was hypothesized that there
should be an increase in RH-use with increasingly severe pressure. Additionally, it was tested
whether time pressure fosters RH-use even if there are no negative monetary consequences of
taking one's time. Again, this might be considered an increasingly conservative test of the
hypothesis.
Design and procedures
As in Experiment 1, participants were asked to repeatedly infer which of two large
world cities was more populous in the judgment task. A total of 17 cities were randomly
selected from a list of the 61 largest cities in the world. These were paired exhaustively,
resulting in 136 paired-comparisons presented in random order in the judgments task. In the
recognition task, participants indicated which of the cities they had heard of previously.
Again, the order of the two tasks was counterbalanced across participants and all tasks were
displayed on a computer screen.
The experiment comprised three conditions, manipulated between participants: In the
no-time-pressure condition, decision makers performed the paired-comparisons as usual, that
is, without any limitations in time. In the two remaining conditions, a digital clock was
displayed along with the to-be-compared-cities. This clock counted up the time (in seconds
with one decimal place) that had elapsed since stimulus onset in bold red numerals.
Participants in the mild-time-pressure conditionwere simply asked to let as little time elapse
as possible. Finally, in the severe-time-pressure condition, decision makers were additionally
instructed to provide judgments within 2000 ms (cf., Experiment 1). Whenever they failed to
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Running Head: Antecedents of the Recognition Heuristic 15
do so, the trial was followed by a 2000 ms display spelling "Too slow; please respond within
2 seconds" in bold red letters.
A total of 67 participants (38 female; aged 19 to 47 years,M= 23.8, SD= 4.1) were
recruited from the MPI Decision Lab subject pool. Participants were randomly assigned to
one of the three conditions sketched above. All were paid conditional on the success of their
judgments. Specifically, they received 0.10 for every factually correct answer, whereas 0.10
were subtracted for every false one. Participants earned 5.36 on average.
Results and Discussion
Again, we first checked all relevant descriptive statistics. Across the three conditions,
participants recognizedM= 10.2 (SD= 1.4) of the 17 cities. Thus, the judgment task
comprised 68 recognition cases, 42 knowledge cases, and 26 guessing cases on average per
individual. The mean recognition validity (M= .78, SD= .07) was well above chance level
(t(66) = 33.6,p< .001, Cohen's d= 4.1), as was participants' overall proportion of correct
judgments (M= .68, SD= .06), with t(66) = 23.3,p< .001, Cohen's d= 2.9. Participants often
judged recognized cities to be more populous than unrecognized cities (M= .85, SD= .11).
Once again, the manipulation was successful: Median response latencies in the judgment task
were shortest in the severe-time-pressure condition (M= 1175 ms, SD= 296 ms), followed by
the mild-time-pressure condition (M= 1431 ms, SD= 232 ms), and the no-time-pressure
condition (M= 1742 ms, SD= 420 ms), respectively. Differences between all conditions were
significant (allp< .01, Cohen's d> .80).
The main prediction that increased time pressure would increasingly foster RH-use
was again tested using the r-model. Aggregate category frequencies across participants (per
condition) were used to determine the best-fitting parameter values of a, b, gand r(see Table
2). The model fit the data very well (G(3) = 2.3,p= .52). As the parameter estimates show,
each increase in time pressure was accompanied by an increased probability of RH-use ( r).
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Running Head: Antecedents of the Recognition Heuristic 16
Constraining all r-estimates to equality resulted in a significant decrement in model fit
(G(2) = 19.1,p< .001), confirming the impact of time pressure on RH-use. Interestingly,
the difference in rbetween no-time-pressure and severe-time-pressure was .15, thus mirroring
exactly the difference in RH-use observed in Experiment 1.
As in the previous experiment, we again tested for the effect of time pressure
specifically in those individuals who could have maximized their normative accuracy by non-
use of the RH (those participants with larger knowledge than recognition validities).
Repeating the aggregate analysis for this subset of our sample revealed substantially lower
estimates of the r-parameter overall, namely .37 (SE= .06), .59 (SE= .06), and .59 (SE= .05)
in the no-time-pressure, mild-time-pressure, and severe-time-pressure condition, respectively.
Nonetheless, time pressure again substantially increased RH-use: Constraining the r-
parameter to equality across the three conditions yielded a clear decrement in model fit
(G(2) = 10.3,p< .01). Note, though, that the severity of time pressure no longer appeared
to be influential, as the two time-pressure conditions yielded practically identical r-estimates.
In other words, the mere presence of time pressure (even in a mild form) sufficed to foster
RH-use. As proposed by the adaptive decision making framework, individuals who were
normatively well-advised to rely on their knowledge (and who thus used the RH quite rarely),
apparently traded off accuracy against effort.
In summary, this second experiment was designed to provide a more detailed test of
the prediction that time pressure would increase RH-use. Specifically, a step-wise increase in
the degree to which decision makers were pressurized was implemented. As hypothesized,
these increases were accompanied by increased probabilities of RH-use. Notably, these effects
were observed even though slower judgments did not bear negative monetary consequences.
Thus, unlike the findings from Experiment 1, the current results show that time pressure per
se without entailing opportunity costs is effective in fostering RH-use.
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Running Head: Antecedents of the Recognition Heuristic 17
GENERAL DISCUSSION
One of the most ubiquitous notions in research on human cognition is that it is
adaptive. This view is well-mirrored in theory and research on judgment and decision making,
especially in the adaptive decision making framework (Payne, 1982; Payne et al., 1988,
1993). In a nutshell, each strategy one could select to solve the current choice task entails a
certain degree of accuracy and, concurrently, necessitates a specific amount of effort. A trade-
off between these two dimensions is made and one applies the strategy maximizing accuracy
given the current constraints in terms of the effort one is maximally willing or able to exert.
A particularly attractive strategy in terms of this effort-accuracy trade-off is the
recognition heuristic (RH; Goldstein & Gigerenzer, 2002). It substantially simplifies
probabilistic inferences (e.g., which of two cities is more populous) by basing judgments on
the recognition cue in isolation, and thus reduces the effort required (Shah & Oppenheimer,
2008), while actually retaining a high level of accuracy. Viewed from the adaptive decision
making perspective, the RH should thus be particularly useful (i) the higher its accuracy and
(ii) the stronger the current constraints in terms of effort. While the former (i) has been
repeatedly confirmed (Gigerenzer & Goldstein, 2011; Hilbig, Erdfelder, & Pohl, 2010; Pohl,
2006), the latter (ii) has remained a largely open question. That is, is RH-use dependent on
factors related to cognitive effort?
One of the few findings speaking to this question was reported by Pachur and Hertwig
(2006). In one of their experiments, participants were placed under time pressure and it was
demonstrated that choices were somewhat more in line with the RH as compared to another
experiment without time pressure. This tendency is principally compatible with repeatedly
observed effects of time pressure on judgment and choice (Ben Zur & Breznitz, 1981;
Bckenholt & Kroeger, 1993; Edland & Svenson, 1993; Payne et al., 1996; Rieskamp &
Hoffrage, 2008). Using experimental manipulations of time pressure (rather than cross-study
comparisons) and based on a recently developed unbiased measure of RH-use (Hilbig, 2010a,
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2010b), we tested whether and how time pressure influences RH-use in two experiments. In
particular, we investigated whether the hypothesized effect would hold with different degrees
of time pressure as implemented in the response deadline, but not through brief stimulus
presentation or the like.
In Experiment 1, we compared RH-use in an experimental condition comprising time
pressure to two control conditions (which differed only in whether or not payment was
performance-contingent). As hypothesized, time pressure substantially fostered RH-use which
held in comparison to both control conditions. Thus, the pattern previously indicated by
Pachur and Hertwig (2006) could be corroborated based on an experimental manipulation and
adequate measurement of RH-use. Also, note that our response deadline in the time pressure
condition (2000 ms) was more than twice that of Pachur and Hertwigs study. So, despite a
more conservative test of the time pressure hypothesis, corresponding effects were observed.
Finally, it can additionally be ruled out that short stimulus presentation (rather than time
pressure per se) was responsible for the findings previously observed, since there was no
limitation of presentation time in our experiment.
In Experiment 2, we then intended to (i) trace the impact of time pressure more closely
and (ii) to provide an even more conservative test of the hypothesis. To this end, we (i) varied
the degree to which decision makers were pressurized and (ii) omitted the opportunity costs
inherent in our first experiment. In other words, participants now no longer faced monetary
losses when taking longer to perform judgments. Summarized briefly, we found that despite
this monetary inconsequentiality of time pressure RH-use increased with every increase in
the degree to which participants were pressurized. Specifically, both (a) merely displaying the
time elapsed while asking for quick judgments and (b) an explicit response deadline of 2000
ms increased RH-use. For the latter condition (b) the increase in RH-use observed was twice
that of the former (a). At the same time, the maximally observed increase in RH-use was
almost exactly the same in the 2000 ms response-deadline conditions of both experiments
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Running Head: Antecedents of the Recognition Heuristic 19
(despite the differences in the consequentiality of slow responses). Thus, the results of the two
experiments showed high convergence.
In both experiments, it was further shown that time pressure clearly influenced the
choice behaviour particularly of those individuals who should not have relied on the RH in
normative terms. Whereas these decision makers apparently relied on the RH less pervasively
in general (at least in Experiment 2), they consistently shifted towards RH-use once time-
pressure was introduced. Importantly, Experiment 2 revealed that mild time-pressure (without
any specific response deadline and without opportunity costs for slow decisions) sufficed to
produce an increase in RH-use by .22 which must be considered a very large effect in
comparison to previous experimental manipulations (e.g., Hilbig, Erdfelder, & Pohl, 2010;
Hilbig, Scholl, & Pohl, 2010).
In summary, our results support the hypothesis that time pressure fosters use of the RH
in a trade-off between accuracy and effort. This conclusion, in turn, is well-aligned with
previous research indicating that limited resources aid selection of simpler decision strategies
(e.g., Payne et al., 1996; Rieskamp & Hoffrage, 2008). In addition, the findings are also
compatible with the observation that the RH is more likely to be used when effort-reduction is
a relevant goal (Hilbig, Scholl, & Pohl, 2010), that is, whenever judgments must be made
deliberatively and thus most likely in a serial and effortful fashion (Betsch & Glckner, 2010;
Evans, 2008; Horstmann, Ahlgrimm, & Glckner, 2009). Overall, the current work thus
answers the recent call for investigations of whenthe RH is used rather than whether or how
often this is generally the case (Gigerenzer & Goldstein, 2011). However, the quest does not
end here. Relatively little is known about the effort-related antecedents of the RH so far. For
example, one might expect that resource-demanding secondary tasks increase the need for
effort reduction and thus foster RH-use (cf. Brder & Schiffer, 2006b). Also, the degree to
which further knowledge beyond recognition can be automatically activated (Anderson, 1983)
and effortlessly integrated (Glckner & Brder, 2011) could play an important role. As such,
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Running Head: Antecedents of the Recognition Heuristic 20
it will be an important challenge for future work to investigate in more detail when and how
the need for effort reduction fosters reliance on simple heuristics (Shah & Oppenheimer,
2008).
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Running Head: Antecedents of the Recognition Heuristic 21
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APPENDIX
Description of the r-model
As is generally the case with multinomial processing tree models, categorical
observable data are explained through a set of latent parameters. In a typical study of the RH,
participants provide pair-wise comparisons of objects (e.g., cities; inferring which is more
populous) and recognition judgments for each of these. Thus, each comparison can fall into
one of three cases, depicted as three trees in Figure A1: It can occur that (1) both objects are
recognized, (2) exactly one object is recognized, or (3) neither object is recognized. These
cases are denoted knowledge cases, recognition cases, and guessing cases, respectively
(Goldstein & Gigerenzer, 2002; Pohl, 2006). In reach of these three cases, a decision maker
can make a correct judgment with respect to the criterion (denoted c) or a false judgment
(denoted f). Finally, in recognition cases, a decision maker may have chosen the recognized
option (denoted *) or the unrecognized option (denoted ~). Thus, there are a total of eight
observable outcome categories the model seeks to explain.
All outcome categories and latent parameters accounting for these are depicted in
Figure A1: If both objects are recognized, a correct judgment (1c) will be made with
probability b(denoting the knowledge validity, cf. Goldstein & Gigerenzer, 2002). Thus, with
probability 1 b, a false judgment will follow (1f). If neither objects is recognized, either a
correct choice (3c) will occur with probability gor a false choice (3f) with probability 1 g.
Most importantly, whenever exactly one object is recognized, a decision maker can either use
the RH (probability r) or consider additional knowledge or information (probability 1 r). If
the RH is used, the recognized object will be chosen and this judgment will be correct (2c*)
with probability aor false (2f*) with probability 1 a. Thus, adenotes the recognition
validity (cf., Goldstein & Gigerenzer, 2002). Finally, if the RH is not used (1 r), the success
of the judgment will depend on the validity of the additional information or knowledge
considered (as above, represented by b). If it is valid, one will adhere to the RH whenever the
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latter implies a correct judgment (2c*) but refrain from doing so otherwise (2c~). Vice versa,
given invalid knowledge (1 b), one will erroneously fail to adhere to the RH even though it
implies a correct judgment (2f~) or adhere to it even though this choice is, in effect, false
(2f*). Further details about the model and instructions on model application can be found in
Hilbig, Erdfelder, and Pohl (2010); for a recent model extension see Hilbig, Erdfelder, and
Pohl (2011).
To estimate model parameters, observed frequencies of the eight outcome categories
are computed. Typically, this is done across all trials and individuals (for arguments
supporting aggregation across individuals prior to parameter estimation, see Chechile, 2009).
Then, the model is fit to the data, that is, the set of parameter values is sought which
minimizes the distance between predicted and observed category frequencies. Thus, using
straightforward statistical techniques such as maximum likelihood, model fit can be
determined and parameters (including confidence intervals) estimated. Herein, this was
achieved through minimizing the asymptotically chi-square distributed log-likelihood ratio
statistic G2by means of the EM algorithm (Hu & Batchelder, 1994), as implemented in the
multiTree software tool (Moshagen, 2010). Differences between parameters across
experimental conditions were tested by means of the G2difference statistic, G
2. In essence,
one computes the difference in G between the superordinate model with the to-be-tested
parameters varying freely to a nested submodel with the parameters of interest constrained to
equality (Batchelder & Riefer, 1999; Erdfelder et al., 2009).
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AUTHOR NOTE
Benjamin E. Hilbig, School of Social Sciences, University of Mannheim, Germany
and Max Planck Institute for Research on Collective Goods, Bonn, Germany. Edgar
Erdfelder, Psychology III, University of Mannheim, Germany. Rdiger F. Pohl, Psychology
III, University of Mannheim, Germany.
Manuscript preparation was supported by grant ER 224/2-1 from the Deutsche
Forschungsgemeinschaft. We thank Tina Tanz, Felix Henninger, and Pascal Kieslich for
invaluable assistance in setting up and running the experiments.
Correspondence should be addressed to Benjamin E. Hilbig, School of Social
Sciences, University of Mannheim, D-68131 Mannheim, Germany. Email:
hilbig@psychologie.uni-mannheim.de.
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TABLES
Table 1. Estimated model parameters (standard error of each point estimate in parentheses) of
the r-model in Experiment 1, separately for the three conditions.
Experimental Condition
Parameter Meaning baseline no-time-pressure time-pressure
aRecognition
validity0.59 (0.01) 0.62 (0.01) 0.57 (0.01)
bKnowledge
validity0.67 (0.01) 0.67 (0.01) 0.63 (0.01)
gProbability of
correct guessing 0.45 (0.02) 0.50 (0.02) 0.46 (0.02)
rProbability of
RH-use0.51 (0.02) 0.53 (0.02) 0.67 (0.02)
Table 2. Estimated model parameters (standard error of each point estimate in parentheses) of
the r-model in Experiment 2, separately for the three conditions.
Experimental Condition
Parameter Meaning no-time-pressuremild-time-
pressure
severe-time-
pressure
aRecognition
validity0.76 (0.01) 0.78 (0.01) 0.79 (0.01)
bKnowledge
validity0.74 (0.01) 0.65 (0.01) 0.65 (0.01)
gProbability of
correct guessing0.59 (0.02) 0.50 (0.02) 0.52 (0.02)
rProbability of
RH-use0.56 (0.03) 0.63 (0.02) 0.71 (0.02)
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