2012 - a matter of time. antecedents of one-reason decision making based on recognition - hilbig y...

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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: [email protected]


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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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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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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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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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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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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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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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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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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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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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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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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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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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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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(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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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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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:

[email protected].


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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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