Consciousness and Cognition 19 (2010) 829–837

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Consciousness and Cognition

journal homepage: www.elsevier .com/locate /concog

It just felt right: The neural correlates of the fluency heuristic q

Kirsten G. Volz a,b,*, Lael J. Schooler a, D. Yves von Cramon b,c

a Max Planck Institute for Human Development, Berlin, Germanyb Max Planck Institute for Neurological Research, Cologne, Germanyc Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany

a r t i c l e i n f o

Article history:Received 30 November 2009Available online 16 June 2010

Keywords:Fluency heuristicDecision makingfMRIClaustrum

1053-8100/$ - see front matter � 2010 Elsevier Incdoi:10.1016/j.concog.2010.05.014

q This article is part of a special issue of this jour* Corresponding author. Address: Max Planck Inst

394.E-mail address: kirsten.volz@cin.uni-tuebingen.dURL: http://www.cin.uni-tuebingen.de (K.G. Vol

a b s t r a c t

Simple heuristics exploit basic human abilities, such as recognition memory, to make deci-sions based on sparse information. Based on the relative speed of recognizing two objects,the fluency heuristic infers that the one recognized more quickly has the higher value withrespect to the criterion of interest. Behavioral data show that reliance on retrieval fluencyenables quick inferences. Our goal with the present functional magnetic resonance imagingstudy was to isolate fluency-heuristic-based judgments to map the use of fluency onto spe-cific brain areas that might give a better understanding of the heuristic’s underlying pro-cesses. Activation within the claustrum for fluency heuristic decisions was found. Giventhat claustrum activation is thought to reflect the integration of perceptual and memoryelements into a conscious gestalt, we suggest this activation correlates with the experienceof fluency.

� 2010 Elsevier Inc. All rights reserved.

1. Introduction

Simple heuristics can be used to exploit basic human abilities, such as recognition memory, to make decisions based onsparse information. One such heuristic is the fluency heuristic. Building on a long tradition of research on fluency (Jacoby &Dallas, 1981; Kelley & Jacoby, 1998), Schooler and Hertwig (2005) defined their fluency heuristic this way: If two objects arerecognized, and one of the objects is more fluently retrieved, then infer that this object has the higher value with respect to thecriterion, where retrieval fluency is defined as how long it takes to retrieve a trace from long-term memory. The fluency heu-ristic can help us make good inferences when all relevant objects are recognized and when there is a substantial correlation—in either direction—between the criterion and the retrieval fluency. For convenience, we assume a positive correlation for theremainder of this paper. For example, when choosing which of two recognized cities, say, the Japanese cities of Yokohamaand Kyoto, has more inhabitants, one could achieve a reasonable level of accuracy if there is a substantial correlation be-tween the ease of retrieving a city’s name and its population. The fluency heuristic works well to the extent that it can exploitrelevant characteristics of the environment that are encoded in the relative accessibility of memory traces.

The rationale behind the fluency heuristic is that memory performance reflects the patterns with which stimuli appearand reappear in the environment (Anderson & Schooler, 1991; Schooler & Anderson, 1997). Accordingly, the retrieval fluencyassociated with the recognition of stimuli correlates to a large extent with how frequently and recently relevant stimuli havebeen experienced (Hertwig, Herzog, Schooler, & Reimer, 2008). Since it is not currently possible to measure retrieval fluencydirectly, it has been operationalized by recognition latency, that is, how long it takes people to judge whether they recognize

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830 K.G. Volz et al. / Consciousness and Cognition 19 (2010) 829–837

an object or not (e.g., Hertwig et al., 2008). In short, differences in recognition latencies are taken as differences in retrievalfluencies.

In a series of experiments, Hertwig et al. (2008) pursued several basic questions about how the fluency heuristic operates.More precisely, the authors showed that (a) recognition latencies are indeed indicative of criteria, such as city population orthe wealth of athletes, (b) people can discriminate the recognition latencies of two objects that exceed a difference of 100 ms,(c) people’s judgments adhere more to the predictions of the fluency heuristic when differences between recognition laten-cies are large, which tends to happen when fluency is most predictive, (d) inferences in line with the fluency heuristic takeless time than those that conflict with the heuristic, and (e) people’s inferences frequently accord with those predicted by thefluency heuristic, given that it is applicable. It can be argued, however, that accordance to the fluency heuristic is spurious,perhaps depending on a correlation between fast retrieval and knowledge about an object. So while people’s behavior maycorrespond to the fluency heuristic, the underlying process may be an entirely different knowledge-based strategy(Marewski & Schooler, 2010). Through a priming study, Hertwig et al. provided evidence that fluency does indeed guideinferences irrespective of other factors associated with fluency, such as the amount of knowledge known about the objects.Hertwig et al.’s main goal was to establish that the fluency heuristic does, in fact, guide decisions. Our aim was to uncovermore about the processes underlying fluency-based decisions. One hypothesis draws on previous neuroscientific results onphenomenologically similar kinds of decisions. A second hypothesis focuses on an account of retrieval fluency that is basedon successfully binding memory traces.

The phenomenological experience of decisions based on the fluency heuristic has not been studied. The experience maybe one of familiarity, the ‘‘felt-rightness” of a specific response, or an intuitive feeling about which option to choose. We startwith Volz et al.’s (2006) investigation of the neural correlates of the recognition heuristic, which can be stated as follows: ‘‘Ifone of two objects is recognized and the other is not, then infer that the recognized object has the higher value with respect to acriterion” (Goldstein & Gigerenzer, 2002, p. 76). For this functional magnetic resonance imaging (fMRI) study, Volz et al.adapted Goldstein and Gigerenzer’s city judgment task in which participants had to infer which of two cities was larger.There were three critical types of trials, depending on whether both cities were recognized (RR trials), one of the citieswas recognized (RU), or both cities were unrecognized (UU). After the fMRI experiment, participants were asked about whatstrategies they used in the three types of critical trials. For RR trials participants said they relied on knowledge if available,yet they said that such knowledge-based inferences were possible in only a fraction of these trials. When participants saidthey could not reach a decision based on knowledge, 17 of the 18 participants reported choosing the city that felt larger ormore familiar or that they had made an informed guess. Together, these anecdotal data suggest that if knowledge wasunavailable, participants relied on some other information. Their descriptions suggest that their judgments resemble the‘‘intuitive assessment of the felt-rightness of a memory” (Schnyer, Nicholls, & Verfaellie, 2005, p. 837).

The idea of an intuitive assessment of the felt-rightness of a memory is found in the literature on the ventromedial pre-frontal cortex (VMPFC). For example, Schnyer et al. (2005) investigated feeling-of-knowing (FOK) judgments in an episodicmemory task. Participants had to indicate the probability that they would recognize the final word of a sentence from a list ofpreviously studied sentences. Prior to being shown the final word, the participants made an FOK judgment about whetherthey could retrieve the word on a 5-point scale. The instructions for using the rating scale were as follows: ‘‘Only press 5 ifthe answer pops effortlessly to mind. If you feel the answer is just there under the surface, press 4. . . . If you have no rec-ollection of ever having seen the sentence, then press 1.” The authors found the VMPFC to be specifically engaged duringaccurate FOK judgments, defined as the four ratings on the scale that did not indicate certain knowledge of successful retrie-val (i.e., 1–4 on the scale). In addition, the authors found a positive correlation between VMPFC activation and FOK judg-ments. When ‘‘know” ratings (i.e., 5 on the scale) were added to this analysis of retrievability, the amplitude levels forthese ‘‘know” ratings did not continue the linear trend in the VMPFC but rather dropped off. Accordingly, the authors con-cluded that the VMPFC monitors the output of retrieval processes. Supporting this hypothesis, patients with lesions to theprefrontal cortex encompassing the VMPFC showed a clear impairment when making predictions about their subsequentrecognition performance (Schnyer et al., 2004). Since the patients’ familiarity-based assessment was demonstrated to be in-tact, the results were taken to suggest that the VMPFC crucially subserves the assessment of the accessibility of memory con-tents. In addition, clinical and experimental findings showed the VMPFC to be crucially involved in ‘‘emotional decisionmaking,” for example, when responses were based on feelings of rightness when deciding which option to pursue to gainor lose money or which action to judge as moral or immoral (Bechara, Tranel, & Damasio, 2002; Damasio, 2004; Greene& Haidt, 2002). Accordingly, previous findings may point to a crucial role of the VMPFC when decisions follow fluency.

Schooler and Hertwig (2005) defined retrieval fluency in terms of how long it takes to retrieve a trace form long-termmemory. Besides retrieval latency, other cognitive processes could contribute to a sense of retrieval fluency (Oppenheimer,2008). One such process could be the ease with which associated memories are bound together. There is evidence that such abinding process would be associated with activation in the claustrum. Although activation within this area has rarely beenreported in fMRI studies, Volz and von Cramon (2006), in a study on intuitive perceptual decisions, observed activation with-in the claustrum. They presented participants with fragmented line drawings of common objects. Within 400 ms, the par-ticipants had to indicate whether they perceived a coherent gestalt. A functional connectivity analysis revealed that theperceived gestalt was correlated with activation in the claustrum and medial orbitofrontal cortex (OFC). Medial OFC activa-tion has been associated with a positive affective valence known to bias decisions (Bar et al., 2006; Kringelbach & Rolls, 2004;Volz, Rübsamen, & von Cramon, 2008). Further support for the critical role of the claustrum in binding can be found in ana-tomical and connectivity data (e.g., Crick & Koch, 2005; Fernández-Miranda, Rhoton, Kakizawa, Choi, & Alvarez-Linera, 2008;

K.G. Volz et al. / Consciousness and Cognition 19 (2010) 829–837 831

Kowianski, Dziewiatkowski, Kowianska, & Morys, 1999). Because the claustrum integrates information within and acrossvarious modalities, Crick and Koch (2005) argued that the claustrum plays a key role in conscious experience. Additionally,the two-way connections between the claustrum and most, if not all, parts of the cortex as well as subcortical structures,which in turn have been suggested to subserve emotional processes, suggest that the claustrum binds disparate events intoa single percept.

Thus, the goal of the present fMRI study was to isolate judgments based on the fluency heuristic, so that we could map theuse of fluency onto specific brain areas, which might give us a better understanding of the processes underlying adherence tothis heuristic. As outlined above, it is difficult to tell whether a particular answer is based on knowledge or on fluency. Todistill fluency-based judgments, we calculated two interaction contrasts specifically designed to tap fluency-based judg-ments (as described below) and the conjunction of these two interaction contrasts (test for a positive AND; Nichols, Brett,Andersson, Wager, & Poline, 2005). By drawing on previous results found by Hertwig et al. (2008), that people rely on thefluency heuristic when differences in recognition latencies are large and that inferences in line with the fluency heuristictake less time than inferences conflicting with the fluency heuristic, the first interaction is between differences in recognitionlatencies (large vs. small differences) and response time (individually determined fast vs. slow responses). The second inter-action focuses on the factors that lead to participants’ judgments adhering to the predictions of the fluency heuristic (deci-sions agreeing vs. conflicting with the fluency heuristic) and incorrectness (incorrect vs. correct responses). Assumingknowledge-based strategies are more often right than wrong, looking at trials in which participants’ judgments adheredto the fluency heuristic but were wrong lowers the chances that their judgments were based on knowledge-based strategies.A conjunction of the two interaction contrasts should pull out trials on which judgments are more likely to be based on flu-ency than on knowledge-based strategies. As we are reporting new analyses of largely unanalyzed data from Volz and vonCramon (2006), we provide details of their methods.

2. Method

2.1. Participants

Healthy, right-handed volunteers participated in the fMRI experiment (10 women, 8 men, mean age 25.6 years, SD 3.4,range 20–32 years). Informed consent was obtained prior to the experiment from each participant according to the Decla-ration of Helsinki. The local ethics committee of the University of Leipzig approved the experimental standards. Data werehandled anonymously.

2.2. Stimuli, task, and experimental session

On the left and right side of a screen two city names were presented simultaneously (horizontal visual angle 11�; verticalvisual angle 1.7�). Participants had their left and right index fingers on left and right response buttons spatially correspond-ing to the stimulus locations on the screen. Within each trial a cue was presented for 500 ms, indicating the beginning of thenext trial, followed by the presentation of a fixation cross for 500 ms; thereafter the two city names were presented for amaximum of 4 s during which participants’ response and reaction time were recorded. As soon as participants indicated theirchoice with a button press, the city names disappeared and a fixation cross was presented until the next trial started. Noperformance feedback was delivered whatsoever. The participants’ task (i.e., the inference task) was to indicate which cityin each pair had the larger population. Each session contained 218 trials, consisting of 140 critical trials plus 48 filler trialsand 30 null events, in which no stimulus was presented and the Blood Oxygen Level-Dependent BOLD response was allowedto return to a baseline state. In the filler trials participants had to indicate which of two presented words contained morevowels. All trials lasted for 8 s (i.e., four scans at a repetition time (TR) of 2 s). The onset of each stimulus presentation rel-ative to the beginning of the first of the four scans was randomly varied (0, 500, 1500 ms) to enhance the temporal resolutionof the signal captured (Birn, Cox, & Bandettini, 2002; Miezin, Maccotta, Ollinger, Petersen, & Buckner, 2000). Participantswere unaware of this modulation.

Following the fMRI session (i.e., outside the scanner), participants completed a recognition test in which they were pre-sented with each particular city name and had to indicate whether they knew each city already before the experimental ses-sion. It was emphasized that participants should declare as recognized only those cities that they had heard of before thefunctional session. The data of the recognition test were used first to individually determine trial types in the inference task,for example, whether both cities were recognized (recognize–recognize, or RR trials), neither city was recognized (unrecog-nized–unrecognized, or UU trials), or one of the two cities was recognized and the other not (recognized–unrecognized, orRU trials), and second to determine recognition latency for each stimulus in the inference task. On the basis of these data, wedetermined which of the RR trials in the inference task were solved as predicted by the fluency heuristic, that is, when thecity that was recognized faster (as determined from the recognition test) was chosen as the larger city. Following the rec-ognition test, participants were requested to fill out a questionnaire asking for strategies; subsequently they were debriefedand thanked.

Our experimental design called for the specific order of city task and recognition test, rather then counterbalancing thetask order. Having the recognition test before the city task could have biased participants by making salient that we (the

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experimenters) were interested in whether they recognized the stimuli. In addition, by keeping this fixed task order, wewere able to reliably measure the hemodynamic activity elicited by recognition judgments. Had the recognition test comefirst, the participants would have had to judge not only whether they recognized the city, but also whether the source ofthe recognition was just from the experiment or possibly from elsewhere. The additional demands of this discriminationtask mean that the recognition judgments from the two task orders could draw on somewhat different brain structures andso involve brain structures that would not otherwise be involved in the application of the fluency heuristic. Nevertheless,we expected that in the recognition test, participants could reliably report whether they recognized the city from theexperiment or from elsewhere. That is, they would rarely mis-categorize as recognized a city that they had only encoun-tered in the experiment. Support for this assumption come from the studies by Pohl (2006) and Goldstein and Gigerenzer(2002). Neither study found any differences in the recognition rates that depended on the task order. However, we wouldexpect that the recognition latencies would be faster, because the cities would have been recently seen in the inferencetask. As a result, we may be underestimating the absolute differences in retrieval fluency between items, but the relativedifferences should be preserved. For a detailed description of how the city pairs were generated, please see Volz and vonCramon (2006).

2.3. Data acquisition

Imaging was performed on a 3T scanner (Siemens TRIO, Erlangen, Germany). Twenty-two axial slices (4 mm thickness,20% spacing, field of view [FOV] 19.2 cm, data matrix of 64 � 64 voxels, and in-plane resolution of 3 � 3 mm) parallel tothe bicommissural plane (AC–PC) covering the whole brain were acquired using a single-shot echo-planar imaging (EPI) se-quence (TR 2 s, echo time [TE] 30 ms, flip angle 90�). One functional run with 872 time points was run with each time pointsampling over the 22 slices. Prior to the functional runs, 22 anatomical T1-weighted modified driven equilibrium Fouriertransform (MDEFT; Norris, 2000; Ugurbil et al., 1993) images (data matrix of 256 � 256 voxels, TR 1.3 s, TE 10 ms) were ac-quired as well as 22 T1-weighted EPI images with the same spatial orientation as the functional data. The latter were used tocoregister the functional scans with previously acquired high-resolution full-brain 3-dimensional brain scans.

2.4. Data evaluation

The functional imaging data were processed using the software package LIPSIA (Lohmann et al., 2001). Functional datawere motioncorrected off-line with the Siemens motion–correction protocol. To correct for the temporal offset betweenthe slices acquired in one scan, a cubic spline interpolation was applied. A temporal high-pass filter with a cut-off frequencyof 1/160 Hz was used for baseline correction of the signal and a spatial Gaussian filter with 5.65 mm full-width half-maxi-mum (FWHM) was applied. The anatomical slices were coregistered with the high-resolution full-brain scan that resided inthe stereotactic coordinate system and then transformed by linear scaling to a standard size (Talairach & Tournoux, 1988).The transformation parameters obtained from this step were subsequently applied to the preprocessed functional slices sothat the functional slices were also registered into the stereotactic space. This linear normalization process was improved bya subsequent processing step that performed an additional nonlinear normalization known as ‘‘demon matching.” In thistype of nonlinear normalization, an anatomical 3-dimensional data set (i.e., the model) is deformed such that it matches an-other 3-dimensional anatomical data set (i.e., the source) that serves as a fixed reference image (Thirion, 1998). Voxel sizewas interpolated during coregistration from 3 � 3 � 4 mm to 3 � 3 � 3 mm. The statistical evaluation was based on a least-squares estimation using the general linear model (GLM) for serially autocorrelated observations (random effects model;Friston, Frith, Turner, & Frackowiak, 1995; Worsley & Friston, 1995).

The general linear regression performs a ‘‘precoloring” of the data; that is, it applies a temporal Gaussian smoothing witha user-specified kernel width given by the parameter FWHM. The smoothing imposes a temporal autocorrelation that deter-mines the degrees of freedom. An event-related design was implemented; that is, the hemodynamic response function wasmodeled by means of the experimental conditions for each stimulus (event being onset of stimulus presentation). The designmatrix was generated using a synthetic hemodynamic response function and its first and second derivative (Friston et al.,1998) and a response delay of 6 s. The model equation, including the observation data, the design matrix, and the error term,was convolved with a Gaussian kernel of dispersion of 4 s FWHM to deal with the temporal autocorrelation (Worsley & Fris-ton, 1995). Contrast images, that is, estimates of the raw score differences between specified conditions were generated foreach participant. The single-subject contrast images were entered into a second-level analysis based on Bayesian statistics(Neumann & Lohmann, 2003). In Neumann and Lohmann’s approach, posterior probability maps and maps of the effect sizefor the effects of interest in groups of participants are calculated on the basis of the resulting least-squares estimates ofparameters for the GLM. The output of the Bayesian second-level analysis is a probability map showing the probability ofthe contrast being larger than zero. For visualization, a threshold of 99% was applied to the probability maps. For each par-ticipant all contrasts of interest were calculated.

Reasons to use Bayesian second-level analysis for fMRI data are manifold: A comparison between the established analysisbased on t statistics and Bayesian second-level analysis showed that the latter is more robust against outliers. Furthermore,the Bayesian approach overcomes some problems of null hypothesis significance testing, such as the need to correct for mul-tiple comparisons, and this approach provides estimates for the size of an effect of interest as well as for the probability thatthe effect occurs in the population (Neumann & Lohmann, 2003).

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The main purpose of the fMRI study was to reveal brain areas involved in fluency-heuristic-based decision processes. Thefirst analysis therefore included regressors for trials with large differences in recognition latencies, trials with small differ-ences in recognition latencies, trials that were answered quickly (individually determined as trials below the median re-sponse time (RT)), trials that were answered slowly (individually determined as trials above the median RT), RU trials,and UU trials. Concerning the factor differences in recognition latencies, we classified trials with latencies > 400 ms as trialswith large differences and trials with latencies > 100 ms and6400 ms as trials with small differences following Hertwig et al.(2008). A second analysis included regressors for trials that were and were not answered according to the fluency heuristic(factor fluency), correctly and incorrectly answered trials (factor incorrectness), RU trials, and UU trials. The analysis that wasconducted as a manipulation check for recognition- and retrieval-related activations was conducted by building special con-trasts within the two described models. Note that in all analyses only trials in which the difference in recognition latenciesbetween two recognized cities equaled or exceeded a just noticeable difference (JND) of 100 ms were included (see below forthe rationale).

3. Results

3.1. Behavioral results

On average, participants faced 55 RR trials (range: 34–76), 46 RU trials (range: 38–61), and 39 UU trials (range: 19–62).Volz and von Cramon (2006) specifically investigated the RU trials, where by definition the recognition heuristic is applicablebut the fluency heuristic is not. Here we focus on the RR trials, where the fluency heuristic is applicable but the recognitionheuristic is not.

As outlined above, in this contribution, we are exclusively interested in how people deal with situations in which theyrecognize both of two presented objects and have to arrive at a decision about a criterion, which is very likely unknown(e.g., city size). Hence, in the following section on behavioral data we concentrate on RR trials only. For an overview ofthe behavioral results, please refer to Table 1.

3.2. The validity of the fluency heuristic in the present sample

To determine how ecologically valid retrieval fluency was in our sample, we quantified the strength of the relationshipbetween the retrieval fluency and the criterion as the proportion of times a more quickly recognized city indeed had a highercriterion value than the city that required more time to be recognized. Thus, fluency validity is calculated as: vf = Rf/(Rf + Wf),where Rf is the number of correct inferences made by relying on the fluency heuristic, and Wf is the number of incorrectinferences made by relying on the fluency heuristic. In the present study, the mean fluency validity was .55 (95% confidenceinterval [CI] = 0.497, 0.588) and exceeded chance level (.50), t(17) = 2.13; p = .048. Note that in this analysis only trials inwhich the difference in recognition latencies between two recognized cities equaled or exceeded a JND of 100 ms were in-cluded. The JND of 100 ms was determined following Fraisse’s (1984) suggestion, based on a thorough review of the timingliterature, that durations of less than 100 ms are perceived as instantaneous. Hertwig et al. (2008) provided supporting re-sults by showing that when differences in recognition latencies were shorter than 100 ms, people’s ability to discriminatebetween recognition latencies (of the two objects) dropped close to chance level.

To be able to directly compare our results with those of Hertwig et al. (2008), we categorized the objective difference inrecognition latencies into four equal bins: 0–99 ms, 100–399 ms, 400–699 ms, and > 700 ms and calculated fluency validityas a function of these four bins (see Table 1). For the first three bins, we replicated Hertwig et al.’s findings showing that thereis a tendency that the larger the objective difference in recognition latencies, the higher the fluency validity. Yet, the lineartrend did not continue for the last bin (>700 ms), F(3, 14) = .508; p = .634. Given recent results on fluency validities in differ-ent environments, fluency validity in the present sample can be considered moderate and hence participants could at leasttheoretically infer the distal properties of the world (Hertwig et al., 2008).

3.3. Participants’ accordance with the fluency heuristic

To determine to what degree people’s inferences agreed with the fluency heuristic in the present sample, we computedfor each participant the percentage of inferences that were in line with the fluency heuristic among all cases in which it could

Table 1Means (SD) of fluency heuristic validity, fluency heuristic accordance, percent correct overall, and percent correct when applying the fluency heuristic.

Overalla 0–99 ms 100–399 ms 400–699 ms >700 ms

Fluency heuristic validity 0.55 (0.21) 0.49 (0.13) 0.53 (0.13) 0.58 (0.24) 0.53 (0.26)Fluency heuristic accordance 0.68 (0.05) 0.48 (0.13) 0.64 (0.09) 0.75 (0.17) 0.74 (0.17)% Correct 62.8 (19.5) 60.4 (20.4) 65.3 (11.1) 63.6 (23.3) 59.5 (24.2)% Correct with the fluency heuristic 81.3 (14.8) 54.2 (32.9) 79.3 (10.2) 83.7 (14.9) 80.9 (19.2)

a Data for this category exclude trials with a just noticeable difference of 100 ms or less.

Conjunction analysis of the contrasts shown in A & B

C

claustrum x = 31

y = 6

Interaction fluency and incorrectness

B

claustrumx = 34

y = 6

RCZ

claustrum

Interaction difference in recognition latencies & response time

A

x = 31claustrum

Fig. 1. Group averaged activations are shown on four slices taken from an individual brain normalized and aligned to the Talairach stereotactic space. Forvisualization a threshold of 99% was applied to all probability maps. (A) Results of the interaction contrast differences in recognition latencies (large vs. small)and response time (individually determined fast slow responses). To understand which factor combination is driving the effects, the mean percent signalchanges with standard errors are reported for all four conditions. (B) Results of the interaction contrast fluency heuristic (decisions agreeing vs. conflicting withthe fluency heuristic) and incorrectness (incorrect vs. correct responses). To understand which factor combination is driving the effects, the mean percentsignal changes with standard errors are reported for all four conditions. (C) Result of the conjunction analysis of the two interaction contrasts. Abbreviations:amla: above median and large differences in recognition latencies; amsm: above median and small differences in recognition latencies; bmla: below medianand large differences in recognition latencies; bmsm: below median and small differences in recognition latencies; RCZ: rostral cingulate zone; flc: fluencyheuristic trials and correct; fli: fluency heuristic trials and incorrect; nflc: no fluency heuristic trials and correct; nfli: no fluency heuristic trials and incorrect.

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K.G. Volz et al. / Consciousness and Cognition 19 (2010) 829–837 835

be applied (i.e., among all RR trials), excluding trials with a JND of 100 ms or smaller. The mean fluency heuristic accordancein the present sample was .678 (CI: 0.649, 0.707). The interindividual variation in the proportion of judgments that agreedwith the fluency heuristic was rather moderate. The rate of individuals’ fluency heuristic accordance ranged between .58 and.79. Hence, none of the participants appeared to have systematically decided against fluency (i.e., fluency accordance ratebelow .50), nor did anyone use the fluency heuristic all the time. The fluency heuristic applicability ranged between .24and .54 with a mean of .39 (CI: 0.343, 0.437) and hence in more than a third of all inferences, the fluency heuristic was appli-cable. In Table 1, we report fluency heuristic accordance rates as a function of differences in recognition latencies. Accor-dance rates increased with larger differences in recognition latencies, F(3, 14) = 11.67; p = .0001, but did not differ muchbetween the last (>700 ms) and the second to last (400–699 ms) bin, t(17) = .876. Again, except for the last bin, we replicatedthe results of Hertwig et al. (2008).

3.4. Participants’ performance in the inference task

Overall, participants scored a median .63 accuracy (CI: 0.580, 0.683) calculated as percent correct of all RR trials irrespec-tive of response strategy (trials with a JND of 100 ms or smaller were excluded). In Table 1, we report percent correct of all RRtrials as a function of differences in recognition latencies. Overall, performance did not differ substantially subject to differ-ences in recognition latencies, F(3, 14) = .322; p = .76. Yet, this pattern changes when we look at percent correct made by thefluency heuristic: There is a clear tendency that the larger the objective difference in recognition latencies, the higher thepercent correct, F(3, 11) = 5.50; p = .010. Again, the linear trend did not continue for the last bin. Inferences in line withthe fluency heuristic were made faster (mean RT: 2842 ms) than inferences conflicting with the fluency heuristic (meanRT: 2944 ms), t(17) = �3.47, p = .003.

In all analyses where the variable of interest was split according to differences in recognition latencies, the last bin(>700 ms) seemed to be the odd one out. One reason for this finding might be the restriction of response time. Before theexperimental session, participants were informed that the two city names would be presented for a maximum of 4 s duringwhich their response would be recorded. If no response was given within the 4 s, then the trial would be counted as a no-response trial. Participants’ mean response time was 3055 ms when differences in recognition latencies exceeded 700 ms.Thus, when it took participants fairly long to recognized one of the city names, the overall decision time was almost ex-pended. Accordingly, this time pressure might have eventually led to guessing.

3.5. Imaging results

To test for the specific neural correlates of fluency-heuristic-based decision processes, we calculated two interaction con-trasts and their conjunction. Based on previous findings on fluency-heuristic-based decisions (Hertwig et al., 2008), the inter-action contrasts were designed to capture fluency-based inferences, relatively uncontaminated by extensive knowledgeabout the options.

3.5.1. Interaction between differences in recognition latencies and response timeThe interaction contrast of the factors large versus small differences in recognition latencies and fast versus slow response

times revealed activation bilaterally within the dorsal claustrum and the left anterior insula; bilaterally within the anterior thal-amus region and amygdala; and within the anterior portion of the left superior temporal gyrus, left supramarginal gyrus, and rightcuneus (see Fig. 1A and Table 2). When we plotted the mean percent signal change in the dorsal claustrum, results revealed spe-cifically those trials with large differences in recognition latencies and fast response times to elicit activation within this area.

3.5.2. Interaction between fluency and incorrectnessThe interaction contrast of the factors fluency heuristic trials versus no fluency heuristic trials and incorrect versus correct

responses revealed activation within the right dorsal claustrum and bilaterally within the posterior rostral cingulate zone

Table 2Anatomical specification, cluster size (mm3), and Talairach (x, y, z) coordinates of significantly activated voxels of the interaction contrast differences inrecognition latencies (large vs. small differences) and response time (fast vs. slow responses).

Anatomical specification mm3 x y z

L dorsal claustrum 81 �35 �6 3R dorsal claustrum 81 31 4 3L anterior insula 162 �26 15 12R anterior thalamus region 1755 13 �3 12L anterior thalamus region 621 �11 �12 9L superior temporal gyrus 324 �50 �36 9R amygdala 108 19 �6 �9L amygdala 81 �23 �9 �9L supramarginal gyrus 162 �47 �48 27R cuneus (occipital lobe) 351 4 �84 27

Note: Only clusters of at least five contiguous voxels are reported. L: left, R: right.

836 K.G. Volz et al. / Consciousness and Cognition 19 (2010) 829–837

(RCZ) and the left superior and middle temporal gyrus (see Fig. 1B and Table 3). When we plotted the mean percent signalchange in the right dorsal claustrum, results revealed both incorrect fluency trials and correct nonfluency trials to specificallyelicit activation within this area.

3.5.3. Conjunction analysis of the two interaction contrastsTo test for those regions that are commonly activated across the two interaction contrasts, we calculated a conjunction

analysis, that is, a test for a logical AND (Nichols et al., 2005). Activation was revealed solely within the right dorsal claus-trum (x = 31, y = 6, z = 9; mm2 = 135; see Fig. 1C).

4. Discussion

The present imaging study was conducted to investigate the cognitive processes underlying the fluency heuristic. Simplystated, when people use the fluency heuristic they will infer that the more easily retrieved item has the higher criterion value(Schooler & Hertwig, 2005). Our method was to isolate fluency-heuristic-based judgments, to facilitate mapping specificbrain areas subserving the heuristic.

We started with two hypotheses: The first one assumed that the VMPFC is a neural correlate of fluency-heuristic-baseddecisions and was derived from previous imaging findings on phenomenologically similar kinds of decisions. The secondhypothesis suggested that the claustrum is a neural correlate of fluency-heuristic-based decisions and was derived froman account of retrieval fluency based on successfully binding memory traces.

We ran two interaction contrasts specifically designed to pull out trials that were very likely based on the fluency heu-ristic. The first interaction included the factor difference in recognition latencies (large vs. small differences) and the factorresponse time (fast vs. slow responses). The second interaction included the factor adherence to the fluency heuristic and thefactor correctness. Both specific interaction contrasts elicited activation within the dorsal claustrum, as did a conjunctionanalysis of the two interaction contrasts. No activation within the VMPFC was observed. We take the latter result to indicatethat fluency-heuristic-based decisions are dissimilar to metacognitive judgments about the felt-rightness of a memory.

Thus, the present results revealing the dorsal claustrum as the most consistent neural correlate of decisions that are mostlikely to rely on the fluency heuristic support our second hypothesis. Hitherto, comparative anatomical, tractographic, elec-trophysiological, tracing, histological studies and the like have dominated the investigation of the claustrum, yet claustralactivation has only seldom been reported in fMRI studies (Volz & von Cramon, 2006). Recently, Crick and Koch (2005) spec-ulated that the claustrum gives rise to integrated conscious percepts.

Morphologically, the claustrum is a thin, irregular band of gray matter, hidden beneath the inner surface of the neocortexand in close proximity to the insula. Macroscopically, the claustrum is divided into a dorsal (or posterosuperior) part and aventral (anteroinferior) part (Fernández-Miranda et al., 2008; Kowianski et al., 1999). The dorsal claustrum lies between theputamen, from which it is separated by the external capsule, and the insular cortex, from which it is separated by the ex-treme capsule (Edelstein & Denaro, 2004; Fernández-Miranda et al., 2008).

Considering the claustrum’s tractography may hint at its function. Tractography studies have revealed extensive neocorticalconnections and a topographical organization in the dorsal claustrum, where posterior cortical areas project to the posteriorpart of the dorsal claustrum and more anterior parts of cortical areas converge onto the anterior part of the claustrum, therebyforming (overlapping) cortical projection zones (Fernández-Miranda et al., 2008; Morys, Narkiewicz, & Wisniewski, 1993). Al-most all of the claustrum-to-cortex projections are reciprocated, with only a few exceptions (e.g., V1; Sherk, 1986). In addition,the claustrum maintains two-way connections with subcortical structures involved in the limbic system, such as the amygdalaand prepiriform cortex (Fernández-Miranda et al., 2008). These data were taken to suggest that the claustrum is a structure thatinterconnects the senses, providing them direct access to each other (Ettlinger & Wilson, 1990). In doing so, the claustrum playsa crucial role as a polymodal structure engaged in the transfer of information to and from various cortical regions (Kowianskiet al., 1999). Crick and Koch’s (2005) metaphor for the claustrum is that of a conductor coordinating the players in an orchestra,where the musicians are the various cortical regions. The conductor is responsible for binding the performances of individualmusicians into an integrated, synchronous whole. Similarly, the claustrum rapidly combines the different attributes of objectsboth within and across modalities so that an integrated whole is experienced, rather than a collection of isolated attributes.

Given the findings on the function of the claustrum so far, claustral activation during fluency-heuristic-based decisionsmay reflect the experience of an integrated signal associated with the retrieval of one or more memory records. For example,

Table 3Anatomical specification, cluster size (mm3), and Talairach (x, y, z) coordinates of significantly activated voxels of the interaction contrast fluency (decisionsagreeing vs. conflicting with the fluency heuristic) and incorrectness (incorrect vs. correct responses).

Anatomical specification mm3 x y z

R claustrum 216 34 6 9L rostral cingulate zone, posterior part 1107 �10 6 39R rostral cingulate zone 216 12 3 42L superior temporal gyrus 297 �53 �30 21L middle temporal gyrus, posterior part 243 �42 �62 9

Note: Only clusters of at least five contiguous voxels are reported. L: left, R: right.

K.G. Volz et al. / Consciousness and Cognition 19 (2010) 829–837 837

episodic, semantic, and visual information may all be rapidly combined and bound in the claustrum. This signal may thensuffice to guide inferences, and accordingly, be experienced as fluency. This differs from the way fluency was treated bySchooler and Hertwig (2005), where it was taken to simply represent the speed with which a single memory record is re-trieved. The present finding corresponds to findings on perceptual fluency: In the study by Volz and von Cramon (2006), par-ticipants had to judge the coherence of pictorial stimuli, and claustral activation was suggested to represent the temporalsynchronization process during perceptual fluency judgments. Together, we take the present results to indicate that infer-ences that are made according to the fluency heuristic specifically rely on an integrated signal at the fast time scale.

Acknowledgments

We thank Thomas Dratsch for helping with the behavioral data analyses and Anita Todd for editing a draft of this article.

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