Probabilistic Inferences Under Emotional Stress: How Arousal Affects Decision Processes

14 Pages·2015·0.63 MB·English

Journal of Behavioral Decision Making,J. Behav. Dec. Making (2015)
Published online in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/bdm.1896
Probabilistic Inferences Under Emotional Stress: How Arousal Affects
Decision Processes
SZYMONWICHARY1,2*,RUIMATA2,3andJÖRGRIESKAMP2
1UniversityofSocialSciencesandHumanities,Warsaw,Poland
2UniversityofBasel,Basel,Switzerland
3MaxPlanckInstituteforHumanDevelopment,Berlin,Germany
ABSTRACT
Manymodelsofdecisionmakingneglectemotionalstatesthatcouldaffectindividuals’cognitiveprocesses.Thepresentworkexploresthe
effectofemotionalstressonpeople’scognitiveprocesseswhenmakingprobabilisticinferences.Twocontrastinghypothesesaretestedagainst
oneanother:theuncertainty-reductionandattention-narrowinghypothesesofhowemotionalstressaffectsdecisionmaking.Intheexperi-
mental study, emotional stress was induced through the use of highly aversive pictures immediately before each decision. Emotional state
wasassessedbybothsubjective(stateanxiety,arousal,andpleasantnessratings)andobjective(skinconductance)measures.Theresultsshow
that emotional stress impactsdecision making; in particular, emotionally aroused participants seemto have focused on the most important
informationandselectedsimplerdecisionstrategiesrelativetoparticipantsinacontrolcondition.Theresultsareinlinewiththeattention-
narrowing hypothesis and suggest that emotional stress can impact decision making through limited predecisional information search and
theselectionofsimplerdecisionstrategies.Copyright©2015JohnWiley&Sons,Ltd.
keywords emotion;skinconductance;informationsearch;strategyselection
Soldiers,surgeons,andfirefightersareoftenfacedwithemo- & Peterson, 2005; Russell, 1980), which describes affective
tionally stressful situations in which they must gather infor- states with two dimensions of arousal (bodily activation vs.
mation before making life-and-death decisions. Laypeople deactivation) and valence (pleasure vs. displeasure). The
face similar—albeit less extreme—situations in which they adjectives typically used to describe emotional stress include
must make decisions under emotional stress. How do high- “stressed,” “nervous,” and “tense,” with antonyms being
arousal negative emotional states impact the information “relaxed,”“calm,” and“atease” (FeldmanBarrett&Russell,
searchanddecisionstrategiesthatpeopleemployundersuch 1999;Russell,Lewicka,&Niit,1989).
conditions?Therearetwoalternativeviewsregardingthein- High arousal and negative valence are also characteristic
fluenceofhigh-arousalnegativeaffectoninformationsearch of acute affective states—the specific emotions of anger,
anddecisionmaking.Oneviewemphasizestheappraisalten- disgust, and fear. These emotions are thought, however, to
dencies associated with such states. For example, anxiety is differ in other regards, such as where they can be situated
characterized byappraisals ofone’slackof controlandthus on an approach-avoidance dimension: Anger is usually
mayleadtoanincreasedrelianceonmorecomplexinforma- associated with approach motivation, whereas disgust and
tion processing in an attempt to reduce risk and uncertainty fear are associated with avoidance motivation (Carver &
(e.g.,Han,Lerner,&Keltner,2007).Anotherlineoftheoriz- Harmon-Jones, 2009; Rozin, Haidt, & McCauley, 2008).
ingholdsthathigh-arousalnegativestatesleadtonarrowing The latter is also true of sadness, but sadness is associated
of the scope of perceptual and cognitive function in an with varying degrees of arousal (Bonanno, Goorin, &
attempt to maximize the likelihood of finding a solution to Coifman,2008).Anotherrelatedaffectivestate,stateanxiety,
the problem at hand (e.g., Derryberry & Tucker, 1994). We is also characterized by high arousal, negative valence, and
present a study in which we tested these two competing avoidance motivation; however, it can be more prolonged
views in the decision-making domain. In particular, we thantheacuteemotionoffear,andthisiswhyitissometimes
examine the role of a high-arousal negative emotional state referredtoas“sustainedfear”(Lang,Davis,&Ohman,2000).
in predecisionalinformation search andstrategyselection in Inthecurrentstudy,wefocusonacutehigh-arousalnega-
a probabilisticinference task. tive affective states that are typically associated with avoid-
ance motivation, such as anxiety, fear, and disgust. We use
Christianson’stermemotionalstress(Christianson,1992)to
Emotional stress: high-arousal negative emotional states refertoageneralacutestateofhigharousal,negativevalence,
Higharousalandnegativevalencearecharacteristicsof“emo- and avoidance motivation, which represents a typical re-
tionalstress” (Christianson,1992),anaffectivestateinduced sponse to negative, threatening events (Christianson, 1992;
by threatening stimuli. Emotional stress can be identified by Mathews&Mackintosh,1998).Pleasenotethatwhilehigh-
reference to the circumplex model of affect (Posner, Russell, arousal negative-valence states are heterogeneous in a num-
berofaspects,theysharemanybiologicalunderpinningsthat
leadustopredictsimilarimpactsondecision-makingbehav-
*Correspondence to: Szymon Wichary, Interdisciplinary Center for Applied ior.Specifically,acutestates,including,fear,acutestress,and
Cognitive Studies, University of Social Sciences and Humanities, Ul.
Chodakowska19/31,03-815Warsaw,Poland.E-mail:[email protected] state anxiety, and the more prolonged states, such as trait
Copyright©2015JohnWiley&Sons,Ltd.
Journal of Behavioral Decision Making
anxietyandchronicstress,arerelatedtotheactivityofthestress andMikulincer,Kedem,andPaz(1990).Afewstudiesinthe
response system, the hypothalamic–pituitary–adrenal axis decision-making domain are also compatible with this view.
and the sympathetic–adrenal–medullary systems (Schommer, Mano (1992) reported that participants under social stress
Hellhammer, & Kirschbaum, 2008; Tsigos & Chroussos, (induced by an anticipated in-class presentation) were more
2002). High-arousal states are associated with increases in extreme in their judgments of job applicants, suggesting that
specific hormone levels released from the hypothalamus and theymayhavefocusedononlyasubsetofthepresentedinfor-
central amygdala (e.g., corticotrophin releasing factor; Owens mation. Similarly, Lewinsohn and Mano (1993) showed that
&Nemeroff,1991).Inturn,thesehormonesmodulatetheactiv- highlyarousedparticipantsacquiredlessinformationanddid
ityofthelocuscoeruleus(LC),abrainstemnucleusassociated so in a more selective manner by focusing more on subjec-
withemotionalarousalandattentionalselectivity(Aston-Jones, tivelyimportantattributes.
Rajkowski,&Cohen,1999;Aston-Jones,Rajkowski,Kubiak, In summary, the uncertainty-reduction and attention-
Valentino, & Shipley, 1996). In parallel to the activation of narrowinghypothesesrepresenttwoalternativeviewsofhow
theLC,specifichormonestriggertheactivationofthesympa- high-arousalnegativeaffectassociatedwithavoidancemotiva-
thetic adrenalmedullarysystem,whichleads tothe peripheral tioninfluences judgment anddecision making.Wesought to
physiologicaleffects,describedas“arousal,”thatareassociated testthetwoviewsusingacomplexdecision-makingtask.
with the activity of the sympathetic nervous system and
indexed, for example, by skin conductance (Boucsein, 2011,
pp.347–390).Insum,emotionalstressmaybeacommonterm Probabilistic inference task
to describe high-arousal, negative states with common func- Inaprobabilisticinferencetask,peoplehavetoinferthecor-
tionalconsequencesfordecision-makingbehavior. rectstateoftheworldonthebasisofdifferentpiecesofgiven
information (Rieskamp, 2006; Rieskamp & Hoffrage, 1999,
2008).Forinstance,a physicianmayinferwhichoftwopa-
Emotional stress and decision making: the uncertainty- tients requires more urgent treatment on the basis of several
reductionversusattention-narrowinghypothesis cues, such as respiration and heart rate, that predict, albeit
Fromafunctionalperspective,emotionsandmoodsevolved without certainty, the physical status of the patient. One
to convey information regarding the nature of the situation prominent approach to probabilistic inference is that people
(Schwarz, 2000; Schwarz & Clore, 2003). In particular, select inference strategies from their repertoire of strategies
negative emotional states can signal danger and motivate adaptively, depending on the task conditions (Gigerenzer,
the individual to avoid or escape threats. In line with this Todd, & the ABC Research Group, 1999; see also Payne,
view, the appraisal-tendency framework emphasizes how Bettman, &Johnson,1993).Strategies for performing prob-
appraisal tendencies regarding uncertainty and control can abilisticinferencedifferintheamountofinformationtheyre-
impact judgment and decision making (Han et al., 2007; quireandthewayinwhichtheinformationisprocessed.One
Lerner&Keltner,2000).Forexample,Hanetal.(2007)pro- simple lexicographic strategy called take the best (TTB;
posed that anxiety tends to be associated with appraisals of Gigerenzer & Goldstein, 1996) infers that when comparing
uncertainty(Lazarus,1991)andthatitmotivatesindividuals different alternatives, the one with the highest cue value for
toreduceuncertaintyandincreasecontrol—ahypothesisthat themostvalidcueisselectedastheonewiththehighestcri-
we term the uncertainty-reduction hypothesis. Some studies terion value. The validity of a cue is defined as the condi-
support this view. For instance, Raghunathan and Pham tional probability of making a correct inference when the
(1999) showed that anxious decision makers preferred a cue discriminates, that is, when one alternative has a higher
low-risk/low-reward option more so than did sad decision cue value than all the other alternatives. If the cue with the
makers. Tiedens and Linton (2001) manipulated whether highest validity does not discriminate, then TTB considers
participants experienced anxiety or anger (a high-certainty thecuewiththesecondhighestvalidity,andsoon.Analter-
emotion) before filling out a survey designed to measure native strategy called weighted additive (WADD) integrates
reliance on a single heuristic cue in making judgments. the available information; it computes the sum of all cue
Anxious participants demonstrated less reliance on the heu- values multiplied by their cue validity for each alternative
risticcuethandidangryparticipants,suggestingthatanxious and selects the alternative with the largest sum. The first
participants engaged in more effortful processing. strategy, TTB, is a noncompensatory strategy that does not
Alternativefunctionalviewsofaffectsuggestthataversive integrate any information. It is noncompensatory because
situations, which provoke anxiety, can lead to attention nar- theinformationofcueswithhighvaliditycannotbecompen-
rowing,tofacilitateconcentrationontheproblemandthereby satedforbycueswithlowervalidity.Incontrast,WADDisa
increase the likelihood of success (emotion as modulation of compensatorystrategy that integrates all the information.
informationprocessing;Derryberry&Tucker,1994;Friedman Takentogether,TTBandWADDareoftenquitesuccess-
& Förster, 2010)—which we call the attention-narrowing ful in describing people’s inferences (Bröder, 2000, 2003;
hypothesis. This hypothesis follows Easterbrook’s (1959) Rieskamp&Hoffrage,2008).PeopleseemtoselectWADD
seminal proposal that emotional states characterized by high moreoftenwhentheymakeinferencesinanewenvironment
physiological arousal and negative valence narrow the scope with which they have no experience and where the costs of
of both perceptual and conceptual attention. The hypothesis applying the strategy are low. In contrast, in cases with in-
hasfoundsupportinvariousstudiessuchasthoseofCacioppo, creasedapplicationcosts,forinstance,owingtohighmonetary
Berntson,andCrites(1996),GableandHarmon-Jones(2010), costsassociatedwithinformationsearch,TTBmoreaccurately
Copyright©2015JohnWiley&Sons,Ltd. J.Behav.Dec.Making(2015)
DOI:10.1002/bdm
S.Wichary et al. Inferences Under EmotionalStress
predicts people’s inferences (Rieskamp & Otto, 2006). Fol- and task performance. Additionally, six participants (9.2%)
lowing the approach taken by Rieskamp and Otto (2006), wereexcludedfromtheanalysesinvolvingskinconductance
we concentrated on TTB and WADD as two prototypical as nonresponders, owing to the lack of specific and nonspe-
inference strategies that represent simple and exhaustive cific electrodermal responses in the EDA signal (Boucsein,
decisionmaking,respectively,whilebeingawarethatpeople 2011, pp. 439–440), so here, only n=59 (41 women and
mightusevariantsofthesestrategiesorevenotherstrategies. 18 men) remained. Although excluding these participants
influenced the results of the manipulation check involving
skinconductance,itdidnotinfluencetheresultsofanalyses
The current experiment of information search and strategy selection with skin con-
The goal of the current study was to test the uncertainty- ductance as the independent variable. Participants were stu-
reduction and attention-narrowing hypotheses against each dents of various departments at the University of Basel and
other by observing the effects of a direct induction of were recruited through an online recruitment system and a
emotional stress (relative to a low-arousal neutral state) on generally accessible information exchange webpage. They
decision making. Specifically, we showed participants high- werepaidaflatshow-upfeeof20CHFplusaperformance-
arousal aversive (vs. low-arousal neutral) slides from the basedbonuscontingentontheirdecisions(upto2.80CHF).
InternationalAffectivePictureSystem(IAPS;Lang,Bradley,
&Cuthbert,1990,1998)withthegoalofobservingtheeffects
Apparatusand materials
of this manipulation on predecisional information search and
Probabilistic inference task
strategyselection.Specifically,wechosetheslidesfromtheparts
Thecomputerizedinferencetaskconsistedofmakingdecisions
oftheemotioncircumplexrepresenting“distress”versus“calm-
aboutwhichoffourjobcandidateswouldbethemostproduc-
ness”(Langetal.,1998;Posneretal.,2005).Intermsofspecific
tiveworker.Thecandidatesweredescribedwiththefollowing
verballabels,theslidesinourneutral(control)conditionelicited
six cues: conscientiousness, intelligence, initiative, creativity,
“calmness”or“relaxation,”whereastheslidesintheemotional
communicationskills,andagreeableness.Thecuevalueswere
(experimental) condition elicited “nervousness,” “tension,” or
coded as numbers ranging from 1 to 5, with 5 indicating the
“anxiety”(FeldmanBarrett&Russell,1999;Posneretal.,2005).
highest evaluation on a cue. The corresponding cue validities
According to the uncertainty-reduction hypothesis, in-
were.82,.80,.76,.69,.57,and.44.Thecuevaliditieswerede-
creased anxiety will motivate individuals to decrease uncer-
fined as the conditional probabilities of making the correct
taintybyengaginginmoreextensiveandeffortfulinformation
choice basedona particularcue,ontheconditionthatthecue
processing,whichshouldtranslateintoincreasedpredecisional
discriminates among the alternatives (Rieskamp & Hoffrage,
informationsearchandtheselectionofmorecomplexdecision
1999). The task consisted of an instruction phase, a training
strategies.Incontrast,theattention-narrowinghypothesissug-
phaseofthreedecisions,andatestphaseof56decisions.
gests that emotional stress, that is characterized by increased
anxiety,willleadindividualstoreduceinformationprocessing
through a focus on the most relevant pieces of information
Affect manipulation
and the selection of simpler decision strategies that neglect
The manipulation of affect consisted of presenting one IAPS
someoftheavailableinformation.
Wetestedthesehypothesesbyrelatingparticipants’ emo- slide (Lang et al., 1990, 1998) before each of the 56 trials in
the test phase. The slides were selected from the parts of the
tionalstatetotheirinformationsearchandstrategyselection.
To accurately determine participants’ emotional state, we emotion circumplex representing “distress” versus “calmness”
(Bradley, Codispoti, Cuthbert, & Lang, 2001; Lang et al.,
assessedstateandtraitanxiety,subjectivearousal,andvalence
1998;Posneretal.,2005)withmeanarousalandvalencenorma-
in response to the affective pictures as well as electrodermal
tiveratingsfortheemotionalstressconditionatM =6.01,
activity (EDA). When assessing the effects of the manipula- arousal
SD=0.76andM =2.27,SD=0.79andfortheneutralcon-
tion on EDA, we took into account that individuals might valence
ditionatM =2.80,SD=0.46andM =4.92,SD=0.35
differ in electrodermal reactivity (Boucsein, 2011, p. 392). arousal valence
(see forslideidentifiersandtheirindividualnormativeratings).1
Byusingthediversemeasuresofemotionalstateanddecision
making,wehopedtoaccuratelyassesstheroleofbothsubjec-
tiveandobjectivecomponentsofhigh-arousalnegativeaffect
ondecisionmaking. Skin conductance
We measured skin conductance with a Coulbourn V-71-23
skin conductance coupler (Coulbourn Instruments, LLC,
METHOD
1Thespecificityoftheaffectivestatethatweinducedwasessentialforour
Participants predictions (i.e., testing the attention-narrowing and uncertainty-reduction
Seventy young adults participated in the study. However, hypotheses)asfaras(i)usingtheIAPSslides,weinducedanxietythatis
five participants failedto complete the inference task, sofor thecommongroundbetweenthetwotestedhypothesesand(ii)wedidnot
induce negative emotions associated with approach, that is, anger (Frey
the analyses of arousal, valence, and task performance, data etal.,2014).ThemanipulationcheckwiththeStateTraitAnxietyInventory
of only 65 participants (46 women and 19 men) were used. ensuredthatwedidinduceanxiety,andanadditionalanalysisoftheIAPS
picturesusingtheemotionalcategorydatafromMikelsetal.(2005)allowed
Two participants provided no state anxiety ratings, so here,
ustostatethatwedidnotinduceanger(onlyoneslideinourmaterials-in-
n=63 (45 women and 18 men) for the analyses of anxiety ducedanger).
Copyright©2015JohnWiley&Sons,Ltd. J.Behav.Dec.Making(2015)
DOI:10.1002/bdm
Journal of Behavioral Decision Making
Allentown, PA), with two Ag–Cl electrodes filled with Results
Microlyte gel provided by Coulbourn Instruments. The Theresultsarestructuredasfollows.First,weshowthatthe
electrodeswereplacedonthemiddlephalangesoftheindex affect manipulation was successful in increasing emotional
andmiddlefingersofthenondominanthand,afterthehands stress in the emotional relative to the neutral condition.
werewashedwithwarmtapwaterwithoutsoap.Dataacqui- Second,weconsidertheimpactofemotionalarousalonboth
sitionwascarriedoutwithaNationalInstrumentsanalog-to- information search andstrategy selection.
digital acquisition card (USB-6008) with LabView software
(National Instruments, Inc., Austin, TX). The data were
sampled with 500-Hz sampling rate and stored for offline Affect manipulation
processing.The raw skin conductance signal wasresampled The affect manipulation was effective, as indicated by the
at100HzandsmoothedwithaGaussianlow-passfilterwith scores on the State Anxiety Inventory and arousal and va-
awindowof300milliseconds(Alexanderetal.,2005).Asan lence ratings. For each participant, we calculated the differ-
index of autonomic arousal, we used the frequency of ence in state anxiety (SA) before and after the manipulation
spontaneous skin conductance fluctuations (SF), which is a (ΔSA=SA (cid:1)SA ), and we also calculated average
posttest pretest
popular measure in studies on emotions and stress (Bach, arousal and valence across the whole task. Independent
Friston, & Dolan, 2010; detailed overview in Boucsein, samples t-tests indicated that these three measures differed
2011). It was calculated for each test trial as the number of substantially between the two conditions—ΔSA and arousal
fluctuations per minute (Boucsein, 2011,pp.174–175). were higher (ΔSA: M =1.9 vs. M =8.9,
neutral emotional
p=.002, Cohen’s d=0.83; arousal: M =36.7 vs.
neutral
M =57.6, p<.001, Cohen’s d=1.29), and valence
emotional
Procedure was lower (M =55.1 vs. M =35.9, p<.001,
neutral emotional
The study took place at the University of Basel. After Cohen’sd=1.29) in the experimentalcondition(Table 1).
entering the laboratory, participants signed the informed We also determined the influence of the affect manipula-
consent form and were randomly assigned to the experi- tion on SF frequency, taking into account that individuals
mentalorcontrolconditions.Participantshadskinconduc- differing in trait anxiety might differ in neurophysiological
tance electrodes attached and waited 10minutes to allow reactivity, as has often been reported in the literature (Etkin
for electrode adaptation. Afterwards, participants com- et al., 2004; overview in Boucsein, 2011, pp. 391–409). To
pleted the Trait Anxiety Inventory (Laux, Glanzmann, thisend,weestimatedalinearmodelwithconditionandtrait
Schaffner, & Spielberger, 1981), waited for a 2-minute anxiety as between-subjects factors and SF frequency as the
baseline skin conductance measurement, and then filled dependent variable, using the PROCESS macro for SPSS.
out the State Anxiety Inventory (Laux et al., 1981). This procedure allows for testing direct and indirect effects
Participantsthencompletedthecomputer-basedinference of independent variables (mediation analysis) as well as
taskadaptedfromMata,Schooler,andRieskamp(2007).On testingtheinteraction(i.e.,moderation)effectsofcontinuous
each decision trial, participants could acquire the cuevalues and categorical predictors (Hayes, 2012, 2013). Assuming
in a sequential manner in the descending order of cue that the SF frequency can be associated with baseline and
validities (Figure 1), cue-wise and option-wise. After they meanlevels of skin conductance, weentered these variables
had acquired enough information, they made their choices. in the model as covariates.
Participantsdidnotreceiveinformationonwhethertheirde- The analysis showed the effects of both covariates on SF
cisionwascorrecttoavoidanylearningeffects.Immediately frequency (mean skin conductance level: coefficient=5.85,
beforeeachofthe56decisiontrialsinthetestphase,partic- p<.001, baseline skin conductance level: coefficient=1.15,
ipants saw for 6seconds one aversive (experimental group) p=.048),andnomaineffectoftraitanxiety(coefficient=0.03,
or neutral (control group) slide from the IAPS (Lang et al., p=.540). In addition, the analysis suggested that there might
1998) and rated their subjective arousal and valence on two be a main effect of condition (coefficient=1.22, p=.069)
continuousratingscales(from0to100)thatwerepresented andaninteractioneffectofconditionandtraitanxiety(interac-
sequentially on separate computer screens, by moving a tioneffect:coefficient=0.17,p=.055;Table2),althoughthese
slideralongalineaccompaniedbyiconsoftheSelfAssess- twoeffectsfailedtoreachstatisticalsignificance.Toexamine
ment Manikin (Lang et al., 1998) depicting four possible the interaction of condition and trait anxiety more closely,
levels of arousal or valence. Participants clicked an infor- we computed, using the Johnson–Neyman technique (Hayes,
mation box to observe a cue value for a particular alter- 2012;Johnson& Neyman,1936;Preacher,Curran,&Bauer,
native. The cue value was presented until the participant 2006), the exact value of the trait anxiety score above which
clicked on the next information box. To choose an op- the experimental condition had an effect on SF frequency,
tion, participants clicked on the box where the cue values significant at α=.05. The value of trait anxiety limiting this
for that option were presented. Once the inference task significance region was 36 points. In other words, only for
was completed, participants filled out the State Anxiety participants scoring 36 points or above on the Trait Anxiety
Inventory for a second time, the skin conductance mea- Inventory (41.7% of the sample) did the emotional stress
surementwasterminated,andtheelectrodeswereremoved. manipulationhave a significant effect; thatis,it increasedthe
Finally,adebriefingsessionfollowed,inwhichwethanked SFfrequency,ascomparedwiththecontrolcondition.
and paid participants. The entire procedure took about To further validate the manipulation effectiveness, we
1hour. examined the association between participants’ ratings of
Copyright©2015JohnWiley&Sons,Ltd. J.Behav.Dec.Making(2015)
DOI:10.1002/bdm
S.Wichary et al. Inferences Under EmotionalStress
Figure1.Experimentaldisplay.TheparticipantsclickonaboxtoseetheinformationconcerningCandidateAandobservesthecuevalue5;
afterobservingadditionalinformation,theparticipantschooseCandidateAbyclickingtheappropriatebox.Toobservetheothercuevaluesof
thesamealternative(alternative-wisesearch),theparticipantshavetorepeatedlyclickonthesamebox.Toobservethevaluesofthesamecue
fordifferentalternatives(cue-wisesearch),theparticipantshavetoclicktherespectiveboxesfortheotheralternatives
Table1. Means(andSDs)forthemanipulationchecksbycondition
Measure M (SD) M (SD) t p Cohen’sd
neutral emotional
Stateanxiety(ΔSA) 1.9(7.9) 8.9(8.8) (cid:1)3.32 .002 0.83
Arousal 36.7(17.1) 57.6(15.3) (cid:1)5.19 <.001 1.29
Valence 55.1(14.1) 35.9(15.5) 5.21 <.001 1.29
Note:ΔSA,changeinstateanxietyfrompretesttoposttest;SD,standarddeviation.
Table 2. Model estimates for the effects of condition and trait
anxietyonthefrequencyofskinconductancefluctuations arousal and valence and the SF frequency.For this purpose,
weestimatedageneralizedlinearmixedeffectsmodel.Such
n=59 SFfrequency modelsofferthepossibilityofdeterminingtheeffectsofcon-
Effects Estimate SE t p tinuous predictors and their interactions; they also allow for
multilevel analyses and for modeling of random effects
Intercept 3.60 0.675 5.346 <.001
(e.g., Bolker et al., 2008). The model was estimated using
Condition(ref:Neutral) 1.22 0.661 1.851 .069
restricted maximum likelihood estimation with lme4 and
Traitanxiety 0.03 0.044 0.616 .540
Condition:Traitanxiety 0.17 0.086 1.95 .055 lmerTest packages in the statistical software R (R Develop-
Meanskinconductancelevel 5.85 1.571 3.721 <.001 ment Core Team, 2008). We estimated the model with SF
Baselineskinconductancelevel 1.15 0.568 2.022 .048 frequency as the dependent variable, arousal and valence as
Note:R2=.43. independentfixedeffects,andparticipantasarandomeffect,
SF,spontaneousfluctuationsofskinconductance. assumingrandominterceptsforparticipants.Asintheearlier
Copyright©2015JohnWiley&Sons,Ltd. J.Behav.Dec.Making(2015)
DOI:10.1002/bdm
Journal of Behavioral Decision Making
analysis,weincludedmeanskinconductancelevelandbase- measures were dependent variables. Figure 2 shows that in
line skin conductance level as covariates. theemotionalcondition,informationsearchwasmorefrugal
Table 3 summarizes this model. It shows that SF fre- andmorefocusedonthebestcueduringthewholetask,asin-
quency was positively associated with emotional arousal dicatedbythemaineffectsofconditiononACQ,ENTROPY,
(coefficient=0.009, p=.018). In turn, no association and BEST CUE (ACQ: coefficient=(cid:1)1.62, p<.001;
between SF frequency and emotional valence was observed ENTROPY:coefficient=(cid:1)0.23,p=.024;BESTCUE:coef-
(coefficient=(cid:1)0.0005,p=.900).Asinthepreviousanalysis, ficient=0.09,p=.020).
baselineandmeanskinconductancelevelswerealsoassoci- Additionally, the values of the search variables ACQ and
ated with SF frequency (Table 3). This analysis thus vali- ENTROPYslightlydecreasedoverthe courseofthe task,and
dated SF frequency as an index of emotional arousal for the values of BEST CUE slightly increased, as indicated by
use in subsequent analyses. the main effect of trial on these variables (ACQ:
coefficient=(cid:1)0.06,p<.001;ENTROPY:coefficient=(cid:1)0.003,
p<.001; BEST CUE: coefficient=0.001, p<.001). This
Informationsearch meansthatasthetaskprogressed,participantsmadefewerac-
Theuncertainty-reductionandattention-narrowinghypotheses quisitions and focused the search on the most important cue.
lead to different expectations regarding the role of emotional Thiseffectmightindicatetheinfluenceofaccumulatingmental
stress on predecisional information search. In our analyses, effort or learning; however, the design of the current study
we considered both the amount of information searched and doesnotallowustodistinguishbetweentheseexplanations.
thepatternoftimespentondifferentcues.First,weanalyzed Furthermore, the interaction between condition and trial for
overallinformationsearchbycalculatingthenumberofacqui- all search variables implies that the changes in search that
sitions (ACQ) as the average number of information boxes happenedacrosstrialsweredifferentintheneutralandtheemo-
openedduringa tasktrial.Second,weconsidered the pattern tionalcondition(ACQ:coefficient=0.03,p=.036;ENTROPY:
of attention allocation by calculating the proportion of time coefficient=0.004,p<.001;BESTCUE:coefficient=(cid:1)0.002,
spent on the best cue (BEST CUE), that is, the time spent p<.001; Table 4). Figure 2 shows that in the emotional
looking at the best cue divided by the time spent looking at condition, participants searched information in a frugal and
allcues.Further,wecalculatedthesamemeasureforallcues focused manner rather consistently across the task, whereas in
soastocalculateinformationentropy(ENTROPY): the neutral condition, participants’ information search was less
frugalandlessfocusedatthebeginningofthetaskandbecame
H ¼(cid:1)∑pilog pi (1) morefrugalandfocusedasthetaskprogressed.
i
Theseresultswerefurtherstrengthenedbytheanalysesin-
where p is the proportion of time spent on the ith cue (cf. volvingskinconductance.Becausewemeasuredskinconduc-
i
Jepma&Nieuwenhuis,2011;Sedek,Kofta,&Tyszka,1993, tancecontinuouslyacrossthetask,itcouldbeusedasamore
for the use of entropy in studies on decision making). precisepredictorofsearchthantheexperimentalconditionit-
ENTROPYprovidesinformationaboutthedistributionoftime self. Having validated SF frequency as a measure of arousal,
spentonthecues—ifattentionwasdistributedequallyacross we used it for predicting information search across the task.
allcues(i.e.,ifparticipantsspentanequalamountoftimeon To this end, we estimated three linear mixed effects models
eachcue),entropywouldbemaximal(1.79natsinourstudy, with the search measures ACQ, ENTROPY, and BEST
wheresixcuescouldbesearched);ifattentionallocationwas CUEasdependentvariablesandSFfrequencyasacontinuous
extremelyskewedtoasinglecue(i.e.,ifparticipantsspentall predictor.Becauseearlieranalysesshowedtheeffectofcondi-
theirtimeononecueandnotimeontheremainingcues),then tion and trial, as well as their interaction, on the search mea-
entropy would be minimal (0 nats). In other words, we used sures, we added these as independent predictors to see
ENTROPYasacompoundmeasureoftheallocationofatten- whetherSFfrequencyhasaneffectbeyondtheeffectsofthese
tiontodifferentcuesduringinformationsearch. variables.Asinpreviousanalyses,weassumedrandominter-
Weestimated threelinearmixedeffectsmodelswithtrial ceptsforparticipants.Table5showsthatSFfrequencyhadan
as a within-subject factor, condition as a between-subjects effect on the search measures that was independent of the
factor,theirinteraction,andparticipantastherandomfactor, effectsofcondition,trial,andtheirinteraction.SFfrequencywas
assumingrandominterceptsforparticipants.Thethreesearch negatively associated with ACQ (coefficient=(cid:1)0.11, p<.001)
and ENTROPY (coefficient=(cid:1)0.004, p=.008) and positively
Table3. Modelestimatesfortheeffectsofarousalandvalenceon withBESTCUE(coefficient=0.003,p=.002).IncreaseinSFfre-
thefrequencyofskinconductancefluctuations quency was associated with more frugal information search fo-
cused on the most important cue. In summary, the analyses
n=59 SFfrequency
concerninginformationsearchshowthatarousalmanipulatedby
Fixedeffects Estimate SE t p aversiveslidesandindexedbyskinconductancenarrowsinforma-
Intercept 3.560 0.523 6.805 <.001 tionsearch,whichisseenasadecreasedamountofacquiredinfor-
Arousal 0.009 0.004 2.355 .018 mationandincreasedfocusonthemostimportantinformation.
Valence (cid:1)0.0005 0.004 (cid:1)0.125 .900
Meanskinconductancelevel 0.334 0.044 7.542 <.001
Strategy selection
Baselineskinconductancelevel 11.451 0.306 3.137 .003
Wewentbeyondinformationsearchbyanalyzingparticipants’
Note:SF,spontaneousfluctuationsofskinconductance. choices. First, we determined the percentages of inferences
Copyright©2015JohnWiley&Sons,Ltd. J.Behav.Dec.Making(2015)
DOI:10.1002/bdm
S.Wichary et al. Inferences Under EmotionalStress
Figure2.Searchmeasuresasafunctionoftrialintheemotional(solidline)andneutral(dashedline)conditionsasestimatedfromthelinear
models.(A)Numberofacquisitions,(B)entropy,and(C)theproportionofthetimespentonthebestcue.Theshadeofthebackgroundrep-
resentsthenumberofparticipantswithaparticularvalueonagiventrial
predictedbythetwostrategiesWADDandTTB,acrossallpar- For example, if WADD predicted 70% of a participant’s
ticipants and across all trials. WADD predicted 34% of all choicesandTTBpredicted30%ofthisparticipant’schoices,
choices compared with TTB, which predicted 45% of all thenthisparticipantwasclassifiedasa“WADDuser.”Ifthere
choices. Thus, both strategies did substantially better than was a tie between WADD and TTB, the participant was not
chance(i.e.,25%)inpredictingparticipants’choices,andonav- classified,whichwasthecaseforoneparticipantinthesample.
erage,TTBwasbetterthanWADDinpredictingtheinferences. Moreover,wecomparedthepredictiveperformanceofWADD
Further, we determined the strategy that was best in andTTBwiththepredictiveperformanceofabaselinemodel
predicting each participant’s choices across the whole task. assumingrandomchoices(i.e.,withapredictiveperformance
Table4. Modelestimatesfortheeffectsoftrialandconditiononsearchmeasures
n=65 Acquisitions Entropy Bestcue
Fixedeffects Estimate SE t p Estimate SE t p Estimate SE t p
Intercept 15.9 0.313 50.756 <.001 1.38 0.069 19.906 <.001 0.439 0.028 15.28 <.001
Trial (cid:1)0.06 0.009 (cid:1)5.889 <.001 (cid:1)0.004 0.0005 (cid:1)7.758 <.001 0.001 0.0002 5.62 <.001
Condition(ref:Neutral) (cid:1)1.62 0.446 (cid:1)3.637 <.001 (cid:1)0.23 0.099 (cid:1)2.304 .024 0.090 0.041 2.20 .020
Trial:Condition 0.03 0.014 2.095 .036 0.004 0.0007 5.660 <.001 (cid:1)0.002 0.0003 (cid:1)5.98 <.001
Copyright©2015JohnWiley&Sons,Ltd. J.Behav.Dec.Making(2015)
DOI:10.1002/bdm
Journal of Behavioral Decision Making
Table5. TheeffectsoftrialandSFfrequencyonsearchmeasures
n=59 Acquisitions Entropy Bestcue
Fixedeffects Estimate SE t p Estimate SE t p Estimate SE t p
Intercept 16.522 0.940 17.575 <.001 1.451 0.057 25.216 <.001 0.432 0.0299 14.272 <.001
SFfrequency (cid:1)0.11 0.026 (cid:1)4.355 <.001 (cid:1)0.004 0.002 (cid:1)2.654 .008 0.003 0.0009 3.019 .002
Trial (cid:1)0.05 0.006 (cid:1)8.701 <.001 (cid:1)0.003 0.0004 (cid:1)7.870 <.001 0.001 0.0002 5.355 <.001
Condition(ref:Neutral) (cid:1)1.26 1.349 (cid:1)0.933 .354 (cid:1)0.170 0.082 (cid:1)2.104 .039 0.077 0.042 1.810 .074
Trial:Condition 0.03 0.009 3.531 <.001 0.004 0.0006 6.229 <.001 (cid:1)0.002 0.0003 (cid:1)6.395 <.001
Note:SF,spontaneousfluctuationsofskinconductance.
of34%ofthechoices,asdeterminedbyabinomialtest).We traitanxietyasacovariate,totakeintoaccountthatchangesin
excluded from further analysis those participants for whom stateanxietymightpartlydependontheleveloftraitanxiety.
both WADD and TTB performed worse than the baseline Thisanalysisshowedthatemotionalconditionalonedidnot
model(nineparticipants).Inthissample,thechoicesofthema- haveadirecteffectonstrategyclassification,ln(odds)=(cid:1)0.55,
jority of participants (68.5%) were best described by TTB, p=.449. However, state anxiety had a positive effect on the
whereas the choices of the remaining 31.5% of participants probability of being classified as a TTB user (as opposed to
werebestpredictedbyWADD. being classified as a WADD user), ln(odds)=0.12, p=.030.
Theclassificationofparticipants’inferencestrategieswas Moreover,theanalysissuggeststhattheexperimentalcondition
consistent with their search behavior. Participants who were hadanindirecteffectonstrategyclassificationmediatedbystate
classifiedasbeingbestdescribedbyTTBincomparisonwith anxiety, ln(odds)=0.66, lower limit of the bootstrapped 95%
WADD searched for information in a more frugal and more confidence interval=0.04,andupper limit of the bootstrapped
focused manner. TTB users searched on average for 11.7 95%confidenceinterval=2.33(Table6).Traitanxiety,inturn,
cuevalues(outof24availablecuevalues),ascomparedwith did not have an effect on strategy classification, ln(odds)
WADDusers,whosearchedfor18.2cuevalues,t(52)=5.21, =(cid:1)0.09, p=.150. This conclusion concerning the mediation
p<.001, d=1.44. TTB users also had lower ENTROPY effect holds despite the fact that the experimental condition did
valuesthandidWADDusers(M =1.01vs.M =1.52), not have a direct effect on strategy classification (see Hayes,
TTB WADD
t(52)=5.14, p<.001, d=1.42, and they spent 57% of their 2012,forarguments).Insum,ourresultssuggestthatwhilethere
search time on the best cue, as compared with WADD users was no overall effect of our experimental manipulation on
who spent only 37% of their search time on the best cue, strategy classification, an effect of condition can be observed
t(52)=(cid:1)4.82,p<.001,d=1.34. whenconsideringthatanxietylevelsdifferedbetweenparticipants.
In addition, we examined whether strategy classifications We also aimed to link skin conductance, one measure of
weredifferentfortheneutralandemotionalconditionsofthe arousal, to strategy classification. For this purpose, we
experiment. Here, we observed only small differences, with estimated a binary logistic regression model to predict the
67.9% TTB users and 32.1% WADD users in the neutral classification of participants as either WADD or TTB users
condition, as compared with 69.2% TTB users and 30.8% onthebasisofconditionandskinconductance.Pastresearch
WADD users in the emotional condition. Thus, according (e.g., van den Bos, Harteveld, & Stoop, 2009; Wolf,
to this analysis, on average, the participants selected TTB Schommer, Hellhammer, McEwen, & Kirschbaum, 2001)
only slightly more frequently under emotional arousal than and initial analysis with our data suggested that gender may
intheneutralcondition.However,itmightbethattheselec- moderate the relationship between skin conductance and
tionofadecisionstrategycouldvaryindividuallyandcould
be a function of the individual effect of emotional arousal
Table 6. Model estimates for the direct effects of condition and
manipulation. To take individual differences into account,
stateanxietyonstrategyclassification
weconsideredstrategyclassificationasafunctionofexperi-
mental condition and (i) state anxiety and (ii) skin conduc- n=52 TTBuservs.WADDuserclassification
tance rather than of the experimental condition alone.
Effects Estimate SE z p
Todeterminetheimpactofstateanxietyonstrategyselection,
weestimatedabinarylogisticregressionmodelusingthePRO- Directeffects
Constant (cid:1)0.51 1.666 (cid:1)0.308 .758
CESS macro for SPSS. As already noted, this procedure allows Condition(ref:Neutral) (cid:1)0.55 0.734 (cid:1)0.757 .449
for testing direct and indirect effects of independent variables
Stateanxiety 0.12 0.054 2.160 .030
(mediation analysis), as well as testing the moderation effects Traitanxiety (cid:1)0.09 0.063 (cid:1)1.455 .150
(Hayes, 2012, 2013). To estimate the indirect effects, it uses
bootstrappedconfidenceintervals,whichisamajorimprovement Estimate BootSE BootLLCI BootULCI
Indirecteffect
overtraditionalmethods(Hayes,2012).Anindirecteffectissta-
Condition 0.66 0.535 0.045 2.336
tistically significant when the bootstrapped confidence interval
(throughStateanxiety)
doesnotcontainzero.Weestimatedamodelwiththeexperimen-
tal condition and state anxiety at posttest as predictors and the Note:McFaddenR2=.103.
classification of a participant as a WADD user (codedas 0) or B(uopopteSrE),libmoiottsotfrabpopoetdstrsatpapneddard95e%rrocro,nBfidoeontLceLCinIte(rBvoalo;tUTLTCBI,)t(cid:1)akleowtheer
TTBuser(codedas1)asthedependentvariable.Wealsoadded bestheuristic;WADD,weightedadditivestrategy.
Copyright©2015JohnWiley&Sons,Ltd. J.Behav.Dec.Making(2015)
DOI:10.1002/bdm
S.Wichary et al. Inferences Under EmotionalStress
strategyselection.Therefore,weincludedgenderasapredictor ofourarousalmanipulation,theresultsareoverallsupportive
in the model. We also included the baseline and the mean of the attention-narrowing hypothesis, which suggests that
skin conductance levels as covariates to account for initial high arousal elicited by aversive stimuli is associated with
individual differences that may covary with the frequency higher reliance on simpler decision strategies that focus on
of nonspecific skin conductance fluctuations. the most relevant information.
This analysis showed that emotional condition alone did
not have the effect on strategy classification, ln(odds)
=0.63, p=.361. In turn, SF frequency had an effect on DISCUSSION AND CONCLUSION
strategy classification, ln(odds)=3.23, p=.020, such that
the higher the skin conductance, the higher the probability In the present work, we examined whether emotional stress
of being classified as a TTB user. In addition, there was an affects judgment and decision-making processes. In particu-
interaction effect of gender and skin conductance, ln(odds) lar,weexaminedwhetheremotionalstresselicitedbyaversive
=8.61, p=.025, and no effect of gender alone, ln(odds) picturesinfluencespeople’ssearchforinformationpriortoa
=0.04, p=.959 (Table 7). Figure 3 illustrates the aforemen- decision and their selection of decision strategies. To this
tioned relationship—it shows the probability (computed from end,wemanipulatedemotionalstressandobserveditsimpact
the log odds) of being classified as a TTB user depending ondecisionmakingbyusingapersonnelselectiontask.
onskinconductanceandgender.Althoughtherewasanover- Overall, we found that our manipulation led to reduced
allincreaseintheprobabilityofbeingclassifiedasaTTBuser information search and increased allocation of attention to
withincreasingskinconductance,thiseffectwasmuchmore the most important cue. In addition, our results suggest that
pronouncedformen,ln(odds)=8.97,SE=3.78,p=.021,than emotionalstress,asindicatedbyanxietyscoreandskincon-
forwomen,ln(odds)=0.36,SE=0.81,p=.654. ductance,leadstotheuseofsimplerdecisionstrategies.This
In summary, and even though there were considerable pattern of results favors the attention-narrowing hypothesis,
individual and gender differences that mediated the effects whichsuggeststhathigharousalcanleadtoattentionnarrowing
so as to facilitate concentration onthe problem and increase
the likelihood of success (Derryberry & Tucker, 1994;
Table 7. Model estimates for the effects of SF frequency and Easterbrook,1959;Friedman&Förster,2010).Inturn,these
genderonstrategyclassification
resultsareinconsistentwiththeuncertainty-reductionhypothe-
n=51 TTBuservs.WADDuser sis, which emphasizes how appraisal tendencies regarding
classification uncertainty and control can impact judgment and decision
makingbyleadingtomoreeffortfulandextensiveprocessing
Effects Estimate SE z p
ofinformation(Hanetal.,2007;Lerner&Keltner,2000).
Constant 1.22 0.634 1.92 .06
Condition 0.63 0.691 0.91 .361
SFfrequency 3.23 1.392 2.32 .020
Gender 0.04 0.850 (cid:1)0.05 .959 Skin conductance
SFfrequency:Gender 8.61 3.843 2.24 .025 Toourknowledge,thisisthefirststudythatdemonstratesalink
Meanskinconductancelevel (cid:1)1.09 1.639 (cid:1)0.67 .502 betweenskinconductanceandpredecisionalinformationprocess-
Baselineskinconductancelevel (cid:1)8.23 4.766 (cid:1)1.72 .084 ingandstrategyuseinmultiattributechoice.Previousstudiesthat
Note:McFaddenR2=.121. used skin conductance in decision making were concerned with
SF,spontaneousfluctuationsofskinconductance;TTB,takethebestheuris- risktakingandemployedparadigmssuchastheIowaGambling
tic;WADD,weightedadditivestrategy. Task(Bechara,Damasio,Tranel,&Damasio,1997)ortheColum-
biaCardTask(Figner,Mackinlay,Wilkening,&Weber,2009).
Here,weshowedhowskinconductancecanalsoberelatedtoin-
formationsearchandstrategyuseinaprobabilisticinferencetask.
Skin conductance has been frequently linked to emotions
and stress (Bach et al., 2010; Boucsein, 2011) and is an
established peripheral index of sympathetic arousal, which is
apartoftheadaptivestressresponse.Skinconductanceisthus
a proxy for the central mechanisms regulating this response
(Critchley, 2005; Nieuwenhuis, De Geus, & Aston-Jones,
2011). One particular brain structure that may help elucidate
our results is the LC–norepinephrine system, a brainstem nu-
cleusregulatingarousal(Aston-Jonesetal.,1996)andinforma-
tionprocessing(Aston-Jonesetal.,1999).LC–norepinephrine
activity is anatomically and functionally related to skin con-
ductance (Nieuwenhuis et al., 2011) and is the neurophysio-
logicalmechanismresponsiblefornarrowingattentiontothe
Figure 3. Probability of being classified as a take the best (TTB) most relevant features of the environment (Aston-Jones &
user by spontaneous skin conductance fluctuation (SF) frequency Cohen,2005).Thus,itisapossiblemediatoroftherelation-
andgender shipsobservedinourstudy.
Copyright©2015JohnWiley&Sons,Ltd. J.Behav.Dec.Making(2015)
DOI:10.1002/bdm
Journal of Behavioral Decision Making
Affect, information search,and decision outcomes women’s. Our study suggests that this might be the case in
Previousstudiesanalyzedonlytheimpactofaffectoninforma- decisionmaking,andfurtherstudiesareneededtounderstand
tion search, showing that naturally occurring and manipulated gender differences in the impact of emotional states on deci-
negativeaffectcausesindividualstosearchonlyasubsetofavail- sion making. Further studies on this topic are needed also
ableinformation(Lewinsohn&Mano,1993;Luce,Bettman,& becausethemalesampleinourstudywasrelativelysmall,so
Payne,1997;Mano,1992).Althoughthosestudiesindicatedthat theconclusionsfromouranalysesneedfurthersupport.
informationsearchisaffectedbyarousal,theypointedonlyindi- Anothersourceoftheaforementioneddifferencesmighthave
rectly to the possibility that emotionally distressed decision been emotion regulation strategies aimed at avoiding negative
makersmightbeusingasimpledecisionstrategy. content of the pictures (e.g., attention deployment; Gross &
Inourstudy,wehaveexaminedbothpeople’spredecisional Thompson, 2007, pp. 3–24). A refinement of our study could
search behavior and their final decisions and related them to employ measures to control for attention deployment, for
subjectiveandobjectivemeasuresofarousal.Theresultssup- example,bytrackingparticipants’gazeinresponsetopictures.
porttheconclusionthatpeoplewhoarehighlyarousedsearch Anotherpossibilitywouldbetoemploystrongermanipulations
forlessinformationandfocusonmoreimportantinformation of stress, such as the cold pressor test or Trier Social Stress
as predicted by the simple noncompensatory heuristic TTB. Test, which have been shown to influence decision making
Processandoutcomemeasuresoftendonotleadtoconsistent (Lighthall,Mather,&Gorlick,2009;vandenBosetal.,2009).
results (see Rieskamp & Hoffrage, 1999, 2008, for a discus- Ourresultshelpdistinguishbetweendifferentviewsofhow
sion). One important reason for this is that search measures arousalimpactsdecisionmakingandfavoraninterpretationin
alonecannotunequivocallydistinguishamong differentdeci- which arousal leads to increased focus through attention
sion strategies, thus undermining the use of search measures narrowing.Butwhatarethecognitivemechanismsunderlying
as a means for identifying the underlying decision strategy. such narrowing of attention? Both psychological and neuro-
Therefore, using both search and outcome measures offers a physiological evidence suggests that high-intensity negative
significant methodological improvement, which increases the affect may modulate information processing through reduc-
chances of identifying the strategies underlying choices. In tionsininformationprocessingcapacity.Forexample,anum-
ourstudy,weshowedconsistencybetweenthetwomethods beroffindingssuggestthatstressandintensivenegativeaffect
by providing search characteristics of participants classified narrow attention (Gable & Harmon-Jones, 2010) and reduce
as users of a particular decision strategy. Moreover, we working memory capacity (Derakshan & Eysenck, 1998;
showed that in our study, these two methods of identifying Elzinga & Roelofs, 2005; Oei, Everaerd, Elzinga, Van Well,
the cognitive mechanism lead to similar conclusions with & Bermond, 2006). Indeed, our related work (Hoffmann, von
regardtotheimpactofemotionalstressonprobabilisticinferences. Helversen, & Rieskamp, 2013; Mata et al., 2007) suggests that
limitationsinworkingmemorycapacitymaybeaviableexplana-
tionfordifferencesinstrategyuse.Futurestudiesshouldevaluate
whetheranyimpactofarousaloninformationsearchandstrategy
Affect, arousal, and strategy selection useismediatedbyreductionsinworkingmemoryfunctioningor
On average, we observed no difference in strategy selection moderated by stable individual differences in working memory
between the two experimental conditions. Thus, in contrast capacity.Ourresultsalsosuggestthatthoseindividualsmoresen-
to the attention-narrowing hypothesis, we did not observe sitive to the effects of arousal may be more prone to working
that a larger number of participants selected the simpler memoryreductionsandrelyonlessinformationandsimplerdeci-
TTB heuristic in the emotional stress condition. There are sionstrategies.Afutureavenueforresearchistoconsiderwhether
different explanations for this finding. First, individual dif- orwhensuchchangesleadtochangesindecisionquality.
ferences in responsiveness to the manipulation might have Future research could also address the adaptivity of the
played a role here. The subsequent analyses showed that observed effects. We observed the attention-narrowing
substantial individual differences existed in how people effects in a compensatory environment favoring the use of
responded to the affect manipulation. Only when we took complex strategies such as WADD. In this light, the ob-
these differences into account was it possible to identify the served effects are maladaptive, that is, they lead to the use
associationbetween affective state and strategy selection. of a strategy that is not well fitted to the environment. A
Second,theremightbedifferencesinhowinformationinte- direct extension of our study would be to test the same
gration is buffered from the impact of affect manipulations. effects in a noncompensatory environment that favors the
Onesourceofthesedifferencesmightbeassociatedwithgen- use of noncompensatory strategies. Here a switch to a
der. Our analysis of skin conductance and strategy use indi- simplerstrategysuchasTTBduetoarousalwouldbefavored
catedthatitwasmostlymenwhosestrategyusewasaffected bytheenvironmentandwouldbequiteadaptive.
by high arousal. This resonates well with the gender differ- The present study is limited by being focused on high-
encesinsusceptibilitytostressmanipulationsobservedinstud- arousalversuslow-arousalnegativeaffectivestatesassociated
iesofworkingmemoryfunctioning(Elzinga&Roelofs,2005; withavoidancemotivationandthusismutetoalargeextenton
Wolfetal.,2001).Inthese studies,itismostoftenmenwho thepossibleeffectsofspecificemotions.Otherstudies,notably
respond to stressors with heightened physiological arousal Frey, Hertwig, and Rieskamp (2014) and Scheibehenne and
andwhosecognitiveprocessesareaffectedbystressors.Itsug- vonHelversen(2015),haveanalyzedthisissueinmoredetail.
gests that men’s cognitive processes might be less buffered Our study adds tothisresearchbyfocusing moreonvarying
from the physiological effects of stress, as compared with degreesofarousal,withoutspecifyingthepossibledifferences
Copyright©2015JohnWiley&Sons,Ltd. J.Behav.Dec.Making(2015)
DOI:10.1002/bdm