Decision Making and Emotions

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The influence of discrete emotions onjudgement and decision-making: A meta-analytic reviewAmanda D. Angie a , Shane Connelly a , Ethan P. Waples b & Vykinta Kligyte ca Department of Psychology, University of Oklahoma, Norman, OK, USAb Department of Management, University of Central Oklahoma, Edmond,OK, USAc Development Dimensions International (DDI), Toronto, Ontario, Canada

Version of record first published: 15 Apr 2011.

To cite this article: Amanda D. Angie, Shane Connelly, Ethan P. Waples & Vykinta Kligyte (2011): Theinfluence of discrete emotions on judgement and decision-making: A meta-analytic review, Cognition &Emotion, 25:8, 1393-1422

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The influence of discrete emotions on judgementand decision-making: A meta-analytic review

Amanda D. Angie1, Shane Connelly1, Ethan P. Waples2, and Vykinta Kligyte3

1Department of Psychology, University of Oklahoma, Norman, OK, USA2Department of Management, University of Central Oklahoma, Edmond, OK, USA3Development Dimensions International (DDI), Toronto, Ontario, Canada

During the past three decades, researchers interested in emotions and cognition have attempted tounderstand the relationship that affect and emotions have with cognitive outcomes such asjudgement and decision-making. Recent research has revealed the importance of examining morediscrete emotions, showing that same-valence emotions (e.g., anger and fear) differentially impactjudgement and decision-making outcomes. Narrative reviews of the literature (Lerner & Tiedens,2006; Pham, 2007) have identified some under-researched topics, but provide a limited synthesis offindings. The purpose of this study was to review the research examining the influence of discreteemotions on judgement and decision-making outcomes and provide an assessment of the observedeffects using a meta-analytic approach. Results, overall, show that discrete emotions have moderate tolarge effects on judgement and decision-making outcomes. However, moderator analyses revealeddifferential effects for study-design characteristics and emotion-manipulation characteristics byemotion type. Implications are discussed.

Keywords: Emotions; Judgement; Decision-making; Meta-analysis.

During the past three decades, researchers inter-

ested in emotions and human cognition have

attempted to understand the complex relation-

ship that affect and emotions have with different

cognitive outcomes such as judgement and

decision-making (JDM; Johnson & Tversky,

1983; Loewenstein & Lerner, 2003). Many early

studies on this topic focused on examining the

impact of general positive and negative affective

states on decision-making processes, treating

affect and emotions as unidimensional and

bipolar (e.g., positive/negative or happy/sad)

constructs (Clore, Schwarz, & Conway, 1994;

Forgas, 1995; Raghunathan & Corfman, 2004;

Schwarz, 1990). Isen and colleagues have pro-

duced a corpus of research on the influence of

global positive affect on cognitive outcomes such

as cognitive organisation and flexibility, problem

solving, decision-making, and risk taking (Isen &

Labroo, 2002). Overall, their investigations and

others have shown that affect does play a role,

with positive affect having a facilitative influence

and negative affect demonstrating more mixed

results (Isen, 2001; Martin, Ward, Achee, &

Correspondence should be addressed to: Amanda D. Angie, 247 Tennessee Ave., Alexandria, VA 22305, USA. E-mail:

[email protected]

COGNITION AND EMOTION

2011, 25 (8), 1393�1422

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http://dx.doi.org/10.1080/02699931.2010.550751

Wyer, 1993; Schwarz, Bless, & Bohner, 1991).However, recent research has revealed the impor-tance of examining how discrete emotions influ-ence human cognition, showing that same-valenceemotions (e.g., anger and fear) impact JDM out-comes in different ways (DeSteno, Petty, Wegener,& Rucker, 2000; Garg, Inman, & Mittal, 2005;Lerner & Keltner, 2000; Raghunathan & Trope,2002).

To date, narrative reviews of the emotions anddecision-making literature (Lerner & Tiedens,2006; Pham, 2007) have identified some topics inneed of additional study, but in general haveprovided a limited synthesis of overall findings. Inparticular, these reviews tend to focus on only asmall subset of discrete emotions and thus do notintegrate the variety of JDM studies conductedusing discrete emotions (e.g., economic decision-making, persuasion, likelihood judgements, etc.).The primary purpose of this study was to reviewthe research examining the influence of discreteemotions on JDM outcomes using a meta-analytic approach. It should be noted, however,that due to the range of empirical research andadvancements in theory development in this areathe literature includes a variety of cognitiveoutcomes (e.g., endowment effect, risk judge-ments, choice to buy a product, etc.) as well as awide range of hypotheses attempting to explainthe effects of discrete emotions on JDM (e.g.,cognitive appraisals of dimensions of emotion,depth of processing, activation of active andpassive responses, etc.). This array of outcomesand hypotheses makes it difficult to establish anormative method for assigning positive ornegative values to the effect sizes. Thus, thecurrent meta-analysis uses the absolute values ofthe effect sizes coded from the eligible studies.While this method limits some of the conclusionsthat can be drawn from the results of the meta-analysis (i.e., positive or negative effects ofspecific emotions), it still provides a synthesis ofthe magnitude of effect that discrete emotionshave on JDM and has implications for theoreticaland methodological considerations in future re-search. In addition, research studies investigatingemotions have adopted different methods of

manipulation, measurement, and evaluation oftheir effects on JDM. These methodologicalfactors may play a role in the effects seen acrossvarious studies. Therefore, the secondary purposeof this study was to examine the influence ofpotential moderator variables.

Emotions and cognition

Early circumplex models of emotion (Plutchik,1991; Russell, 1980; Watson & Tellegen, 1985)viewed emotional experience as being comprisedof arousal and valence. This conceptual represen-tation was used as a framework for studyingrelationships of emotions to cognitive and beha-vioural outcomes. However, discrepancies in find-ings began to emerge for cognitive outcomes suchas JDM, pointing to the need for a more fine-grained approach to describing and accounting fordifferences seen within these dimensions. Re-searchers have begun to focus on contrastingdifferent discrete emotions (e.g., fear and happi-ness) in an attempt to better understand theirinfluence on cognitive outcomes.

Discrete emotions. Discrete emotions are consid-ered to be short-lived, intense phenomena thatusually have clear cognitive content that isaccessible to the person experiencing the emotion(Clore et al., 1994). In contrast to affect, discreteemotions are specific feeling states that arise from‘‘stimulus events’’, which refers to both events thathappen and to prevailing situations (Frijda, 1986).The events or situations have attributes thatuniquely trigger the experience of specific emo-tions while the emotions themselves have distinctaction tendencies or behavioural outputs. It is thisemphasis on the cognitive aspects of the experi-ence and expression of discrete emotions thatmakes them particularly relevant to more cogni-tively oriented outcomes.

Much of the research conducted on discreteemotions has focused on contrasting anger andfear, noting that their patterns of appraisaltendencies are opposite from one another onseveral dimensions and therefore each will impact

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JDM outcomes somewhat differently (Lerner &Tiedens, 2006). In particular, anger has been inthe spotlight of a lot of research both within andoutside the emotions literature. Anger has beendescribed as one of the most frequently experi-enced emotions (Averill, 1982), as having uniqueattention-focusing properties for the individualexperiencing it (Solomon, 1990; Tavris, 1989),and as commonly carrying over from past situa-tions (Lerner & Tiedens, 2006). All of theseattributes make anger a particularly influentialemotion.

Several studies focusing on anger and fear haveshown that fearful and angry participantsgive different assessments of the likelihood offuture negative events (e.g., fear activates higherestimates of the likelihood of risky events occur-ring while anger activates the opposite) and makedifferent choices between risky alternatives (e.g.,fearful individuals tend to choose the ‘‘sure thing’’while angry individuals choose the opposite;Lerner & Keltner, 2001). In addition, angryindividuals are more likely to stereotype targets,make heuristically based judgements, and showautomatic prejudice toward an out-group than sador neutral individuals (Bodenhausen, Kramer, &Susser, 1994; Bodenhausen, Sheppard, & Kramer,1994; DeSteno, Dasgupta, Bartlett, & Cajdric,2004).

Likewise, happiness has been shown to havesimilar effects as anger where both angry and happyindividuals estimate a higher probability of positiveevents than negative events (Garg, 2004). In a studycomparing positive and negative emotions, Chuangand Kung (2005) found that individuals experien-cing happiness tend to choose the safe option moreoften than those experiencing sadness. In contrast,the emotion of sadness has been associated withfeelings of loss as well as the tendency to engage inthoughtful and more detail-oriented processing ofcognitive tasks (Garg, 2004; Semmler & Brewer,2002), possibly as a way to avoid thinking about theemotion-eliciting situation (Smith & Ellsworth,1985). As discussed previously, anger tends to leadto more heuristic processing in judgement whereassadness tends to lead to more effortful and detailedprocessing in judgements. Early work by Bless,

Bohner, Schwarz, and Strack (1990) found thatwhen presented with both strong and weak persua-sive messages, happy individuals were equallypersuaded by both strong and weak messageswhereas sad individuals were more persuaded bystrong than weak messages.

In another set of studies, Lerner, Small, andLoewenstein (2004) examined the effects ofsadness and disgust on everyday economic trans-actions. Participants were either endowed with anobject and were asked at what price they would bewilling to sell it back (sell condition), or endowedwith nothing and asked whether they wouldprefer to receive the object or receive a specificcash amount for it (choice condition). Resultsshowed that individuals in the sadness conditiondecreased selling prices and increased choiceprices as opposed to those in the neutral condi-tion. Individuals in the disgust condition reducedboth choice and sell prices. This phenomenon iscalled the ‘‘endowment effect’’ (Kahneman,Knetsch, & Thaler, 1991) and is supported byrobust findings in the economic literature whereindividuals endowed with an object tend to over-value it (i.e., ask for a higher amount to sell;Lerner et al., 2004). However, the influence ofspecific emotions reversed the effect (i.e., thesadness condition) or completely eliminated it(i.e., the disgust condition).

Two general theoretical approaches dominatethis research. One approach views emotion as asystem of discrete categories (Frijda, 1986;Lazarus, 2001). Research within this approachfocuses on discrete emotion states (e.g., anger,fear, sadness) and examines various appraisaldimensions underlying these emotions (e.g.,control, responsibility, certainty, etc.) that dif-ferentiate them (Lazarus, 2001; Ortony, Clore,& Collins, 1988; Roseman, Spindel, & Jose,1990; Smith & Ellsworth, 1985). Studies in-vestigating differences on these dimensionsacross emotions support this idea. For example,Tiedens and Linton (2001) demonstrated thedifferential influence of appraisals of certaintyon judgement tasks, where emotions high onthe dimension of certainty (i.e., anger andcontentment) led to individuals being more

EMOTIONS AND COGNITION META-ANALYSIS

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persuaded by a message than emotions low oncertainty (i.e., worry and surprise). In addition,the emotions of anger and fear carry oppositeappraisal patterns for the dimension of control,where anger is associated with a sense ofindividual control and fear is associated with asense of situational control (Lerner & Keltner,2001). Thus, when asked to attribute negativeevents to specific causal agents, assess past andpresent risk, or choose policies to address socialissues, angry individuals were more likely to citehuman factors, perceive lower levels of past andpresent risk, support more punitive policiestoward social issues, and decrease welfare assis-tance. In contrast, fearful individuals were morelikely to cite situational factors, perceive higherlevels of past and present risk, and prefer moreprotective policies while sad individuals in-creased welfare assistance (Fischhoff, Gonzalez,Lerner, & Small, 2005; Gault & Sabini, 2000;Keltner, Ellsworth, & Edwards, 1993; Lerner,Gonzalez, Small, & Fischhoff, 2003; Nabi,2003; Small & Lerner, 2008). Based on theissues discussed above, several hypotheses weregenerated:

H1a. Comparisons between anger and fear, andcomparisons between anger and sadness willhave moderate to large mean effect sizes, bothfor judgement outcomes and decision-makingoutcomes.

H1b. Comparisons between anger and happi-ness will have small to moderate effect sizes bothfor judgement outcomes and decision-makingoutcomes.

H2. Comparisons between fear and sadness willhave small mean effect sizes, both for judgementoutcomes and decision-making outcomes.

H3a. When compared to a control group, angerwill have moderate mean effect sizes for riskseeking and policy choice.

H3b. When compared to a control group, fearand sadness will have small mean effect sizes forrisk seeking and policy choice.

Another approach examines the underlyingdimensions associated with the generation ofemotions (i.e., direction and intensity; Cacioppo& Gardner, 1999). Research has described thesedimensions in terms of a motivational systemwhere emotions evolve from action tendenciesthat directly reflect activation of aversive orappetitive responses (Lang, 1995; Lang, Bradley,& Cuthbert, 1990, 1992). Specifically, discreteemotions can be associated with decreases inactivation, no change, or increases in activation(Cacioppo, Gardner, & Bernston, 1999). Theoutputs of the evaluative processors comprisingthis system are then combined in order to computepreferences and organise action (Cacioppo &Gardner, 1999). Appetitive tendencies are asso-ciated with more approach responses or a positivitydisposition whereas aversive tendencies are asso-ciated with more withdrawal responses or anegativity disposition. This concept of activationis similar to the idea of action readiness in theappraisal approach in that emotions shape thepreparation or impulse to respond in some formto reach a goal (Frijda, 1986; Roseman, 1984). Inother words, high levels of activation may motivatepreferences and actions, ultimately impacting out-comes to a greater degree than low levels ofactivation (Waples & Connelly, 2008).

It is suggested that, based on research in thisarea, the experience of certain emotions results inmore passive or active types of responses thatgo beyond increased states of internal arousal(Connelly, Gaddis, & Helton-Fauth, 2002). Forexample, emotions like anger, which arise fromevents or people threatening/thwarting one’sefforts with negative outcomes for the self/others,motivate a person to take some action against thecausal agent (i.e., high level of activation). An-other example would be the emotion of fear,which leads to escape, serving the function ofprotection (Seitz, Lord, & Taylor, 2007). Onthe other hand, emotions like disappointment,which arise from events resulting in personal loss,lead to withdrawal from the situation or otherpeople (i.e., low level of activation). Therefore,some types of events or situations may have astronger probability than others of triggering

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certain emotions (Connelly et al., 2002). In turn,these emotions may have a stronger probability ofleading to certain outcomes or consequences.

H4a. When compared to a control group, angerand fear will have moderate mean effect sizes, bothfor judgement outcomes and decision-makingoutcomes.

H4b. When compared to a control group, sadnesswill have small mean effect sizes, both for judge-ment outcomes and decision-making outcomes.

Potential moderators of emotional influence

In addition to the discrete emotion types, thereare also some key potential methodological mod-erators that may help to further explain therelationship between emotions and JDM (Parrott& Hertel, 1999). These factors include: (1) type ofcognitive task; (2) emotion-manipulation charac-teristics; and (3) study-design characteristics.

Type of cognitive task. The type of cognitive taskmay have implications for differences in theobserved effects of emotions and includes thedistinction between the JDM criteria. Judge-ments, in general, are often comprised of attri-butes that need to be combined into a singlerating such as estimates about some object(s) ofinterest (Shafir & LeBoeuf, 2002). These caninclude estimates of intuitive probabilities, like-lihoods (Kahneman, Slovic, & Tversky, 1982) orthe accuracy of predictions of future events(Dunning, Griffin, Milojkovic, & Ross, 1990;Pulford & Colman, 1996). Studies have consis-tently shown that individuals are not good atweighting attributes within situations and thus arenot good at combining these attributes to make afinal judgement (Shafir & LeBoeuf, 2002). It isthis combination of fallible intuitions and un-certainty that leaves room for the influence ofdiscrete emotions.

Decision-making is made up of preferencesthat depend on the subjective utilities of antici-pated outcomes that are weighted, overall, bytheir probabilities (Shafir & LeBoeuf, 2002). In

general, individuals tend not to have clear andwell-organised preferences. Rather, these prefer-ences are assembled during the decision-makingprocess and this assembly is heavily influenced bythe nature and context of the decision. Researchin this area has uncovered that psychologicalcharacteristics of decision makers, including dis-crete emotions, do influence outcomes (Bell,1982; Hsee, 1996; Raghunathan & Pham, 1999;Shafir, 1993, 1995; Slovic & Lichtenstein, 1983;Tversky & Shafir, 1992; Tversky, Slovic, &Kahneman, 1990).

Observed differences among these two types ofcognitive tasks (i.e., judgement and decision-making) may be related to the different ways inwhich individuals combine desires (i.e., utilities,goals, personal values) and beliefs (i.e., expecta-tions, knowledge, means) to choose a course ofaction (Hastie, 2001). For this reason, they will beanalysed separately.

Emotion-manipulation characteristics. This factoris comprised of study-design characteristics thatare directly related to the emotional componentsof the studies. First, the setting in which theemotion is manipulated can have implications forthe influence of those emotions on subsequentcognitive outcomes. In particular, those partici-pants who received an emotion induction in agroup setting as opposed to alone may besusceptible to, for example, additional factorssuch as contextual influences or attentional biasesleading to smaller observed effects.

Second, the relatedness of the emotion manip-ulation to the cognitive outcome (i.e., incidentaland integral) can also exert some influence.Incidental emotional states are those whose sourceis unrelated to the object of judgement or decisionand include current emotions not caused by thetarget object, pre-existing mood states, and en-during emotional dispositions such as chronicanxiety (Bodenhausen, 1993; Loewenstein &Lerner, 2003; Pham, 2007). In some instancesincidental emotion may be strong enough to focusand direct thought on tasks unrelated to initialemotion triggers (Lerner & Tiedens, 2006). Incontrast, integral emotional responses are those



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experienced in relation to the object of judgementor decision (i.e., emotions and feelings that areelicited by features of the target object). It hasbeen demonstrated that when cognition is emo-tion related it may interrupt pre-existing cognitiveprocesses and direct one’s attention and judge-ment to address the emotion-eliciting event(Johnson-Laird & Oatley, 1992; Lazarus, 1991,Schwarz, 1990).

Third, a variety of techniques are used to induceemotions in participants. However, there areseveral ways in which different induction methodsmay impact outcomes. In particular, one concern isthe duration of the resulting mood or emotion,which under some conditions may be relativelybrief (Chartier & Ranieri, 1989). Another concernis the generalisability of the induced emotionalstate in terms of its correspondence to everydayexperience (Ellis & Ashbrook, 1988). It is alsoimportant to note that the emotion induction itselfmay influence participants to perform cognitivetasks in particular ways resulting in differentialresults among studies (Parrott & Hertel, 1999). Inthis case, differences in effect sizes may beobserved across induction methods.

Lastly, research that incorporates emotionalstates into the design requires that some mea-surement of the level at which participants areexperiencing that emotion is made. Whether ornot studies employ these manipulation checks canhave implications for the strength and quality ofthose inductions as well as for the internalvalidity of the study in general (Parrott & Hertel,1999).

Study-design characteristics. The design featuresof the study may have an important impact onemotional influences (Parrott & Hertel, 1999).For example, studies in which the cognitive taskis administered in a group setting versus anindividual setting may have implications for theway in which emotions continue to influencetask performance (i.e., the stability of the effect incontext). In addition, the nature of thesample, whether it be undergraduates, communityvolunteers, predominantly male, or predomi-nantly female may have implications for the

generalisability of findings outside of thesepopulations. Another potentially important vari-able is the format of the measure (i.e., multiplechoice versus open-ended), which may introducebias or demand characteristics into the assessmentof performance (Parrott & Hertel, 1999) leadingto differences. Research has shown that evalua-tions of prospects are better when they are madein the context of alternative courses of actionrather than in isolation (Hsee, Loewenstein,Blout, & Bazerman, 1999; Read, Loewenstein,& Rabin, 1999).

Lastly, a design characteristic that may not beconsidered a moderator as such but that has beenidentified as a general issue in emotions researchis the use of a control condition as a comparisongroup (Parrott & Hertel, 1999). There is a fairlyeven split across the emotions and JDM litera-ture as to the number of studies that employcontrol conditions as part of their experimentaldesign. In terms of the analytic aspects of thecurrent study, this discrepancy in study designrequires that the studies comparing emotiongroups to control groups be analysed separatelyfrom the studies comparing only emotion groups.Based on the issues raised in the precedingsection, this study addressed the following re-search questions:

R1. How do emotion-manipulation character-istics moderate the influence of discrete emotionson JDM tasks? For example, will observed effectsizes vary based on the induction method forstudies that induce sadness? It should be notedthat because we expected these characteristics todiffer as a function of the discrete emotion, webroke down these moderators by emotion type.

R2. How do study-design characteristics mod-erate the influence of discrete emotions on JDMtasks? For example, will observed effect sizes varybased on the composition of the sample (i.e.,undergraduates, community volunteers, etc.) forstudies that induce anger? It should be noted thatthese moderators were also broken down byemotion type.

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METHOD

Literature search

To meta-analyse the relationship between discreteemotions and JDM outcomes, an extensive litera-ture search was conducted. First, an initial manualsearch was done on prior issues of the followingjournals because of their focus on publishingresearch on discrete emotions and cognitive out-comes: Emotion, Cognition and Emotion, Organi-zational Behavior and Human Decision Processes,and Judgement and Decision-Making. Second, amore complete review of relevant databases wasconducted. This search consisted of an examina-tion of PsycInfo, PsycArticles, Academic Search Elite,Communication and Mass Media Complete, Com-munication Abstracts, Lexis Nexis, ERIC, SocIN-DEX, Business Source Elite, EconLit, and ABI/Inform using the key words emotions, discreteemotions, affect, decision making (or decision-making), judg(e)ment, cognition, and risk. Thesesand dissertations that examined discrete emotionsand JDM were identified through a search ofDissertation Abstracts. Third, the reference sectionsof previously located articles and any relevantreview articles were physically examined by theauthors to identify additional studies that metthe inclusion criteria but were not identified in theinitial database searches.

When locating studies for inclusion in meta-analytic reviews, the ‘‘file drawer problem’’, whichrefers to the potential bias of meta-analyticresults due to non-significant studies not beingpublished is an important issue to address(Rosenthal, 1979; Rosenthal & DiMatteo,2001). Because published studies on averagehave been shown to have larger mean effect sizes(Lipsey & Wilson, 1993) than unpublishedstudies, two steps were taken to obtain additionaldata. First, an announcement soliciting anyunpublished data relevant to the current studywas posted on the listserv of an academic groupcomprised of those individuals who conductemotions research or are interested in emotionsresearch (e.g., EMONET). Second, a manualsearch of published abstracts and proceedingsfrom several relevant annual conferences was

conducted that included the Society for IndustrialOrganizational Psychology, Academy of Manage-ment, Association for Psychological Science, AmericanPsychological Association, and Society for Judgementand Decision-Making. No restrictions for inclu-sion were placed on the year of publication foreither the database search or for conferenceproceedings (i.e., all years for which studieswere available on emotions and cognition wereexamined and considered for inclusion). Throughthe literature search and solicitation of unpub-lished studies via listserv and conference proceed-ings, 240 studies were identified as candidates forinclusion in the meta-analysis.

Inclusion and exclusion criteria for relevantstudies

In order to address the research questions posedpreviously, four inclusion criteria were developedto identify the most relevant studies. First, thestudy was required to focus expressly on theinfluence of discrete emotions (e.g., anger, fear,happiness, etc.) on JDM. Thus, studies thatexamined affect (e.g., global positive or negativeorientation of mood or disposition, mixed emo-tions, etc.) or did not examine discrete emotionsdirectly were not considered. Second, the studywas required to employ an experimental designwhere discrete emotions were manipulated andemotion groups were compared on some judge-ment and/or decision-making task. Third, thejudgement and/or decision-making task(s) ofinterest in the study had to be measured followinga manipulation of discrete emotions. Fourth, therelevant article was required to be available inEnglish and provide either the descriptive (i.e.,means and standard deviations) or inferential (i.e.,F, t, x2) statistics needed to calculate Cohen’s dstatistic. Studies providing only global summariesof findings were eliminated.

In addition, studies that did not use a controlcomparison group but compared two discreteemotions were retained for a separate analysisfrom the studies comparing a discrete emotiongroup and a control group. Citations for thesestudies are provided in the reference list where they



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are indicated by an asterisk (*). Studies that did notreport sufficient numerical data (e.g., no standarddeviations) but met the remaining inclusion criteriawere not eliminated immediately. The authors ofthese articles were each contacted individually toobtain additional information and the studies wereincluded if the requested data was submitted.Electronic-mail addresses were obtained from thearticles’ contact information, authors’ academicinstitution’s web directory, or from a Googlesearch. The first authors of 13 articles werecontacted. Of those authors who were contacted,2 (15%) provided usable data, 5 (39%) did notrespond, and 6 (46%) could not access their dataanymore. Application of these criteria yielded 61effect sizes drawn from 31 empirical studiescontaining 4,864 participants for the treatment�control sample and 52 effect sizes drawn from 45empirical studies containing 5,786 participants forthe treatment�treatment sample.

Content coding procedures

To examine the influence of discrete emotions,emotion-manipulation characteristics, and study-design characteristics on JDM, a content analysis ofall studies was conducted. Three psychologistsfamiliar with the emotions and JDM literaturecoded the studies. Each coder received approxi-mately 20 hours of training in coding proceduresand the variable set to be coded and were instructedto only provide a rating if the material was explicitlydiscussed in the study, or could be reasonablyinferred from the information provided. In specificcases where there was not enough informationavailable, coders were instructed to provide amissing data code. Any discrepancies in ratingswere resolved through consensus. To demonstratethe accuracy of coders prior to consensus, agree-ment analyses were conducted on the codingdimensions. Because categorical decisions weremade for all of the dimensions coded, percentagreement was utilised to assess reliability (Arthur,Bennett, & Huffcutt, 2001; Cooper & Hedges,1994). Overall, the average agreement was high(90%).

Description of variables

The variables coded in the meta-analysis consistedof the cognitive task used in the study, and anumber of potential moderators of JDM effectsizes including: (1) type of cognitive task; (2)emotion-manipulation characteristics; and (3)study-design characteristics.

Judgement and decision-making tasks. Based on areview of the emotions and JDM literature, thecognitive tasks used in these studies were groupedinto two general rubrics: (1) judgement outcomes(e.g., risk perceptions, information assessment,and social perceptions); and (2) decision-makingoutcomes (e.g., risk-seeking behaviour, risk-aver-sion behaviour, policy choice, and consumerbehaviour). These criteria were collapsed intothese two broad categories because of the limitednumber of effect sizes available for analysis.

Emotion-manipulation characteristics. The infor-mation coded in this section included study-designvariables that were specifically associated with theemotions under investigation and included: (1)whether the emotion manipulation was taskrelated or task unrelated (i.e., integral vs. inciden-tal); (2) what manipulation procedures were used;(3) the setting in which the emotion manipulationtook place; and (4) information regarding the useof an emotion-manipulation check and whether itwas significant.

Study-design characteristics. These characteristicsconsisted of both methodological and individual-differences types of moderators. Specifically, themethodological moderators included: (1) fundingstatus of the study (e.g., no or yes, if yes private,federal, or internal); (2) the setting in which thecognitive tasks were administered (i.e., individualvs. group); (3) cognitive task measure type (e.g.,objective or subjective); and (4) cognitive taskmeasure format (e.g., multiple choice or openended). The individual differences moderatorsexamined the individual attributes of the sample.These variables included: (1) nature of sample (e.g.,undergraduates, MBA students, grad students,

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professionals, community volunteers, and mixed);and (2) gender make-up (e.g., predominantly male,predominantly female, and equal).

Computation of effect-size estimates

The present study used Cohen’s d statistic as thecommon effect-size metric. Forty (35%) of the 113data points were computed using means andstandard deviations or test statistics (i.e., t statistics,F statistics, x2) from formulas recommended byArthur et al. (2001) and Lipsey and Wilson (2001).The remaining 73 (65%) data points were com-puted from proportion values (e.g., the proportionof individuals who chose a risky option vs. thosewho chose a neutral option) by calculating an odds-ratio statistic using an arcsine transformation toadjust for the dichotomisation (i.e., the differencebetween the arcsine transformed treatment groupproportion and the arcsine transformed controlgroup proportion) as recommended by Lipsey andWilson (2001). This method of calculating dresults in a more conservative effect-size estimatethan other comparable transformations (e.g., logitand probit) and is approximately equivalent to thestandardised mean difference (Lipsey & Wilson,2001). It should be noted that absolute values werecomputed for each of the mean effect sizes becauseno normative rules exist for determining whetheror not certain judgements or decisions are ‘‘better’’or ‘‘worse’’. The mean effect sizes used in theanalysis represent only the magnitude of differencebetween a discrete emotion group and a controlgroup or two discrete emotion groups. All dcalculations were conducted by the first author.To establish reliability, half of the studies wererecoded by the first author resulting in a percentagreement of 92%.

Prior to these calculations, though, the inde-pendence of data points was considered. It wasfirst determined if the effect size computed wasindependent of other effect sizes produced fromthe same dataset. Thus, if a study measured bothprobability estimates of risk and actual risk choicesand the effect sizes were computed for bothcognitive tasks from the same dataset, they wereconsidered independent. Second, it was then

determined if the effect sizes from each studyrepresented one construct or multiple constructs.For example, if a study reported multiple effectsfor choice behaviour within the same emotioncomparison, such as two assessments of risk takingfor the comparison of anger and a control group,these effects were combined to avoid problemscaused by data dependency.

Data analyses

Preliminary analyses. Sample-weighted mean ef-fect sizes were calculated using random-effectprocedures recommended by Arthur et al. (2001)based on the meta-analytic approach by Hunterand Schmidt (1990). This approach allows forstatistical artefacts, such as sampling error andunreliability, to be corrected. Confidence intervalswere also calculated to provide an assessment of theaccuracy of the mean effect sizes (Arthur et al.,2001). Finally, an analysis was conducted todetermine if publication bias may have influencedthe magnitude of the results. Because of the upwardbias of effect sizes in published studies versusunpublished studies (Lipsey & Wilson, 1993) andbecause published studies are easier to obtainthan unpublished studies, it is important formeta-analysts to investigate this phenomenon(Rosenthal, 1979). To check for possible bias,two steps were taken. A funnel plot of the effectsizes (x-axis) by the number of participants perstudy (y-axis) was generated and inspected, and asystematic examination of funnel plot asymmetrywas conducted using linear regression (Egger,Smith, Schneider, & Minder, 1997). It should benoted that because the absolute values of the meaneffect sizes were used in the analysis, a funnel plotof the effect sizes could not be calculated withoutnegative values. Thus, each effect size was meancentred (the mean of all effect sizes was subtractedfrom each individual effect size) and then includedin the two analyses.

Moderator analyses. The decision to test formoderator variables can either be empiricallydriven, where the presence of one or moremoderators is suspected when sufficient variance



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remains in the corrected effect size, or theoreti-cally driven. In this particular study, the decisionto test for moderators was empirically driven. Thehomogeneity statistic, Q, and percentage ofvariance accounted for by sampling error werecalculated. The Q statistic assesses the extent towhich there is variance beyond sampling errorvariance and when significant, indicates a lack ofhomogeneity (Hunter & Schmidt, 1990, 2004).However, the homogeneity statistic tends to havelow power unless the moderator effect is very largeso the 75% rule-of-thumb method suggested byHunter and Schmidt (2004) was also employedwhen the Q statistic was significant. This methodstates that if 75% or more of the variance can beaccounted for by sampling error, then the conclu-sion may be reached that all of the variance in theobserved effects is due to artefacts (Arthur et al.,2001; Hunter & Schmidt, 1990).

For moderator analyses, it is suggested that aminimum number of cases (i.e., k�10) berequired for analysis and interpretation of results(Hunter & Schmidt, 2004). The decision wasmade, however, to be as inclusive as possibleallowing for the minimum number of cases to beset at two. While still consistent with Arthuret al.’s (2001) recommendations, an importantcaveat that should be noted is that meta-analyticprocedures based on too few cases are relativelyunstable and results should be interpreted withcaution (Rosenthal, 1991, 1995).

RESULTS

Overall effects

Before describing the results regarding the effectsof discrete emotions on JDM outcomes, character-istics of the studies included in the meta-analysisare provided. Tables 1 and 2, in alphabetical order,report qualitative descriptions of study content forthe treatment�control and treatment�treatmentsamples, respectively: (1) author(s), year of pub-lication (and study number if applicable); (2)number of participants (n); (3) the emotion-induction procedure used; (4) type of emotiongroups compared and which group had a higher

level on the cognitive outcome (denoted by anasterisk *); (5) a brief description of the cognitivetask and whether it was a judgement (j) or decision-making (dm) task; and (6) the correspondingabsolute value of the unweighted effect-size esti-mate (d).

An examination of the effect sizes presentedin the summary tables shows some notablepatterns. In terms of the judgement outcomesfor both samples, the larger effect sizes areobserved for judging the likelihood of futureevents (i.e., risk perceptions) and indicatingpreference for different types of information orpreference for desirable tasks (i.e., informationassessments). Only a few opposing effects areseen for the treatment�control sample where inone study the anger group had indicated a higherunfavourable attitude toward an out-group targetand in another study the control group indicateda higher unfavourable attitude toward an out-group target. In terms of the decision-makingoutcomes for both samples, many of the largereffect sizes were seen for risk-taking behaviour(i.e., risk seeking and risk aversion) and con-sumer behaviour (i.e., choosing consumer pro-ducts or setting buying/selling prices on objects).Again, a few opposing effects are seen for thetreatment�control sample where in one study thesadness group was less risk averse than the controlgroup but in another study the control group wasmore risk seeking. In general, mean effect sizeswere small to moderate.

The results of the meta-analysis for the JDMcriteria for the treatment�control samples byemotion group comparison are presented inTable 3. Each table contains: (1) the number ofdata points for each effect-size estimate (k); (2)the number of participants (N); (3) the absolutevalues of the average sample-weighted Cohen’s d;(4) standard deviations of effect-size estimates(SD); (5) standard errors of effect-size estimates(SE); (6) percent of variance accounted for bysampling error (PVA); (7�8) 95% upper (U) andlower (L) bound confidence intervals (CI); and(9) the Q statistic (Hunter & Schmidt, 1990).

Cohen’s (1988) interpretation of effect-sizemagnitude was used to interpret the results (i.e.,

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Table 1. Study information and effect sizes for treatment�control sample

Study n

Emotion

induction Comparison groups Dependent variable

Effect

size (d)

Bless et al., 1996 (Study

2)

41 Film clips Sadness vs. control* Recognition of typical items j 0.10

Bless et al., 1996 (Study

2)

41 Film clips Happiness* vs.

control

Recognition of typical items j 0.05

Cryder et al., 2008 33 Film clips Sadness* vs. control Buying price for an arbitrary productdm 1.40

DeSteno, Dasgupta et al.,

2004 (Study 1)

43 Recall an

emotional

event

Anger* vs. control Unfavourable attitude toward out-group target j 0.01


2004 (Study 1)

44 Recall an

emotional

event

Sadness vs. control* Unfavourable attitude toward out-group target j 0.07


2004 (Study 2)

40 Recall an

emotional

event

Anger vs. control* Unfavourable attitude toward out-group target j 0.03


2004 (Study 2)

41 Recall an

emotional

event

Sadness vs. control* Unfavourable attitude toward out-group target j 0.03

DeSteno, Petty et al.,

2004 (Study 1)

69 Read an

article

Sadness vs. control* Favourable attitude toward emotion-congruent

message j0.67

DeSteno, Petty et al.,

2004 (Study 1)

69 Read an

article

Sadness* vs. control Intention to vote for an emotion-congruent

proposaldm0.68

Fessler et al., 2004 79 Recall of

emotional

event

Anger* vs. control Risk seekingdm 0.06

Fessler et al., 2004 79 Recall of

emotional

event

Disgust vs. control* Risk seekingdm 0.03

Gangemi & Mancini,

2007 (Study 1)

99 Recall of

emotional

event

Guilt* vs. control Choice to buy a cardm 0.84

Gangemi & Mancini,

2007 (Study 1)

106 Recall of

emotional

event

Anger vs. control Choice to buy a cardm 0.00

Gangemi & Mancini,

2007 (Study 2)

113 Recall of

emotional

event

Guilt* vs. control Choice to repair an old cardm 1.45

Gangemi & Mancini,

2007 (Study 2)

114 Recall of

emotional

event

Anger* vs. control Choice to repair an old cardm 0.03

Gangemi & Mancini,

2007 (Study 3)

121 Recall of

emotional

event

Guilt* vs. control Choice of medical professionaldm 0.66

Gangemi & Mancini,

2007 (Study 3)

123 Recall of

emotional

event

Anger vs. control* Choice of medical professionaldm 0.24

Garg, 2004 (Study 1) 69 Recall of

emotional

event

Happiness* vs.

control

Judgements of the likelihood of life events j 0.01

(continued)



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Table 1. (Continued)

Study n

Emotion


Effect

size (d)


emotional

event

Anger vs. control Judgements of the likelihood of life events j 0.00


emotional

event

Happiness* vs.

control

Judgements of the likelihood of life events j 0.03


emotional

event

Anger vs. control Judgements of the likelihood of life events j 0.00

Garg et al., 2005 115 Recall of

emotional

event

Anger vs. control* Choice of the status quodm 0.49

Garg et al., 2005 123 Recall of

emotional

event

Sadness vs. control* Choice of the status quodm 0.31

Harle & Sanfey, 2007 79 Film clips Sadness vs. control* Choice of monetary offerdm 0.30

Innes-Ker & Niedenthal,

2002 (Study 2)

61 Film clips

and music

Sadness* vs. control Judgements of the similarity of words j 0.16


2002 (Study 2)

61 Film clips

and music

Happiness* vs.

control

Judgements of the similarity of words j 0.18

Isbell et al., 2005 46 Recall of

emotional

event

Happiness* vs.

control

Information preference j 0.76

Leith & Baumeister,

1996 (Study 3)

25 Recall of

emotional

event


Leith & Baumeister,

1996 (Study 4)

25 Recall of

emotional

event


Leith & Baumeister,

1996 (Study 5)

22 Film clips Sadness vs. control Risk seekingdm 0.00

Lerner et al., 2004 133 Film clips Sadness vs. control* Choice of selling price for a productdm 0.51

Lerner et al., 2004 113 Film clips Disgust vs. control* Choice of selling price for a productdm 0.59

Liersch et al., 2007

(Study 4)

64 Recall of

emotional

event

Sadness vs. control* Risk aversiondm 0.14

Liersch et al., 2007

(Study 4)

64 Recall of

emotional

event

Happiness vs.

control*

Risk aversiondm 0.38

Maner & Gerend, 2007

(Study 3)

112 Film clips Fear vs. control* Judgements of the likelihood of positive events j 0.02

Nabi, 2002 155 Read an

article

Anger vs. control* Perceived argument strength j 0.01

Nabi, 2002 153 Read an

article

Fear* vs. control Perceived argument strength j 0.03

Nabi, 2003 107 Rate emotion

words

Anger* vs. control Choice of retributive solutionsdm 0.22


words

Fear vs. control* Choice of retributive solutionsdm 0.02

(continued)

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Study n

Emotion


Effect

size (d)


words

Anger vs. control* Judgements of societal causes j 0.24


words

Fear* vs. control Judgements of societal causes j 0.19

Raghunathan &

Corfman, 2004

(Study 1)

62 Scenarios Fear vs. control* Preference for completing an enjoyable task j 0.35

Raghunathan &

Corfman, 2004

(Study 1)

62 Scenarios Sadness* vs. control Preference for completing an enjoyable task j 0.44

Raghunathan &

Corfman, 2004

(Study 1)

62 Scenarios Fear vs. control* Choice of completing an enjoyable task firstdm 0.52

Raghunathan &

Corfman, 2004

(Study 1)

62 Scenarios Sadness* vs. control Choice of completing an enjoyable task firstdm 0.28

Raghunathan & Pham,

1999 (Study 1)

56 Scenarios Fear vs. control* Preference for low probability/high payoff

gamble j0.06

Raghunathan & Pham,

1999 (Study 1)

58 Scenarios Sadness* vs. control Preference for low probability/high payoff

gamble j0.06

Raghunathan & Pham,

1999 (Study 1)

56 Scenarios Fear vs. control* Choice of low probability/high payoff gambledm 0.30

Raghunathan & Pham,

1999 (Study 1)

58 Scenarios Sadness* vs. control Choice of low probability/high payoff gambledm 0.14

Raghunathan & Pham,

1999 (Study 2)

50 Scenarios Fear vs. control* Preference for high pay/low job-security job j 0.13

Raghunathan & Pham,

1999 (Study 2)

48 Scenarios Sadness* vs. control Preference for high pay/low job-security job j 0.07

Raghunathan & Pham,

1999 (Study 2)

50 Scenarios Fear vs. control* Choice of high pay/low job-security jobdm 0.49

Raghunathan & Pham,

1999 (Study 2)

48 Scenarios Sadness* vs. control Choice of high pay/low job-security jobdm 0.47

Raghunathan et al., 2006

(Study 1)

99 Scenarios Fear vs. control* Choice of a comforting gamedm 0.07


(Study 1)

99 Scenarios Sadness* vs. control Choice of a comforting gamedm 0.03


(Study 2)

109 Scenarios Fear vs. control* Choice of a prescription drugdm 0.06


(Study 2)

109 Scenarios Sadness* vs. control Choice of a prescription drugdm 0.07

Semmler & Brewer, 2002 102 Tape-recorded

scenario

Sadness vs. control Preference for a guilty verdict j 0.00

Small & Lerner, 2008

(Study 1)

76 Recall of

emotional

event

Anger* vs. control Choice to decrease assistancedm 0.49

Small & Lerner, 2008

(Study 1)

76 Recall of

emotional

event

Sadness* vs. control Choice to increase assistancedm 0.47

Notes: n�total sample size; *denotes which comparison group had a higher level on the dependent variable; j�judgement task;dm�decision-making task; d�the absolute value of the unweighted effect-size estimate.



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Table 2. Study information and effect sizes for treatment�treatment sample

Study n Emotion induction Comparison groups Dependent variable

Effect

size (d)

Bless et al., 1996

(Study 1)

82 Scenarios Sadness vs.

happiness*


Bless et al., 1996

(Study 2)

40 Film clips Sadness vs.

happiness*


Bless et al., 1996

(Study 3)

80 Film clips Sadness vs.

happiness*


Brinol et al., 2007

(Study 1)

92 Recall of emotional event Sadness vs.

happiness*

Favourable attitude toward a

policy proposal j0.01

Brinol et al., 2007

(Study 2)

89 Recall of emotional event Sadness* vs.

happiness



Brinol et al., 2007

(Study 3)

79 Scenarios Sadness vs.

happiness*



Chua, 2006 31 Gambling task Regret* vs.

disappointment

Risk seekingdm 0.07

Chuang & Kung, 2005 104 Read a story Sadness vs.

happiness*

Choice of a safe optiondm 0.40

Chuang & Lin, 2007

(Study 1)


happiness*

Choice of a safe optiondm 0.24

Chuang & Lin, 2007

(Study 2)


happiness

Choice of the status quodm 0.26

Chuang & Lin, 2007

(Study 3)


happiness

Choice switchingdm 0.27

Chuang & Lin, 2007

(Study 4)


happiness

Choice of a productdm 0.40

Chuang & Lin, 2007

(Study 5)


happiness*

Choice of the compromise

optiondm0.34


2004 (Study 1)

58 Recall of emotional event Anger* vs. sadness Unfavourable attitude toward

out-group target j0.06


2004 (Study 2)

54 Recall of emotional event Anger vs. sadness Unfavourable attitude toward

out-group target j0.00

Gangemi & Mancini,

2007 (Study 1)

105 Recall of emotional event Anger vs. guilt* Choice to buy a cardm 0.85

Gangemi & Mancini,

2007 (Study 2)

115 Recall of emotional event Anger vs. guilt* Choice to repair an old cardm 1.42

Gangemi & Mancini,

2007 (Study 3)

121 Recall of emotional event Anger vs. guilt* Choice of medical professionaldm 0.90

Garg, 2004 (Study 1) 70 Recall of emotional event Anger vs. happiness Judgements of the likelihood of

life events j0.00

Garg, 2004 (Study 2) 88 Recall of emotional event Anger* vs. happiness Judgements of the likelihood of

life events j0.03

Garg et al., 2005 118 Recall of emotional event Anger vs. sadness* Choice of the status quodm 0.18


2002 (Study 2)

62 Film clips and music Sadness vs.

happiness*

Judgements of the similarity of

words j0.02

Isbell, 2004

(Study 2) 157 Recall of emotional event Sadness vs.

happiness*


Isbell et al., 2005 68 Recall of emotional event Sadness vs.

happiness*


Keller et al., 2003

(Study 1)


happiness


(continued)

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Study n Emotion induction Comparison groups Dependent variable

Effect

size (d)

Keller et al., 2003 (Study

1)


happiness*

Risk perceptions j 0.07

Keltner et al., 1993 (Study

2)

56 Recall of emotional event

and physical pose

Anger* vs. sadness Judgements of the likelihood of a

mishap due to others j0.81

Keltner et al., 1993 (Study

4)

68 Recall of emotional event

and physical pose

Anger vs. sadness* Judgements of the likelihood of

negative events j0.65

Lerner et al., 2003 648 Recall of emotional event

and film clips

Anger vs. fear* Risk assessments for self j 0.23

Lerner et al., 2003 648 Recall of emotional event

and film clips

Anger* vs. fear Choice of effective social

policiesdm0.20

Lerner & Keltner, 2001

(Study 4)

60 Film clips Anger* vs. fear Judgements of the likelihood of

risky events j0.57

Lerner et al., 2004 132 Film clips Sadness vs. disgust Choice of selling price for a

productdm0.00

Liersch et al., 2007 (Study

4)


happiness


Lin et al., 2006 160 Recall of emotional event Sadness vs.

happiness*

Choice of selling price for a

productdm0.55

Lin et al., 2006 240 Film clips Sadness vs.

happiness*

Choice of selling price for a

productdm0.18

Nabi, 2002 164 Read an article Anger vs. fear* Favourable attitude toward a

plan j0.08

Nabi, 2003 105 Rate emotion words Anger* vs. fear Choice of retributive solutionsdm 0.24

Nabi, 2003 109 Rate emotion words Anger vs. fear* Judgements of societal causes j 0.43

Raghunathan & Corfman,

2004 (Study 1)

62 Scenarios Fear vs. sadness* Preference for completing an

enjoyable task j0.82

Raghunathan & Corfman,

2004 (Study 1)

62 Scenarios Fear vs. sadness* Choice of which task to complete

firstdm0.81

Raghunathan & Pham,

1999 (Study 1)

52 Scenarios Fear vs. sadness* Preference for low probability/

high payoff gamble j0.19

Raghunathan & Pham,

1999 (Study 1)

52 Scenarios Fear vs. sadness* Choice of low probability/high

payoff gambledm0.45

Raghunathan & Pham,

1999 (Study 2)

48 Scenarios Fear vs. sadness* Preference for high pay/low job-

security job j0.19

Raghunathan & Pham,

1999 (Study 3)

48 Scenarios Fear vs. sadness* Choice of high pay/low job-

security jobdm0.96

Raghunathan & Pham,

1999 (Study 3)

93 Scenarios Fear vs. sadness* Preference for high risk/high

reward j0.10

Raghunathan & Pham,

1999 (Study 2)

93 Scenarios Fear vs. sadness* Choice of low probability/higher

rewarddm0.43


(Study 1)

98 Scenarios Fear vs. sadness* Choice of a comforting gamedm 0.13


(Study 2)

110 Scenarios Fear vs. sadness* Choice of a prescription drugdm 0.15

Rucker & Petty, 2004 83 Read an article Anger* vs. sadness Preference for an active choice j 0.44

Small & Lerner, 2008 76 Recall of emotional event Anger* vs. sadness Choice to decrease assistancedm 0.94

Notes: n�total sample size; *denotes which comparison group had a higher level on the dependent variable; j�judgement task;dm�decision-making task; d�the absolute value of the unweighted effect-size estimate.



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d�0.20 is considered small, d�0.50 is consideredmedium, and d�0.80 is considered a large effect).In the overall treatment�control judgementsample by emotion group comparison, effect sizesfor discrete emotion groups are relatively small(Anger: d�0.06, SD�0.09; Fear: d�0.11,SD�0.11; Sadness: d�0.18, SD�0.22; Disgust:d�0.17, SD�0.26). In the overall treatment�control decision-making sample by emotion groupcomparison, effect sizes for discrete emotiongroups are small to large (Anger: d�0.26,SD�0.29; Fear: d�0.18, SD�0.19; Sadness:d�0.33, SD�0.28; Disgust: d�0.36,SD�0.27; Guilt: d�0.98, SD�0.34). Thesefindings do not support H4a, which states thatwhen compared to a control group, anger and fearwill have moderate mean effect sizes both forjudgement outcomes and decision-making out-comes, or H4b, which states that when comparedto a control group sadness will have small meaneffect sizes, both for judgement outcomes anddecision-making outcomes. In fact, anger, fear,and sadness all have small mean effect sizes, bothfor judgement outcomes and for decision-makingoutcomes.

Additionally, an examination of the Q statisticreveals non-significant values. However, the

percent of variance accounted for by samplingerror in the judgement sample in all emotiongroup comparisons is above 75% indicating thatno moderators are present in these samples. Forthe decision-making sample, however, the percentof variance accounted for by sampling error isbelow 75% for four of the five emotion groupcomparisons (Anger: 26%, Sadness: 67%, Disgust:59%, and Guilt: 35%) with non-significant Q

statistics indicating the need to investigate thepresence of moderators in these particular sam-ples.

The results of the meta-analysis for the judge-ment and decision-making criteria for thetreatment�treatment samples by emotion groupcomparison are presented in Table 4. In the overalltreatment�treatment judgement sample by emo-tion group comparison, effect sizes for discreteemotion groups are small to moderate (Anger/Fear: d�0.27, SD�0.12; Anger/Sadness:d�0.41, SD�0.30; Anger/Happiness: d�0.13,SD�0.16; Fear/Sadness: d�0.31, SD�0.29;Sadness/Happiness: d�0.06, SD�0.05). In theoverall treatment�treatment decision-makingsample by emotion group comparison, effect sizesfor discrete emotion groups are small to large(Anger/Fear: d�0.21, SD�0.01; Anger/Sadness:

Table 3. Overall results for treatment�control sample on judgement and decision-making outcomes by emotion group comparison

95% CI

k N d SD SE PVA L U Q

TC j

Anger 6 535 0.06 0.09 0.05 100.00 �0.04 0.16 1.16

Fear 6 542 0.11* 0.11 0.05 100.00 0.01 0.21 1.54

Sadness 9 553 0.18* 0.22 0.07 100.00 0.04 0.32 6.66

Happiness 5 304 0.17* 0.26 0.07 100.00 0.03 0.31 4.83

TC dm

Anger 10 862 0.26*** 0.29 0.05 58.69 0.16 0.36 17.04*

Fear 6 484 0.18*** 0.19 0.05 100.00 0.08 0.28 4.44

Sadness 13 975 0.33*** 0.28 0.06 71.02 0.21 0.45 18.31

Disgust 2 192 0.36*** 0.27 0.04 59.06 0.28 0.44 3.39

Guilt 3 333 0.98*** 0.34 0.04 34.85 0.90 1.06 8.61*

Notes: TC j�treatment�control judgement sample; TC dm�treatment�control decision-making sample; k�number of effect-size

estimates; N�sum of participants; d�average sample-weighted effect-size estimate using Cohen’s d; SD�standard deviation of

effect-size estimates; SE�standard error of effect-size estimates; PVA�percent of variance accounted for by sampling error; 95%

CI �95% confidence interval; Q�Q statistic (Hunter & Schmidt, 1990). *pB.05; **pB.01; ***pB.001.

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d�0.48, SD�0.37; Anger/Guilt: d�1.06,SD�0.26; Fear/Sadness: d�0.41, SD�0.29;Sadness/Happiness: d�0.30, SD�0.13). These

findings are somewhat in line with H1a, wheremean effect sizes for comparisons between angerand fear and anger and sadness are small tomoderate both for judgement outcomes and

decision-making outcomes. The findings alsosupport H1b, where mean effect sizes for angerand happiness are small for judgement outcomes;however, not enough studies were available tosupport conclusions regarding decision-making

outcomes. Lastly, H2 was supported where themean effect size for the comparison between fearand sadness was small, both for judgement out-comes and decision-making outcomes.

While the percent of variance accounted for by

sampling error in several of the JDM samples was

below 75% indicating the possible presence of

moderators, many of the sample sizes were small

enough to preclude a meaningful analysis. In

addition, the comparison of treatment group to

treatment group makes interpretation of any meta-

analytic findings difficult and ultimately results in

limited conclusions due to the absence of a control

group; therefore, no moderator analyses wereconducted on the treatment�treatment samples.

Effects of moderating variables

Judgement and decision-making criteria. The fol-lowing section reviews the results from themoderator analysis investigating JDM moderatorsof effect size for the treatment�control samples.These results are presented in Table 5. Asdiscussed in the previous section, only four of thefive emotion group comparisons were eligible forthe moderator analysis (Anger, Sadness, Disgust,

and Guilt). However, because the disgust and guiltcomparison groups contain so few studies (i.e., 2and 3, respectively) they were eliminated from theanalysis. Results for anger and sadness are dis-cussed below.

In each emotion-group sample, the decision-making task types were examined. For the angercomparison group, the largest effect size observedwas for Policy choice (d�0.33, SD�0.13) withRisk seeking (d�0.31, SD�0.37) and Economicchoice (d�0.18, SD�0.23) also small to mod-erate in size. For the sadness comparison group,the largest effect size observed was for Policy

Table 4. Overall results for treatment�treatment sample on judgement and decision-making outcomes by emotion group comparison

95% CI


TT j

Anger and fear 5 1058 0.27*** 0.12 0.02 100.00 0.23 0.31 3.54

Anger and sadness 5 319 0.41*** 0.30 0.07 72.38 0.27 0.55 6.91

Anger and happiness 3 242 0.13** 0.16 0.05 100.00 0.03 0.23 1.49

Fear and sadness 4 255 0.31*** 0.29 0.07 77.19 0.17 0.45 5.18

Sadness and happiness 10 832 0.06 0.05 0.05 100.00 �0.04 0.16 0.46

TT dm

Anger and fear 2 753 0.21*** 0.01 0.01 100.00 0.19 0.23 0.34

Anger and sadness 2 194 0.48*** 0.37 0.04 31.47 0.40 0.56 6.36

Anger and guilt 3 342 1.06*** 0.26 0.04 61.48 0.98 1.14 4.88

Fear and sadness 6 462 0.41*** 0.29 0.05 62.75 0.31 0.51 12.75*

Sadness and happiness 10 1166 0.30*** 0.13 0.04 100.00 0.22 0.38 5.07

Notes: TT j�treatment�treatment judgement sample; TT dm�treatment�treatment decision-making sample; k�number of effect-size

estimates; N�sum of participants; d�average sample-weighted effect-size estimate using Cohen’s d; SD�standard deviation of effect-

size estimates; SE�standard error of effect-size estimates; PVA�percent of variance accounted for by sampling error; 95% CI �95%

confidence interval; Q�Q statistic (Hunter & Schmidt, 1990). *pB.05; **pB.01; ***pB.001.



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choice (d�0.57, SD�0.11) with Economicchoice moderate in size (d�0.48, SD�0.31).Risk seeking (d�0.16, SD�0.16) and Riskaversion (d�0.13, SD�0.10) were consideredsmall. These results are not supportive of H3a,which states that, when compared to a controlgroup, anger will have moderate mean effect sizesfor risk seeking and policy choice. However, theseresults are partially supportive of H3b, whichstates that, when compared to a control group,fear and sadness will have small mean effect sizeson risk seeking and policy choice. In fact, angerhad small mean effect sizes for risk seeking andpolicy choice and sadness had a small mean effectsize for risk seeking but a moderate mean effectsize for policy choice. The results for fear didnot indicate the presence of moderators for thefear sample so no results are available for thatsample.

Emotion-manipulation characteristics. The resultswith respect to the moderator analysis of emotion-manipulation characteristics for the decision-making sample are presented in Table 6. For theanger comparison group, when the emotionmanipulation is done individually (i.e., the parti-cipant is alone; d�0.52, SD�0.53) the meaneffect size is moderate whereas when the emotionmanipulation is administered in a group (d�0.10,SD�0.11) the mean effect size is small. In

addition, studies with significant manipulationchecks (d�0.25, SD�0.34) and studies that didnot use a manipulation check (d�0.28,SD�0.16) both resulted in small mean effectsizes.

For the sadness comparison group, results arepresented in Table 7. In particular, the studies thatspecified that the emotion manipulation was doneindividually produced a moderate mean effect size(d�0.63, SD�0.34). Some interesting differ-ences, though, arose in the specific method ofmanipulating sadness. Notably, using film clips(d�0.51, SD�0.36) had a moderately sized meaneffect whereas having participants recall an emo-tional event (d�0.31, SD�0.12) or read ascenario (d�0.29, SD�0.13) resulted in smallmean effect sizes. This finding may point to theeffectiveness of certain induction methods based onthe discrete emotion of interest. Lastly, studies thatused a manipulation check (d�0.53, SD�0.30)showed moderate mean effect sizes whereas thosethat did not use a manipulation check (d�0.19,SD�0.14) had small mean effect sizes.

Study-design characteristics. This section reviewsthe results from the moderator analysis investigat-ing study-design characteristics for the treatment�control decision-making samples. The results forthe anger comparison group are presented inTable 8. In this sample, whether or not a study

Table 5. Results for treatment�control sample by decision-making outcomes by emotion group

95% CI


Anger

Risk seeking 5 344 0.31*** 0.37 0.06 45.16 0.19 0.43 11.07*

Policy choice 2 183 0.33*** 0.13 0.05 100.00 0.23 0.43 0.78

Economic choice 3 3335 0.18*** 0.23 0.04 71.96 0.10 0.26 4.17

Sadness

Risk seeking 4 237 0.16** 0.16 0.07 100.00 0.02 0.30 1.46

Risk aversion 3 225 0.13** 0.10 0.05 100.00 0.03 0.23 0.58

Policy choice 2 145 0.57*** 0.11 0.06 100.00 0.45 0.69 0.37

Economic choice 4 368 0.48*** 0.31 0.05 49.25 0.38 0.58 8.12*

Notes: k�number of effect-size estimates; N�sum of participants; d�average sample-weighted effect-size estimate using Cohen’s d;

SD�standard deviation of effect-size estimates; SE�standard error of effect-size estimates; PVA�percent of variance accounted for by

sampling error; 95% CI �95% confidence interval; Q�Q statistic (Hunter & Schmidt, 1990). *pB.05; **pB.01; ***pB.001.

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received funding did seem to yield small mean

effect sizes (Funded: d�0.21, SD�0.35 and

Unfunded: d�0.32, SD�0.17). Also, whether

studies used undergraduate students (d�0.28,

SD�0.32) or a mix of undergraduate students

and community volunteers (d�0.10, SD�0.00)

mean effect sizes were small for both. Interest-

ingly, for studies that reported gender, mean effect

Table 7. Results for treatment�control sample on decision-making outcomes by sadness: Emotion-manipulation characteristics

95% CI


Emotion manipulation setting

Individual 4 257 0.63*** 0.34 0.07 57.05 0.49 0.77 7.01

Not specified 9 718 0.23*** 0.15 0.05 100.00 0.13 0.33 3.92

Relatedness of emotions to task

Incidental 13 975 0.33*** 0.28 0.06 71.02 0.21 0.45 18.31

Emotion manipulation method

Film clips 4 267 0.51*** 0.36 0.06 48.25 0.39 0.63 8.29*

Recall of emotional event 3 263 0.31*** 0.12 0.05 100.00 0.21 0.41 0.91

Scenarios 3 168 0.29*** 0.13 0.07 100.00 0.15 0.43 0.68

Not specified 2 208 0.05 0.02 0.04 100.00 �0.03 0.13 0.02

Manipulation check used

Yes 6 412 0.53*** 0.30 0.06 68.85 0.41 0.65 8.72

Significant 6 412 0.53*** 0.30 0.06 68.85 0.41 0.65 8.72

No 7 563 0.19*** 0.14 0.05 100.00 0.09 0.29 2.53




Table 6. Results for treatment�control sample on decision-making outcomes by anger: Emotion-manipulation characteristics

95% CI


Emotion manipulation setting

Individual 3 129 0.52*** 0.53 0.10 36.28 0.32 0.72 8.27*

Group 3 343 0.10* 0.11 0.04 100.00 0.02 0.18 0.99

Not specified 3 298 0.39*** 0.13 0.04 100.00 0.31 0.47 1.20

Relatedness of emotions to task

Incidental 8 663 0.29*** 0.32 0.05 49.33 0.19 0.39 16.22*

Emotion manipulation method

Recall of emotional event 7 638 0.26*** 0.26 0.04 65.84 0.18 0.34 10.63

Manipulation check used

Yes 7 548 0.25*** 0.34 0.05 46.92 0.15 0.35 14.92*

Significant 7 548 0.25*** 0.34 0.05 46.92 0.15 0.35 14.92*

No 3 314 0.28*** 0.16 0.04 100.00 0.20 0.36 2.05






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sizes were not that sizeable with predominantly

female samples (d�0.34, SD�0.47) and in

samples composed of equal males and females

(d�0.17, SD�0.06) the mean effect size was

small. Of note also is that the setting in which the

cognitive task is administered seems to impact the

influence of anger on decision-making quite a bit.

Similar to eliciting emotion, which was associated

with a moderate mean effect size when carried out

in an individual setting, a large mean effect size

was found when cognitive tasks were administered

in an individual setting (d�1.18, SD�0.06) and

small mean effect sizes were found when the tasks

were administered in a group setting (d�0.12,

SD�0.10).The results for the sadness comparison group

are presented in Table 9. In this sample, studies

that received funding (d�0.41, SD�0.19) and

studies that did not receive funding (d�0.28,

SD�0.32) had small mean effect sizes. In contrast

to the results for the anger comparison group,

studies that recruited community volunteers as

participants (d�0.75, SD�0.43) had a moderate

to large mean effect size and those that used

undergraduates (d�0.28, SD�0.20) had a small

mean effect size. An additional finding of note is

the gender make-up of the participant pool. In

particular, studies that used predominantly male

participants (d�0.69, SD�0.36) had a moderate

mean effect size and studies that used predomi-

nantly female participants (d�0.28, SD�0.14) or

an equal composition (d�0.29, SD�0.16) had

small mean effect sizes. Similar to the findings in

the anger comparison group, for those studies that

specified the cognitive task was administered on an

individual basis the mean effect was moderate to

large (d�0.75, SD�0.46). Unfortunately, the

majority of studies did not indicate one way or

the other. Lastly, cognitive task measure type did

have a moderate impact on the influence of sadness

Table 8. Results for treatment�control sample on decision-making outcomes by anger: Study-design characteristics

95% CI


Study funding

Yes 6 472 0.21*** 0.35 0.05 43.72 0.11 0.31 13.72*

No 4 390 0.32*** 0.17 0.04 100.00 0.24 0.40 2.67

Sample

Undergraduates 7 615 0.28*** 0.32 0.05 46.85 0.18 0.38 14.94*

Mixed 2 171 0.10* 0.00 0.05 100.00 0.00 0.20 0.00

Gender

Predominantly female 2 117 0.34*** 0.47 0.07 32.92 0.20 0.48 6.08

Equal 2 186 0.17*** 0.06 0.04 100.00 0.09 0.25 0.16

Not specified 6 559 0.28*** 0.27 0.04 59.41 0.20 0.36 10.10

Cognitive task measurement

Individual 2 50 1.18*** 0.06 0.20 100.00 0.79 1.57 0.04

Group 5 529 0.12** 0.10 0.04 100.00 0.04 0.20 1.30

Not specified 3 283 0.36*** 0.18 0.04 100.00 0.28 0.44 2.27

Cognitive task measure type

Objective 9 755 0.27*** 0.30 0.05 53.04 0.17 0.37 16.97*

Cognitive task measure format

Multiple choice 9 755 0.27*** 0.30 0.05 53.04 0.17 0.37 16.97*




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on decision-making for both subjective measures(d�0.40, SD�0.21) and objective measures(d�0.31, SD�0.30).

Assessment of publication bias

Publication bias was assessed using a funnel plotdisplay and Egger et al.’s (1997) regression test offunnel plot asymmetry. It is expected that if

publication bias is present, then the funnel plotwill show small-sample studies reporting smalleffect sizes disproportionately absent becausethese are the studies that will not have reached

statistical significance and thus will not have beenpublished and retrieved (Hunter & Schmidt,2004). Visual inspection of the funnel plot showedthat the data points were widely dispersed across

both small and large effect sizes of low samplesize. In addition, the results of the regression test

indicated that the intercept from the analysis did

not differ from zero (b � �0.16, p�.11). Both

analyses suggest that publication bias does not

pose an issue in the current meta-analysis.

DISCUSSION

Meta-analytic procedures were applied to the

existing emotions and cognition literature to

provide a quantitative estimate of the influence

of discrete emotions on JDM outcomes and also

to investigate the moderating relationship of

emotion-manipulation characteristics and study-

design features. Overall, the results indicate that

emotions have small to large effects on these

cognitive outcomes that vary based on the emotion

type and the type of cognitive task. In addition,

several methodological factors moderate the

Table 9. Results for treatment�control sample on decision-making outcomes by sadness: Study-design characteristics

95% CI


Study funding

Yes 6 409 0.41*** 0.19 0.06 100.00 0.29 0.53 3.66

No 7 566 0.28*** 0.32 0.05 51.32 0.18 0.38 13.64*

Sample

Undergraduates 11 866 0.28*** 0.20 0.05 100.00 0.18 0.38 8.46

Community 2 109 0.75*** 0.43 0.08 44.71 0.59 0.91 4.47

Gender

Predominantly male 2 166 0.69*** 0.36 0.05 41.46 0.59 0.79 4.82

Predominantly female 5 303 0.28*** 0.14 0.07 100.00 0.14 0.42 1.38

Equal 2 106 0.29*** 0.16 0.08 100.00 0.13 0.45 0.68

Not specified 4 400 0.24*** 0.23 0.04 76.58 0.16 0.32 5.22

Cognitive task measurement

Individual 3 124 0.75*** 0.46 0.11 50.71 0.53 0.97 5.92

Not specified 9 718 0.23*** 0.15 0.05 100.00 0.13 0.35 3.92

Cognitive task measure type

Objective 9 738 0.31*** 0.30 0.05 57.95 0.21 0.41 15.53*

Subjective 4 237 0.40*** 0.21 0.07 100.00 0.26 0.54 2.48

Cognitive task measure format

Multiple choice 13 975 0.33*** 0.28 0.06 71.02 0.21 0.45 18.31






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relationship of emotions with decision-makingoutcomes. These results are discussed below.

Overview of main effects

Type of cognitive task. In the treatment�controlsample, the sample-weighted mean effect sizesranged from 0.06 to 0.18 for judgement and 0.18to 0.98 for decision-making, which are consideredsmall effects and small to large effects, respectively(Cohen, 1988). As discussed previously, indivi-duals in general are not good at combiningattributes to make a final judgement (Shafir &LeBoeuf, 2002) so this deficit in their ability tomake assessments and inferences about the situa-tion is relatively unchanged with the introductionof discrete emotions. It is the overall decision thatis impacted much more. In the treatment�treat-ment sample, the sample-weighted mean effectsizes ranged from 0.06 to 0.41 for judgementand 0.21 to 1.06 for decision-making, whichare considered small to moderate and small tolarge effects, respectively. However, for thissample the larger range of effect sizes may bedue more to the comparison type (i.e., differencesbetween two emotions and not between anemotion group and a control group) and shouldbe interpreted as such. Indeed the magnitude ofthe effects in both samples is similar to, and insome instances larger than, those reported forcomparable JDM literatures. Specifically,Schwenk (1990) reported mean effect sizes ran-ging from 0.13 to 0.64 for devil’s advocacy and0.16 for dialectical inquiry on decision-making.Gordon (1996) reported a mean effect size of 0.20for ingratiation on judgements and evaluations. Inaddition, Hart et al. (2009) reported a mean effectsize of 0.36 for information preference when theinformation supported the individual’s beliefs.

Discrete emotions. In the treatment�control sam-ple, the sample-weighted mean effects were fairlydifferent depending on the type of cognitive task.Overall, though, sadness, disgust, and guiltemerged as some of the most influential emotionsfor decision-making (i.e., d�0.33, d�0.36, andd�0.98, respectively). Again, the magnitude of

these effects is similar to, and in some instancessmaller than, those reported for comparableemotions literatures. In particular, Lyubomirsky,King, and Diener (2005) reported mean effectsizes of 0.54 (r�.26) for the relationship betweenhappiness and prosocial behaviour, 0.82 (r�.38)for happiness and physical well-being, and�0.65 (r��.31) for sadness and creativity. Inaddition, Kaplan, Bradley, Luchman, and Hayes(2009) reported mean effect sizes of 0.32 (r�.16)and �0.26 (r��.13) for the relationshipbetween positive affectivity and task performanceand negative affectivity and task performance,respectively. Lastly, Carlson, Charlin, and Miller(1988) reported a mean effect size of 1.29(r�.54) for the relationship between positivemood and helpfulness.

When examining the emotion group compar-isons of the treatment�treatment sample it isagain clear that specific emotions do differ fromone another in their effects on cognitive out-comes. Specifically, it seems that anger differs themost from fear and sadness in both JDM contexts.However, considering the decision-making sam-ple alone, the largest effect size observed wasbetween anger and guilt. This set of findingsis consistent with the results of many studiesinvestigating anger (Lerner & Tiedens, 2006).From an appraisal theory perspective, anger andguilt have opposite appraisals on responsibility fornegative events where angry individuals tend toblame others while guilty individuals attributeresponsibility to themselves (Neumann, 2000).Fear and sadness also differ from anger in severalways. Fear is associated with a sense of situationalcontrol and uncertainty about what has happened.Sadness is associated with a perception thatoutcomes are under the influence of the situation,attributing responsibility for an event to situa-tional factors (Keltner et al., 1993).

From an activation perspective, anger, sadness,and fear also differ in a number of distinct ways.Anger arises from events/people threatening/thwarting one’s efforts with negative outcomesfor the self/others, motivating a person to takesome action against the causal agent (i.e., highlevel of activation). On the other hand, fear is

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experienced in the context of immediate danger orharm leading to high levels of motivation toescape or avoid the threatening object (Langet al., 1990; Seitz et al., 2007). Sadness isproduced from the loss of an object of interest(Lazarus, 2001; Russell & Barrett, 1999). Themotivational tendency involved is to stop move-ment toward a stimulus with a moderate retreat oravoidance of the stimulus (Roseman & Smith,2001).

Moderator variables

Anger. The within-emotion-type analysis re-vealed some interesting results for decision-makingstudies. In line with much of the research on anger,the largest effects were seen for policy choice andrisk seeking. This does not seem surprising giventhat anger has been found to evoke more risk-seeking choices, and lead to more support forpunitive policies toward social issues because of itsassociation with appraisals of individual controland a strong motivational tendency to act againstthe responsible party (Gault & Sabini, 2000;Lerner & Keltner, 2001; Lerner et al., 2004; Smith& Ellsworth, 1985).

Additional moderating effects were found foran emotion-manipulation characteristic and astudy-design characteristic. Individuals inducedwith anger individually had moderate effects andthose induced in groups of two or more had asmall effect. This finding is not surprising andcould be interpreted in the context of emotionaldisplay rules. This notion was initially introducedby Ekman and Friesen (1969) to explain observedcultural differences in the ways in which indivi-duals express emotions, but has been found inorganisational settings as well. Anger, specifically,is considered a powerful emotion and is viewed asfunctional as long as it is expressed in sociallyappropriate ways (Eid & Diener, 2001). However,appropriateness of emotional displays has beenshown to vary based on the interaction partnerwhere individuals tend to feel more comfortablewith expressing emotions to in-group members oftheir social network versus out-group members

(Safdar et al., 2009; Triandis, 1994). Differencesseen in the current study may be due to indivi-duals in a group setting attempting to regulatetheir experience and expression of anger in thepresence of others whereas those who wereinduced alone did not.

Another finding that emerged was in studies inwhich participants were given the choice taskindividually. The mean effect size was largewhereas for studies in which participants weregiven the cognitive task in a group the mean effectsize was small. This is an interesting result andmay point to the tenuous nature of inducedemotions such as anger. Unlike the real world,emotions studied in the laboratory are relativelyweak and short lived, thus making them suscep-tible to outside influences such as methodologicaldesign (Small & Lerner, 2008). Perhaps havingother individuals present, although not interact-ing, at the time of engaging in a choice taskdiminished the influence of anger.

Sadness. The within-emotion-type analysis forsadness revealed some similar results to the angersample for decision-making studies. The largestmean effects were seen for policy choice andeconomic choice. These findings are not surpris-ing based on the research that is available forsadness. In particular, individuals induced to feelsad attribute circumstances to situational factorsresulting in protective policy choices or increasingwelfare assistance (Small & Lerner, 2008) andtend to increase the price of a choice objectbecause it represents an opportunity to changecircumstances (Cryder, Lerner, Gross, & Dahl,2008; Han, Lerner, & Keltner, 2007). Overall,sadness had small to moderate mean effects onpolicy choice and risk seeking.

Additional moderating effects were found forseveral emotion-manipulation characteristics andstudy-design characteristics. Differential effectswere seen in the manipulation method with filmclips having a moderate mean effect size. Thisresult has some support in the literature measur-ing both the subjective experience of emotion andthe physiological or neurobiological activations ofemotions. In particular, films are capable of



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eliciting mild or strong emotional responses andtend to rival or exceed the response strength ofother procedures (Rottenberg, Ray, & Gross,2007). This is also true for their ability to activateresponse systems associated with emotions andpoints to the utility of carefully crafted film clipsfor the successful and potent induction of discreteemotions. In addition, according to appraisaltheories, because emotions are presumed to beelicited by current appraisals, those stemmingfrom remembered or imagined events can bedifferent from and less salient than the originalexperience (Roseman & Smith, 2001). Thisinduction procedure seems to hold promise foremotions research and from the current results itis clear that it has implications for the effects ofsadness on decision-making outcomes.

Lastly, studies that used a manipulation checkthat was significant had a moderate mean effectsize whereas studies that did not use a manipulationcheck had a small mean effect size. This result mayindicate differences in quality of research designwhere those studies with more rigorous designs arelikely to produce significant results. It is recom-mended that researchers employ manipulationchecks because of the necessity of confirming theeffectiveness of the manipulation rather thanpotentially drawing the conclusion that the treat-ment itself has no effect on the decision task ifdifferences are not observed. It also seems toprovide evidence that the use of a manipulationcheck does not diminish the effects of sadness ondecision-making outcomes.

Also of interest is the composition of thesample where studies that used community vo-lunteers had a moderate to large mean effect sizeand studies using undergraduate samples had asmall mean effect size. One explanation for theseresults could have to do with the likelihood thatcommunity volunteers are older than college ageand have more experience with certain types ofdecisions such as policy choice or decisionsinvolving risk. These individuals may be moreapt to make choices that are consistent with theirgoals or prior beliefs allowing the experience ofemotions to obscure more rational channels ofdecision-making (Mishra, 2007). It should be

noted that decisions made on the basis of prob-ability assessments derived from experience differfrom those made on the basis of stated probabil-ities (Weber, Shafir, & Blais, 2004). In addition,predominantly male samples had a moderatemean effect size and predominantly female ormixed samples had small mean effect sizes. This isan interesting finding, but should be interpretedwith caution because only two studies made upthis category. Additional research into genderdifferences in emotions is needed to understandthis finding more fully.

Limitations

This review has some limitations that should benoted. First, and foremost, the result of any meta-analysis is dependent upon the studies it includes.The quality, methodology, and research design ofeach individual study influences the outcome ofany meta-analysis (Cooper, 1998; Cooper &Hedges, 1994). The relative quality of each studywas not assessed prior to inclusion in order to beas comprehensive as possible because of thealready small number of studies available. How-ever, the limitations of these individual studies canhave an effect on overall results. This should bekept in mind when interpreting findings.

Second, the number of studies included in thismeta-analysis was relatively small. This waspartially due to the fact that more than half ofthe studies included did not use a control group asa comparison to the treatment. In addition, thedata presented in some studies was insufficient forcalculating an effect-size estimate. Therefore, itshould be noted that some relevant studies are notpresent in the current meta-analysis.

Third, the absolute values of the effect-sizeestimates calculated for each study were includedin the current analysis. This was due to the lack ofnormative criteria for coding JDM outcomes asbetter or worse (i.e., positive or negative). Un-fortunately, analysing the absolute values of theeffect-size estimates limits the conclusions thatcan be drawn about the specific ways in whichdiscrete emotions impact JDM outcomes. How-ever, the results of the current meta-analysis do

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still allow conclusions to be made about thedegree to which different emotions affect JDMoutcomes. Thus, this consideration should betaken into account when interpreting these re-sults.

Fourth, the current study does not have anexhaustive list of moderators. This is due to severalfactors. In many of the studies, there was a lack ofavailable data. Specifically, variables such as gendermake-up of the sample or other demographiccharacteristics were difficult or impossible to assessin some instances because of a tendency for studiesto report aggregate data. In addition, other,possibly influential, variables (e.g., individualdecision-making style) are not yet examined inthis literature. An attempt was made, though, tofocus on a core set of variables that are consistentthroughout the literature because of the smallnumber of studies that were included.

Implications for future research

The results of the present meta-analysis provideevidence for the influence of emotions on JDMtasks. However, within the moderator analysisseveral important findings emerged. First, guiltappeared to be one of the most influential emotionsin decision-making. Research on moral emotionshas defined guilt as a self-focused emotion thatincreases one’s concerns about responsibility andthe potential for future guilt (Gangemi & Mancini,2007, Nelissen & Zeelenberg, 2009). This acts as amotivator for one to invest time and energy toreconcile actions against another person and alle-viate feelings of guilt (Frank, 2004). In the realm ofdecision-making, guilt has been shown to focusindividuals on what is explicitly represented in thedecisional task and restrict the search for alter-natives, making it a particularly interesting emo-tion for future research (Gangemi & Mancini,2007). Also, the current study allowed a detailedanalysis of anger and sadness, highlighting theextent to which these two same-valence emotionsinfluence decision-making tasks, as well as differ-ences in the extent to which induction methods andcognitive-task settings moderate these effects.

In conclusion, we identified discrete emotion

types and emotion manipulation and study-design

characteristics and then used meta-analytic pro-

cedures to empirically assess their relationships

with JDM tasks. Our results suggest that discrete

emotions that are of the same valence (e.g., anger,

sadness, fear, etc.) differ in the extent to which

they influence JDM as predicted by more fine-

grained approaches (i.e., appraisal theories) to the

study of emotions and cognition. In addition,

emotion type drives differences in effect sizes

associated with how emotions are manipulated

and in what setting, the gender composition, use

of a manipulation check, and the setting in which

the cognitive task is administered. These findings

have some implications for emotion theories such

that patterns of effects lend some support to

differences between emotions that are seemingly

opposite on specific appraisal dimensions (e.g.,

anger compared to sadness and guilt, and fear

compared to sadness). The small effects observed

for specific emotions when compared to a control

group are somewhat in line with activation

theories and the existing research on emotions

(e.g., small to moderate effects are pretty typical).

However, for emotions such as anger, more

moderate effects may be expected based on its

status as an approach emotion. This study also

points to the importance of the moderating design

variables investigated in the application and

interpretation of results.

Manuscript received 9 January 2009

Revised manuscript received 16 January 2010

Manuscript accepted 20 December 2010

First published online 15 April 2011

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Decision Making and Emotions

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