Negotiation Strategies With Incongruent Facial Expressions .
Negotiation Strategies with Incongruent Facial Expressions of Emotion CauseCardiovascular ThreatPeter Khooshabeh (khooshabeh@ict.usc.edu) 1, 3Celso de Melo (demelo@usc.edu) 2Brooks Volkman (volkman@psych.ucsb.edu) 1Jonathan Gratch (gratch@ict.usc.edu) 3Jim Blascovich (blascovi@psych.ucsb.edu) 1Peter J. Carnevale (carnevale@usc.edu) 21Department of Psychological and Brain Sciences, University of California, Santa Barbara, CA 93106 USA2Marshall School of Business, University of Southern California, Los Angeles, CA 900893Institute for Creative Technologies, University of Southern California, Los Angeles, CA 90094AbstractAffect is important in motivated performance situations suchas negotiation. Longstanding theories of emotion suggest thatfacial expressions provide enough information to perceiveanother person’s internal affective state. Alternatively, thecontextual emotion hypothesis posits that situational factorsbias the perception of emotion in others’ facial displays. Thishypothesis predicts that individuals will have differentperceptions of the same facial expression depending upon thecontext in which the expression is displayed. In this study,cardiovascular indexes of motivational states (i.e., challengevs. threat) were recorded while players engaged in a multiissue negotiation where the opposing negotiator (confederate)displayed emotional facial expressions (angry vs. happy); theconfederate’s negotiation strategy (cooperative vs.competitive) was factorially crossed with his facialexpression. During the game, participants’ eye fixations andcardiovascular responses, indexing task engagement andchallenge/threat motivation, were recorded. Results indicatedthat participants playing confederates with incongruent facialexpressions (e.g., cooperative strategy, angry face) exhibited agreater threat response, which arises due to increaseduncertainty. Eye fixations also suggest that participants lookat the face more in order to acquire information to reconciletheir uncertainty in the incongruent condition. Taken together,these results suggest that context matters in the perception ofemotion.Keywords: facial expressions, negotiation, context inemotionIntroductionNegotiation is relatively common in personal andprofessional settings. A child might ask a parent whethershe can leave the dinner table. The parent might sternlycommand the child to finish her vegetables and the childcould make a counter offer to finish the peas but not thebroccoli. This could ensue into a strategic and emotionallycharged social interaction.Emotion is an important human factor in motivatedperformance situations (i.e., those that are self-relevant andtherefore task engaging and require instrumental cognitiveresponses; Blascovich, 2008). Such interactions are rarelyaffectively neutral; that is, they are associated withinteractants’ positive or negative emotional states. Clearly,negotiations represent motivated performance situations tointerested partners. And, experimental negotiation tasks areno exception, including those involving real human players(Van Kleef, De Dreu, & Manstead, 2004) and digital agents(i.e., player representations driven by computer algorithms,de Melo, Carnevale, & Gratch, 2011).The current work examines individuals’ motivationalresponses, using physiological indexes, to emotionallyexpressive virtual characters in a multi-issue negotiationtask. Specifically, we focus on the question of howsituational context affects emotion perception from facialexpressions. In person-to-agent negotiation tasks,experimenters often insert communicative cues such asagent facial expressions intended to strategically manipulateuser’s emotions. Agents that show emotion have now beenused in several domains such as education, entertainment,training, therapy and commerce (for a review see Beale &Creed, 2009). In a multi-issue negotiation task, de Melo andcolleagues (2011) reported that participants made moreconcessions to a virtual human that displayed an angryfacial expression compared to a happy facial expression.Most research on the effects of virtual characters’emotional facial expressions has relied on subjectiveresponses from participants (e.g., Beale & Creed, 2009).However, given the evidence that emotion is processed vianon-conscious pathways, perhaps more so than consciouspathways (Tamietto & De Gelder, 2010), validatedphysiological measures related to affect should provideconfirmation of the operation of non-conscious emotionalprocesses involved in motivated performance tasks such asnegotiation (Blascovich & Mendes, 2010).Psychophysiological Measurement ofMotivational StatesPsychophysiological research is now a well-establishedtechnique to infer peoples’ affective reactions to varioussituations (Blascovich, Vanman, Mendes, & Dickerson,
There is evidence that individuals’ explicit responses inexperimental tasks are not always congruent withunderlying physiologicalical markers (e.g., Blascovich,Mendes, Hunter, Lickel, & Kowai--Bell, 2001). By utilizingthe physiologicalal markers specified by the BPS model ofchallenge and threat, one can identify motivationalresponses to a stimulus that are not typically accessible to aparticipant during a motivated performance situation.Theoretical Motivation and ResearchQuestionsFigure 1:: The angry (left) and happy (right) facialexpressions displayed by the virtual confederate.2011). However, a lot of research involving peripheralphysiological markers has been based on unitaryphysiological responses such as heart rate variability(Rienerman-Jones,Jones, Cosenzo, & Nicholson, 2010) orelectrodermal activity (Meehan, Insko, Whitton, & Brooks,2002) mostly as indexes for workload and stress.Motivational research suggests that relying on unitaryindexes can mask important processes. For example, thephysiological indexesxes specified by the biobio-psychosocialmodel (BPS; Blascovich, 2008) of challenge and threatprovide a much more informative index of task motivation.Briefly, the BPS model is based on the neuroendocrineunderpinnings (i.e., Dienstbier, 1989) of cardiovascularresponses involving the sympathetic-adrenaladrenal-medullary(SAM) axis as well as the hypothalamic pituitarypituitary-adrenalcortical (HPA) axis.Psychologically, challenge motivation occurs when anindividual’s consciously and unconsciously evaluatedresources outweigh evaluated task demands.ands. Threat occurswhen resources are evaluated as not meeting task demands.Both states involve the activation of the SAM axis, whileonly the threat state involves both the axes.Accordingly, activation of common SAM axissympathetic neural and adrenalnal medullary endocrineprocesses affect cardiovascular responses underlying bothchallenge and threat including increased heart rate (HR) andincreased ventricular contractility (VC; i.e., decreased prepreejection period or “PEP”), and task engagement. However,cardiac output (CO) and total peripheral resistance (TPR(TPR)differ depending on motivational state. A challenge stateresults in decreased TPR and an increase in CO, whereas athreat state leads little or no change or a decrease in CO andlittle or no changehange or an increase in TPR (Blascovich &Mendes, 2010).Previous work on cognition and emotion perceptionproposes that context matters when people decode others’emotions (Barrett, Mesquita, & Gendron, 2011; Lanzetta &Englis, 1989; Singer et al., 2006).2006) In their review of theliterature, Barrett and colleagues point out how the visualscene in a stimulus can give rise to different interpretationsof an emotional statee conveyed by a facial expression. Ascowling face can convey anger or disgust depending on thebody posture with which it is paired. Individuals’ patterns ofbehavior can also serve as context cues that affect emotionalprocessing.Two similar individuals can give rise to differentemotional responses in their interaction partners based ontheir behaviors and actions. For example, Singer andcolleagues (2006) led participants to believe thatconfederate players in a Prisoner’s Dilemma game were fairor not based on the confederates’ game investment strategy.Experimenters then randomly cued participants that eitherthe (fair/unfair) confederate or the participant herself wouldreceive a painful shock. Participantsarticipants exhibited moreempathic neural activity (frontoonto-insular and anteriorcingular cortex) when they observed a fair player receive ashock compared to the unfair player (Singer et al., 2006).This is compelling because the only difference between theindividuals was the contextual information of their gameplaying strategy.On the basis of this research (i.e., Barrett et al., 2011;Singer et al., 2006), we can infer that different contextscontext canshape the perception of emotion as well as give rise todifferent neurophysiological responses to facial expressions.In particular, it is possible that an experimental confederatethat employs a fair strategy will be perceivedpedifferently as afunction of whether the individual smiles or scowls.Similarly, a fair individual that smiles might be perceiveddifferently compared to an unfair smiling individual. In thisstudy, we utilized virtual humans as research confederatesconfederat inorder to manipulate their facial expressions and negotiationstrategies while keeping other aspects of the interactionunder experimental control.The research question driving this work was: Dodifferences in virtual humans’ emotional facial expressionscoupled with their behavioral strategies in a negotiation taskaffect neuropsychological processes related to motivationand affect?The contextual emotion hypothesis suggests that if theconfederate’s negotiation strategy affects perceptual process
Figure 2: The multi issue bargaining negotiation taskinterface with areas of interest.of facial expressions, then individuals will show differentresponses to the same facial expression depending oncontext. Specifically, individuals will have a differentcardiovascular response to angry faces when paired with atough strategy compared to angry faces paired with softstrategies.Van Kleef and colleagues have argued that if partners in asocial interaction lack information about the other’s needs,desires, and goals, then emotional displays help peoplemake sense of situations (Van Kleef, De Dreu, & Manstead,2010). It follows then that people will tend to look more atemotionally significant facial features when there isuncertainty in social interactions. Therefore, with respect tothe eye tracking measure, the contextual emotion hypothesispredicts that individuals will fixate more on diagnosticfacial features when the confederate’s negotiation strategy isincongruent with his facial expression.An alternative hypothesis suggests that emotionperception is driven purely by the structural features of aface alone. This hypothesis predicts that individuals willshow heightened threat responses to angry faces comparedto happy faces—regardless of the confederate’s strategywith which they are coupled—and there should be more eyefixations on threatening faces (Mogg, Garner, & Bradley,2007; Tracy & Robins, 2008).MethodParticipants, Design, Materials, ApparatusEighty participants were recruited from universityundergraduate psychology courses. Participants played amulti-issue bargaining task (Van Kleef, De Dreu, &Manstead, 2004). The task involves a scenario in whichparticipants act as mobile phone sellers and have tonegotiate over three issues: a price, length of servicecontract, and warranty duration with the virtual human (seeFigure 2, Payoffs). Each issue had a level that denoted itsworth to the participants. Given that the participant was theseller, she would get the most points by selling the mobilephones for the highest price ( 150, level 9) in order to gain400 points; the shortest warranty period (1 month, level 9)corresponding to 120 points; and the shortest servicecontract (1 month, level 9) corresponding to 240 points.Participant’s maximum score was therefore 760 points.The confederate was an intelligent agent that displayedemotional facial expressions to convey anger or happiness.The study employed a 2 X 2 fully-crossed factorial betweensubjects experimental design. The two factors were thevirtual human’s emotional facial expression (happy orangry) crossed with his negotiation strategy (tough or soft).When the virtual human used a tough strategy (competitive),he made small concessions from his initial offer comparedto the larger concessions he made using a soft (i.e.,cooperative) strategy.Both the soft and tough negotiating confederates made theinitial offer to the participant, which was level 1 of price( 110, zero points to the participant), level 2 of warrantyperiod (8 months, 15 points to the participant), and level 1 oflength of service contract (9 months, zero points to theparticipant). From this 1-2-1 initial offer by the confederate,the soft and tough agent followed different counter offerpolicies. In both cases, the confederate’s offer was notcontingent on the participant’s counter offers (see Table 1).Table 1: Progression of Soft and Tough Negotiationoffers through the six round 2-21-3-22-3-22-3-33-3-3While participants interacted with the virtual characterduring the negotiation game, various measures related diographic (EKG) and impedance cardiographic(ZKG) signals were recorded continuously with a BiopacMP150 system, using a standard lead II electrodeconfiguration (for EKG) and a tetrapolar aluminum-mylartape electrode system (for ZKG); blood pressure wascontinuously recorded using an automated blood pressuredevice. The automated blood pressure recorded readings viaa cuff placed around the participants’ wrists and fingers oftheir non-dominant left hand. The EKG and ZKG signalswere scored using an interactive software program thatproduces ensemble-averaged values for heart rate (HR), pre-
ejection period (PEP). Additionally, cardiac output (CO)was calculated from stroke volume (SV) recordings viaimpedance and heart rate; and total peripheral resistance(TPR) was calculated using impedance and blood pressurereadings as a measure of vascular activity.An SMI RED eye tracker was used with 60Hz samplingrate and a 17” flat screen monitor. The eye tracking camerawas positioned to the monitor and as such was unobtrusiveto the participants during the task.ProcedureParticipants completed a health screening questionnaireand informed consent was obtained prior to theirparticipation. No one refused to participate. Female researchassistants proceeded to apply the necessary sensors forphysiological recording including impedance tapeelectrodes, EKG spot electrodes and blood pressure sensors.A five point calibration was used to ensure proper eyetracking measurement.Next, the participant sat comfortably at rest for fiveminutes prior to receiving any task instructions. Finally, theparticipants were instructed to play 1 practice round to learnthe user interface, during which the virtual human was notvisible. Next, the criterion negotiation game commenced for6 rounds. Afterwards, participants completed surveys thatrecorded their subjective and open-ended responses to thevirtual human.ResultsNegotiation TaskPerformance on the negotiation task was calculated basedon how much the participants conceded to the virtualconfederate over the six rounds. Each issue in thenegotiation was summed at each round to compute ademand score. The best outcome for the participant wouldhave been a demand score of 760 points. The finalperformance measure was the difference between the firstand last round demand scores. If participants conceded moreover the six rounds, this difference score would be higher. Aunivariate ANOVA with two factors (1. emotion: happy orangry; 2. strategy: tough or soft) showed no main effects orinteractions (all p’s 0.5, see Table 2 for means).Table 2: Mean demand score difference from the first andlast round (SD). A score of zero indicates no concession.SoftToughAngry150.2 (223)113.3 (155)131.8 (190)Happy129.4 (217)142.5 (193)136.3 (202)140.6 (217)127.6 (173)133 (195)Cardiovascular Physiological IndexesWe predicted that individuals interacting with the virtualconfederate would exhibit task engagement, and that thoseinteracting with an incongruent virtual confederate (e.g.,soft strategy but angry face) would experience threat.Task EngagementAccording to the Biopsychosocial Model, taskengagement is indexed by increases from baseline insympathetically driven cardiovascular responses. As iscommon in this research, we calculated changes frombaseline in preejection period (PEP), a purelysympathetically driven cardiovascular measure (Tomaka,Blascovich, Kelsey, & Leitten, 1993).We established average baseline values of HR and PEPby averaging baseline minutes 4 and 5 for each of thesemeasures. PEP decreased during the task (M 133.3 ms, SD 15.76) compared to the baseline (M 135.8 ms, SD 16.06), as predicted, two-tailed paired samples t-test, t(78) 3.31, p .001.Challenge and ThreatTotal Peripheral Resistance (TPR) scores were computedby subtracting TPR during baseline from TPR during thenegotiation task. A univariate ANOVA did not show a maineffect of either strategy or emotion (both F’s 1). Therewas also no interaction, F(1, 62) 1.47, p .23.Cardiac output (CO) reactivity scores were computed bysubtracting CO during baseline from CO during thenegotiation task. A univariate ANOVA, controlling forbaseline CO, with two factors showed no main effects ofemotion or strategy (F’s 1). There was an interactionbetween emotion and strategy, F(1, 76) 8.34, p 0.005,ηp2 .098.Using a Bonferroni adjustment, simple effects analysesrevealed that participants in the soft strategy conditionsignificantly differed from each other, F(1, 77) 5.34, p 0.024, ηp2 .065. Participants who interacted with thevirtual confederate that displayed an angry facial expressionwhile using a soft (more conceding) strategy had furtherreduced cardiac output compared to those participants in thesoft-happy condition (see Figure 3).Eye TrackingBeGaze eye tracking analysis (SMI) software was used toconstruct areas of interest (AOI) on different components ofthe task interface as well as the virtual confederate’s facialregions.
Figure 3: Cardiac output reactivity scores in the twovirtual human strategy and facial expression conditions.A multivariate ANOVA was conducted with the eightAOI (see Figure 2). The MANOVA showed a significantdifference among the different AOI, F(7, 504) 77.06, p 0.001, ηp2 .517. As Figure 4 shows, participants fixated onthe offer section of the game interface the longestpercentage of time throughout the task. However, the resultssuggest people also spend considerable time looking at theface. In fact, the percentage of time fixated on the totalface—aggregate of eyes, mouth, and remainder of theface—did not differ from the time fixated on the offer, t(72) .54, p .6.Mouth AOIThe main differentiating facial feature for the angry andhappy expressions was the mouth area. Past work indicatesFigure 4: Percentage of time during the wholenegotiation fixated on different areas of interest.that individuals from samples similar to ours tend to fixatemore on the mouth region (Blais, Jack, Scheepers, Fiset, &Caldara, 2008; Jack, Blais, Scheepers, Schyns, & Caldara,2009). Thus, we conducted an ANOVA with the two factors Figure 5: Percentage of time on the mouth as a functionof the confederate’s emotion and strategy.of strategy and emotion on the percentage of time fixatedon the mouth AOI.There was no main effect of strategy or emotion, F’s 1.There was a marginal interaction of strategy and emotion,F(1, 68) 3.281, p .074, ηp2 .046. As Figure 5 shows,participants in the soft-angry condition tended to fixate themouth for a longer time compared to participants in the softhappy condition.DiscussionVirtual human confederates in a negotiation game causeda threat motivational response (reduced cardiac output)when their facial expressions were not congruent with theirstrategies. Specifically, participants had lower cardiacoutput when the virtual human negotiated using a softstrategy but displayed an angry facial expression.Additionally, despite not reaching significance, similareffects occurred when participants engaged with a toughagent that showed happy facial expressions. Thisincongruence could cause more uncertainty, which is relatedto increases in task demands (Tomaka et al., 1993).Eye tracking results provide converging evidence.Participants in the incongruent strategy and emotioncondition (e.g., soft-angry) tended to fixate on the mostdiagnostic facial region longer compared to participants inthe congruent condition (e.g., soft-happy). This resultsuggests that participants tended to fixate longer at themouth in order to try to gain potential cues to reconcile theiruncertainty from the conflicting strategy and emotioncoupling.These results are compatible with the suggestion thatpeople look at others’ facial expressions in an attempt toreduce inherent uncertainty that occurs in social decisionmaking situations with counterparts that might havedifferent priorities and objectives (Van Kleef et al., 2010).Our results show specific psychophysiological evidence forthis process, especially when there is an incongruencebetween the counterpart’s strategy and the facial displays.These results are also in line with other research whichsuggests that context matters when people decode others’emotions (Barrett et al., 2011; Lanzetta & Englis, 1989;Singer et al., 2006; Szczurek, Monin, & Gross, 2012). An
identical angry facial expression gave rise to differentmotivational states depending on the strategic context inwhich it was displayed during the negotiation task.Finally, the results can have practical implications for thedesign of human-computer interaction systems. This worksuggests that cardiovascular measures are sensitive atdetecting incongruence and uncertainty in human users andsuggests that affecting the context in which emotions areshown (for instance, in virtual humans) can lead toconcomitant changes to the user’s challenge/threatmotivational state.AcknowledgmentsPeter Khooshabeh performed this research while onappointment as a U.S. Army Research Lab PostdoctoralFellow. We thank Kyle O’Donnell for help with analysis.ReferencesBarrett, L. F., Mesquita, B., & Gendron, M. (2011). Contextin Emotion Perception. Current Directions 63721411422522Beale, R., & Creed, C. (2009). Affective interaction: Howemotional agents affect users. InternationalJournal of Human-Computer Studies, 67(9), 755–776. doi:10.1016/j.ijhcs.2009.05.001Blais, C., Jack, R. E., Scheepers, C., Fiset, D., & Caldara, R.(2008). Culture shapes how we look at faces. PLoSONE, 3(8).Blascovich, J. (2008). Challenge and threat appraisal. InHandbook of Approach and Avoidance Motivation(pp. 432–444). CRC Press.Blascovich, J., & Mendes, W. B. (2010). Socialpsychophysiology and embodiment. In Handbookof Social Psychology (5th Edition., pp. 194–227).New York: Wiley.Blascovich, J., Mendes, W. B., Hunter, S. B., Lickel, B., &Kowai-Bell, N. (2001). Perceiver threat in socialinteractions with stigmatized others. Journal ofPersonality and Social Psychology, 80(2), 253–267.Blascovich, J., Vanman, E., Mendes, W., & Dickerson, S. S.(2011). Social psychophysiology for social andpersonality psychology. Los Angeles: SAGE.De Melo, C. M., Carnevale, P., & Gratch, J. (2011). Theeffect of expression of anger and happiness incomputer agents on negotiations with humans. InThe 10th International Conference on AutonomousAgents and Multiagent Systems - Volume 3 (pp.937–944). Richland, SC: International Foundationfor Autonomous Agents and Multiagent m?id 2034396.2034402Dienstbier, R. A. (1989). Arousal and physiologicaltoughness: Implications for physical and mentalhealth. Psychological Review, 96(1), 84–100.Jack, R. E., Blais, C., Scheepers, C., Schyns, P. G., &Caldara, R. (2009). Cultural confusions show thatfacial expressions are not universal. CurrentBiology, 19, 1543–1548.Lanzetta, J. T., & Englis, B. G. (1989). Expectations ofcooperation and competition and their effect onobservers’ vicarious emotional responses. Journalof Personality and Social Psychology, 56(4), 543–554.Meehan, M., Insko, B., Whitton, M., & Brooks, F. P.(2002). Physiological measures of presence instressful virtual environments (p. 645). ACM Press.doi:10.1145/566570.566630Mogg, K., Garner, M., & Bradley, B. P. (2007). Anxiety andorienting of gaze to angry and fearful 1016/j.biopsycho.2007.07.005Rienerman-Jones, L., Cosenzo, K., & Nicholson, D. (2010).Subjective and objective measures of operator statein automated systems. Presented at the AppliedHuman Factors and Ergonomics, Miami, FL.Singer, T., Seymour, B., O’Doherty, J. P., Stephan, K. E.,Dolan, R. J., & Frith, C. D. (2006). Empathicneural responses are modulated by the perceivedfairness of others. Nature, 439(7075), 466–469.doi:10.1038/nature04271Szczurek, L., Monin, B., & Gross, J. J. (2012). The StrangerEffect: The Rejection of Affective Deviants.Psychological Science, 23(10), 1105–1111.doi:10.1177/0956797612445314Tamietto, M., & De Gelder, B. (2010). Neural bases of thenon-conscious perception of emotional signals.Nature Reviews Neuroscience, 11(10), 697–709.doi:10.1038/nrn2889Tomaka, J., Blascovich, J., Kelsey, R. M., & Leitten, C. L.(1993). Subjective, physiological, and behavioraleffects of threat and challenge appraisal. Journal ofPersonality and Social Psychology, 65(2), 248–260. doi:10.1037/0022-3514.65.2.248Tracy, J. L., & Robins, R. W. (2008). The automaticity ofemotion recognition. Emotion, 8(1), 81–95.doi:10.1037/1528-3542.8.1.81Van Kleef, G. A., De Dreu, C. K. W., & Manstead, A. S. R.(2004). The interpersonal effects of anger andhappiness in negotiations. Journal of Personalityand Social Psychology, 86(1), 57–76.Van Kleef, G. A., De Dreu, C. K. W., & Manstead, A. S. R.(2010). An Interpersonal Approach to Emotion inSocial Decision Making. In Advances inExperimental Social Psychology (Vol. 42, pp. lsevier.com/retrieve/pii/S006526011042002X
Negotiation Strategies with Incongruent Facial Expressions of Emotion Cause Cardiovascular Threat Peter Khooshabeh (khooshabeh@ict.usc.edu) 1, 3 Celso de Melo (demelo@usc.edu) 2 Brooks Volkman (volkman@psych.ucsb.edu) 1 Jonathan Gratch (gratch@ict.usc.edu) 3 Jim Blascovich (blascovi@psych.ucsb.edu ) 1 Peter J. Carnevale (carnevale@usc.edu) 2
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