European Journal of Pain 15 (2011) 985.e1–985.e11

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European Journal of Pain

journal homepage: www.EuropeanJournalPain.com

Gender effects in pain detection: Speed and accuracy in decoding femaleand male pain expressions

Paolo Riva ⇑, Simona Sacchi, Lorenzo Montali, Alessandra FrigerioUniversity of Milano-Bicocca, Italy

a r t i c l e i n f o a b s t r a c t

Article history:Received 17 July 2010Received in revised form 4 February 2011Accepted 24 February 2011Available online 23 March 2011

Keywords:Pain expressionsEmotional expressionsGender differencesFace perceptionImplicit measures

1090-3801/$36.00 � 2011 European Federation of Intdoi:10.1016/j.ejpain.2011.02.006

⇑ Corresponding author. Address: Department oMilano-Bicocca, 1, Piazza Ateneo Nuovo, 20126 Mila3775; fax: +39 02 6448 3706.

E-mail address: paolo.riva1@unimib.it (P. Riva).

The ability to detect facial expressions of pain is crucial in eliciting prosocial behaviors towards theindividual experiencing pain. Previous studies have shown that the sufferers’ gender can affect theobservers’ explicit judgment of the pain face, thus suggesting its possible influence on pain decoding.The present study investigates whether the sufferer’s gender affects the observer’s reflexive or implicitdetection of facial expression of pain. More specifically, we used implicit measures to test whetherobservers detect pained expression more quickly or accurately on male or female faces. In threeexperimental studies, we devised a set of stimuli using computer-generated faces. In Experiment 1,prototypical female and male avatars with different facial expressions (pain, anger, disgust, and neu-tral) were displayed, while subjects’ (N = 34) accuracy and speed at identifying the expressions wererecorded. In Experiment 2, participants (N = 56) watched videos of the avatars displaying dynamicexpressions and had to quickly and accurately identify each expression. In Experiment 3, participants(N = 38) were shown an androgynous avatar face showing different expressions and were asked toidentify the face as either female or male. Overall, we found that the target’s gender affected theobserver’s reflexive decoding of the facial expression of pain. Specifically, the results showed that par-ticipants, regardless of their gender, were slower and less accurate in recognizing pain expressions(but not other expressions) on female faces. Furthermore, androgynous faces displaying painedexpressions were more likely to be categorized as male than female. Several potential explanationsare discussed.

� 2011 European Federation of International Association for the Study of Pain Chapters. Published byElsevier Ltd. All rights reserved.

1. Introduction

Since Darwin (1872), the pain face has been considered primar-ily for its communicative function (Craig, 1992; Hadjistavropoulosand Craig, 2004; Prkachin, 2009). Indeed, it often seems advanta-geous for the sufferer to have the chance to translate a distressinginternal state into a message that can be perceived by someone inthe environment (Prkachin et al., 1994; Simon et al., 2008). Re-search has found that the specific pain-related facial movements(Prkachin and Solomon, 2008) are consistent across genders (Kunzet al., 2006), ages (Hadjistavropoulos, 2005) and cognitive abilities(Kunz et al., 2007).

To be effective in its communicative function, the pain face,once encoded, needs to be detected by someone in the environ-ment. The facial expression of pain is highly salient to observers(Craig, 1992); it prompts them to provide support for the suf-

ernational Association for the Stud

f Psychology, University ofno, Italy. Tel.: +39 02 6448

ferer or to protect themselves from a threat (Williams, 2002;Yamada and Decety, 2009). Furthermore, observers need toidentify the pain face correctly and quickly in order to providerapid support or to receive immediate signals of a possiblealarm. Researchers have examined observers’ ability to discrim-inate the facial expression of pain from expressions of other ba-sic emotions (Simon et al., 2008). However, past research hasalso shown variance and errors in the observer’s ability to de-tect pain (Kappesser and Williams, 2002; Prkachin and Craig,1994), suggesting that further investigation of the variables thatcan bias the ability to detect the facial pain expression isneeded.

Several studies indicate that the sufferer’s social characteristics,including gender, ethnicity and age, can affect the observers’perception of pain. For instance, pain may be under-recognizedin older people and members of ethnically disadvantaged groups(Horgas and Elliott, 2004; Green et al., 2003). Gender may also af-fect the process. For instance, Robinson and Wise (2004) found thatobservers judged that women undergoing a cold pressor task expe-rienced more intense pain than men. Hirsh et al. (2008) showedthat computer representations of female faces were rated as suffer-

y of Pain Chapters. Published by Elsevier Ltd. All rights reserved.

985.e2 P. Riva et al. / European Journal of Pain 15 (2011) 985.e1–985.e11

ing more intense pain than representations of male faces. Thesefindings related to observers’ social judgment are consistent withpain-related gender role stereotypes (Hoffmann and Tarzian,2001), and they suggest that gender can bias the observer’s decod-ing of a target face.

However, studies have shown that explicit and reflective socialjudgment might diverge from implicit, or reflexive, cognitive pro-cesses and associations (Greenwald and Nosek, 2008; Sloman,1996). These latter are faster, they take place automatically andunintentionally, and they are less influenced by social desirability.In real life, facial pain cues are often received accidentally, andbehavioral responses can occur under uncertainty or time pres-sure; under these conditions, observers are more likely to rely onautomatic and implicit processes than controlled reasoning (Taitet al., 2009). Therefore, implicit methods can assess the involun-tary effects of the target’s gender on pain detection while bypass-ing explicit reasoning and social desirability concerns. Consistentwith these premises, the present study aimed to examine, usingimplicit measures, whether the sufferer’s gender affects the obser-ver’s speed and accuracy in detecting otherwise identical facialexpressions of pain.

2. Experiment 1

In Experiment 1, we investigated whether participants de-tected facial expression more quickly and/or accurately in femaleor male faces. Furthermore, we tested the distinctness of theinteraction between gender and the pain face versus otherthreat-relevant facial expressions. Anger and disgust were cho-sen as control stimuli because of their negative valence and theirthreat-relevant nature (Kappesser and Williams, 2002; Williams,2002).

As already mentioned, implicit methodology was used in thisand the following experiments. Implicit methods are appropriatein cases in which some sort of knowledge or schema influencesan observer’s cognition or behavior, but the observer might notbe aware of that knowledge and its influence. This methodologicalchoice was preferred because of the known role of automatic cog-nitive processes in pain decoding. Tait and colleagues (2009) ar-gued that observers often perceive sufferers’ pain underconditions of uncertainty. This phenomena fosters the use of anintuitive/heuristic cognitive system (also known as ‘‘System 1’’),which is fast, effortless and automatic, and which can ultimatelybias the observer’s ability to decode pain expressions. Consideringthe role of automatic cognitive processes in pain judgment, we re-corded observers’ reaction times and accuracy to determinewhether an association existed between the targets’ gender andthe observers’ perceptions of pain expressions. Through the accu-racy scores, we examined the interaction between gender andthe observer’s confusion of the pain face with the other negativeemotional expressions (i.e., misinterpreting pain as disgust or dis-gust as pain).

2.1. Method

2.1.1. ParticipantsParticipants were 34 undergraduate students (24 females, 10

males; mean age = 25 ± 5.6) at the University of Milano-Bicocca.They received class credit for participating. All participants wereCaucasian with normal or corrected-to-normal vision.

2.1.2. Stimuli and designWe adopted computer-generated avatar faces to present iden-

tical pain expressions and precisely control the expressive inten-sity of each face, in line with previous studies (Hirsh et al., 2008,2009). A series of 400 � 400 pixel computer avatars was gener-

ated. We used FaceGen 3.1, a computer software program thatallows the user to manipulate the facial movements of an avataralong with other features, such as its gender, age and ethnicgroup. Using this tool, we generated Caucasian faces that wereidentical to each other except for their gender and their facialexpressions, thus controlling all the other possible confoundingvariables (e.g., the degree of facial attractiveness, the gaze direc-tion, facial asymmetry, ethnicity and general facial morphology).Moreover, using computer-generated faces provided us with aunique way to objectively match the faces’ intensity of expres-sion; the exact same expression (in terms of the actions involvedand intensity of each movement) could be displayed on a femaleor a male avatar face. FaceGen 3.1 includes a series of pre-pro-grammed slides that allow the user to generate expressions ofanger, disgust, fear, happiness, sadness and surprise. A computerprogrammer was hired to add a pain slide (see Fig. S1, see theonline version at 10.1016/j.ejpain.2011.02.006) that includedthe facial movements, or action unities (AUs) involved in thepain face (AUs: 4, 6, 7, 10, and 43). The exclusive presence ofthese AUs and the specificity of the resulting pain face comparedwith other basic emotions were confirmed by three trainedjudges, who were blind to the purpose of the research, usingthe Facial Action Coding System (FACS) (Ekman and Friesen,1978). A subsequent study was conducted to determine whethernaïve observers perceived the new slide’s expression as pained.Thirty-one participants (58% females, mean age = 23 ± 2.43) werepresented with a series of eight avatar faces expressing pain, an-ger, disgust and neutrality. Participants had to identify eachexpression by choosing one out of eight options: fear, anger, dis-gust, sadness, shame, happiness, pain and neutrality. The resultsshowed that participants correctly identified the pain expressiongenerated with FaceGen 3.1 in 80.6% of the cases (50 times outof 62 cases). This pattern was similar to that for disgust (79% ofcorrect hits). Anger was correctly identified in 87% of cases,whereas the neutral expression was correctly identified in the93.5% of the total cases.

The resulting experimental design considered the following fac-tors: 2 (face gender: male vs. female) � 4 (expression: anger, dis-gust, neutral or pain) � 2 (participant’s gender: male vs. female),with the first two factors varied within-subjects and the last onebetween subjects.

2.1.3. ProcedureWe employed a speeded dichotomous decision task in which

individuals were presented a series of avatar faces on a com-puter monitor one at a time. Stimuli presentation and responsemeasurements in this and the subsequent experimental proce-dures were controlled by the software package e-Prime™(Schneider et al., 2002). The images were displayed at the centerof a 17-in. color monitor screen on a uniform, black backgroundwith a 75-Hz refresh rate. The level of brightness was consistentfor every image. Participants observed the monitor at a viewingdistance of about 50 cm without head restraint. They were in-structed that for each trial, they should quickly and accuratelyclassify avatar facial expressions as pained or ‘‘other’’ by pressingthe ‘‘D’’ or ‘‘K’’ key on the keyboard with their left and rightforefinger, respectively. During each trial, a fixation cross ap-peared on the screen; approximately 500 ms later, an avatar facewas presented. As soon as the participant provided his or her an-swer, the avatar face disappeared and another fixation cross ap-peared. Participants completed a training phase composed of 25trials and then judged 80 trials (10 stimuli for each condition).After each trial, participants received feedback regarding the cor-rectness of their responses. The stimuli were presentedrandomly.

Table 1Mean (standard deviations) of accuracy in decoding expression when showed byfemale or by male faces. Experiment 1.

Expression Face gender TOT

Female Male

Neutral 9.91a (0.38) 9.79a (0.73) 9.85 (0.55)Anger 9.74a (0.67) 9.56a (0.82) 9.65 (0.74)Disgust 8.79a (1.47) 8.18a (1.62) 8.49 (1.55)Pain 6.88a (1.49) 8.65b (1.43) 7.76 (1.46)

Note: Different letters indicate in each row statistical differences between male andfemale condition, according to the LSD test (p < .001).

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2.2. Results

2.2.1. AccuracyParticipants’ answers were classified as either correct (1) or

incorrect (0). We assigned a score of 1 when the pain expressionwas correctly detected and when expressions of anger, disgust orneutrality were perceived as ‘‘other’’; vice versa, we assigned 0scores when other expressions were identified as pain and painas other expressions. Overall, participants made 289 errors(11.89%) (M = 8.50, SD = 4.51). An accuracy index was computedas the sum of the correct answers.

Crucially, the 2 (face gender: male vs. female) � 4 (expression:anger, disgust, neutral or pain) � 2 (participant’s gender: male vs.female) ANOVA revealed an interaction effect between facialexpression and gender, F(1, 32) = 24.15, p < .001, g2

p ¼ :42. Morespecifically, the results showed that accuracy in detecting painexpressions significantly increased when pain was showed by amale face compared with a female one; there was no difference be-tween male and female faces and observers’ accuracy of identifyingthe other three expressions (see Table 1).

The analysis also yielded a main effect for expression,F(1, 32) = 38.26, p < .001, g2

p ¼ :54. As the post hoc analysesshowed, the expression of anger was misperceived (M = 9.65,SD = 0.74) to the same extent as the neutral expression (M = 9.85,SD = 0.55). The participants were less accurate in identifying dis-gust (M = 8.49, SD = 1.55), which is more similar to pain than theother two expression (Simon et al., 2008). Participants scored low-est on identifying the expression of pain (M = 7.76, SD = 1.46). Atthis regard, data on the type of errors participants made revealedthat 29.61% of pain expressions were classified as non-pain expres-sions, while 8.47% of non-pain expressions as pain expression.These results might arise from the nature of the task, which re-quired that pain be identified specifically, while the expressionsof anger, disgust and neutrality could be classified roughly into a‘‘not-pain’’ category.

The ANOVA did not reveal a main effect for participant’s gender,F(1, 32) = 1.11, p = .30 or interaction effects between participants’gender and the other factors, Fs(1, 32) < 2.50, ps > .12.

Table 2Mean (standard deviations) of participants’ latency time (in milliseconds) betweenthe expression presentation and the answer when correct. Experiment 1.

Expression Face gender TOT

Female Male

Neutral 785.18a (211.02) 848.69b (194.36) 816.94 (202.69)Anger 925.22a (297.04) 905.91a (205.50) 915.57 (251.27)Disgust 969.88a (281.03) 1068.23a (289.27) 1019.06 (285.15)Pain 937.81a (156.44) 866.49b (189.27) 902.15 (212.86)

Note: Different letters indicate in each row statistical differences between male andfemale condition, according to the LSD test (p < .05).

2.2.2. LatencyAfter the analysis on the level of answer accuracy, we consid-

ered the latency time between the stimuli presentation and theparticipants’ answer, for correct answers only. Reaction times(RT) were filtered at 2 standard deviations (calculated for each con-dition). This procedure eliminated 5.51 % of the total responses.

Consistent with the analysis on accuracy, statistics revealed asignificant interaction effect between expression and face gender,F(1, 26) = 3.29, p < .03, g2

p ¼ :11. As Table 2 shows, the face genderdid not affect the participants’ RT in classifying anger and disgust;however, when presented with a neutral expression, participantswere faster in answering when the avatar had a female appearancethan when it was male; conversely, when presented with a painexpression, participants were faster in identifying the stimuluswhen the avatar had a male than female appearance (LSD test:ps < .05).

The statistical analysis did not reveal a main effect of partici-pants’ gender, F(1, 26) = 3.26, p = .08: in decoding facial expres-sions, female participants (M = 871.01, SD = 208.09) were as fastas males (M = 1004.04, SD = 269.39). Moreover participants’ genderinteracted with face gender, F(1, 26) = 5.72, p = .02, g2

p ¼ :18: fe-male participants (M = 899.32, SD = 225.36) were as fast as maleparticipants (M = 978.83, SD = 224.15) when they had to identifymale expressions but male participants (M = 1029.25,SD = 283.67) were slower than female participants (M = 842.69,SD = 191.33) when they were asked to decode expressions showedby female faces. The type of expression showed by the faces signif-icantly affected the participants RT, F(1, 26) = 14.16, p < .001,g2

p ¼ :35: participants were relatively faster in classifying neutralexpressions (M = 849.21, SD = 202.69) and relatively slower in clas-sifying disgust expressions (M = 1054.93, SD = 285.15). Analysis didnot yield the avatar face gender main effect, F(1, 26) = .02, p = .89.

The three-way interaction effect among participants’ gender,avatar gender and expression was not significant, F(1, 26) = 1.85,p > .18.

Overall, Experiment 1 provided some preliminary evidence thatpain was more salient in male faces at the reflexive, implicit level.Participants detected pain on male faces more accurately than onfemale faces. Furthermore, they identifying the pain face morequickly when the avatar had a male face than when it had a femaleone. These results were specific to the pain face, as shown by theanalyses of the other expressions.

3. Experiment 2

In Experiment 2, we aimed to replicate the findings of Experi-ment 1 using dynamic stimuli instead of static facial displays. Spe-cifically, Experiment 2 was meant to investigate the threshold atwhich observers could correctly identify a pain expression as afunction of the gender of the face displaying it. Based on the find-ings of Experiment 1, we expected that female faces needed to dis-play higher magnitudes of pain to be detected by observers.

3.1. Method

3.1.1. ParticipantsFifty-six Milano-Bicocca university students (33 females; mean

age = 23 ± 2.8) were recruited to participate in a study on theperception of facial expressions. They received class credit forparticipating. All subjects were Caucasian and had normal or cor-rected-to-normal vision.

3.1.2. Stimuli and designThe participants were shown series of 16-s dynamic visual

stimuli (video) of male and female avatar faces displaying anger,

Table 3Mean (standard deviations) of participants’ latency time (in milliseconds) betweenthe expression presentation and the answer when correct. Experiment 2.

Expression Face gender TOT

Female Male

Anger 10003.98a (500.99) 10724.702a (470.86) 10523.10 (2767.14)Disgust 9199.50a (428.03) 11458.09b (500.112) 10361.98 (2913.40)Pain 11318.68a (436.14) 9829.29b (580.06) 10771.91 (2637.84)

Note: Different letters indicate in each row statistical differences between male andfemale condition, according to the LSD test (p < .001).

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disgust or pain. Each video was created by pooling 100 frames rep-resenting facial expressions with increasing intensity (the degreeof increase was controlled and matched for each expression usingthe FaceGen 3.1 controls). Each video started with a neutral faceand moved progressively through the sequence of 100 frames,reaching the maximum amount of the three expressions after 16 s.

The experimental design considered the following factors: 2 (facegender: male vs. female) � 3 (expression: anger, disgust or pain) � 2(participant’s gender: male vs. female), with the first two factors var-ied within-subjects and the last one between subjects.

3.1.3. ProcedureThe 16-s dynamic visual stimuli were presented on a computer

monitor one at a time in a randomized order. The images were dis-played at the center of the screen of a 17-in. monitor, with a uni-form black background and a 75-Hz refresh rate. The level ofbrightness was consistent for every image. Participants viewedthe monitor at a distance of about 50 cm without head restraint.Participants were asked to watch each video and identify the facialexpressions as quickly and accurately as possible. They were in-structed to press the space bar on the computer keyboard as soonas they thought they had identified the expression displayed by theavatar face. The face disappeared when the space bar was pressed,and the participant had to indicate which of the three expressionss/he had identified by pressing a designated key. Participants com-pleted a training phase composed of two trials, then judged 12 tri-als (two stimuli for each condition). Participants were givenfeedback about the correctness of their response after each trial.

3.2. Results

The scores for reaction times and accuracy were subjected to a 2(face gender: male vs. female) � 3 (expression: anger, disgust orpain) � 2 (participant’s gender: male vs. female) ANOVA. Reactiontime was the crucial dependent variable (because it directly re-flects the detection threshold), and accuracy was not independentof reaction times (e.g., higher latency corresponds with higherexpression intensity and, consequently, an easier task); however,we report the analyses of reaction times and accuracy separatelyfor improved clarity.

Table 4Mean (standard deviations) of accuracy in decoding expression when showed byfemale or by male faces. Experiment 2.

Expression Face gender TOT

Female Male

Anger 1.59a (0.72) 1.18b (0.95) 1.40 (0.65)Disgust 1.68a (0.74) 1.14b (0.69) 1.41 (0.71)Pain 1.15a (0.93) 1.20a (0.93) 1.19 (0.68)

Note: Different letters indicate in each row statistical differences between male andfemale condition, according to the LSD test (p < .001).

3.2.1. LatencyWe recorded the time between the beginning of the video and

when the participants pressed the space bar and selected casesin which participants correctly identified each expression. Reactiontimes (RT) were filtered at 2 standard deviations (calculated foreach condition). This procedure eliminated 2.73% of the totalresponses.

Facial gender interacted with the type of expression,F(1, 60) = 10.058; p < .001; g2

p ¼ :251. As shown in Table 3, threepatterns of results were found. The face’s gender did not affect par-ticipants’ RT for accurately decoding an expression of anger. Partic-ipants decoded disgust more quickly when it was displayed by afemale face compared with a male face. In contrast, and consistentwith the findings on static facial displays in Experiment 1, partici-pants identified dynamic pain expressions more quickly on maleavatar faces than female ones.

Neither the face’s gender (F(1, 30) = 1.68, p = .204) or of the typeof expression (F(1, 60) = .27, p = .762) had a significant main effecton participants’ RT.

The statistical analysis did not reveal a main effect for partici-pants’ gender, F(1, 30) = 1.16, p = .289. No interaction effects werefound between participants’ gender and the other factors,Fs(1, 30) < 1.52, ps > .22.

3.2.2. AccuracyParticipants’ answers were then classified as either correct (1)

or incorrect (0). Overall, participants made 222 errors (M = 3.96,SD = 2.41; 33.03% of the total; Table S1, see the online version at10.1016/j.ejpain.2011.02.006 for the exact type of errors made).An accuracy index was computed as the sum of the correctanswers.

In line with the results of Experiment 1, a significant interactionbetween face gender and expression was present, F(2, 108) = 7.69,p < .001, g2

p ¼ :12. As shown in Table 4, participants were moreaccurate in decoding the expression of anger and disgust on femalerather than male faces. However, the higher accuracy of detectingfacial expression on a female face dropped when avatars displayeda pain expression.

There was a significant main effect of the face gender on accu-racy level, F(1, 54) = 25.04, p < .001, g2

p ¼ :32. Post hoc analyses re-vealed that accuracy was greater when the expression wasdisplayed by a female (M = 1.47, SD = 0.57) than a male target(M = 1.17, SD = 0.67).

The analysis also yielded a main effect of expression,F(2, 108) = 5.14, p = .008, g2

p ¼ :08. More specifically, participantswere less accurate in decoding the expression of pain (M = 1.18,SD = 0.70) than anger (M = 1.39, SD = 0.65) and disgust (M = 1.41,SD = 0.79). No significant differences emerged between the two lat-ter factors.

The ANOVA revealed neither a main effect of participant’s gen-der, F(1, 54) = 1.49, p = .23, nor interaction effects between partici-pants’ gender and the other factors, Fs(1, 54) < .58, ps > .55.

In sum, Experiment 2 confirmed Experiment 1’s findings thatthe male pain face is more salient than the female pain face atthe implicit level. Results indicated that dynamic expressions ofpain – unlike other negative emotions – were judged as painedmore quickly if they were displayed by a male face. The null resultsfor the accuracy score do not contradict the findings for reactiontime. Indeed, at the moment participants identified an emotionin the dynamic display, the mean pain expression intensity dis-played by female avatars was likely to be higher than that of maleavatars. Thus, we obtained the same accuracy scores for male andfemale faces, but the female pain expressions were easier to detectbecause they were watched for a longer time and were thereforemore intense.

Table 5Mean (standard deviations) of number of female categorizations by type of expressionand intensity level. Experiment 3.

Expression Intensity

SUM Low Medium High

Anger 5.52a (3.04) 1.95a (1.05) 1.75a (1.08) 1.82a (1.09)Disgust 2.98b (2.68) 1.44b (1.16) 0.98b (1.00) 0.56b (0.99)Pain 1.12c (1.93) 0.51c (0.85) 0.33c (0.72) 0.28c (0.56)Neutral 7.22d (2.45)

Note: Different letters indicate in each column statistical differences among eachexpression, according to the LSD test (p < .05).

P. Riva et al. / European Journal of Pain 15 (2011) 985.e1–985.e11 985.e5

4. Experiment 3

Experiment 3 was designed to further test the prediction thatthe facial expression of pain and the target’s gender interact atthe implicit level. The findings from Experiments 1 and 2 revealedthat perceivers detect pain on male faces more quickly and accu-rately than on female faces. Reversing the perspective of these pre-vious experiments, we predicted that detecting a facial expressionof pain would lead observers to perceive an androgynous target asmore masculine.

4.1. Method

4.1.1. ParticipantsParticipants were 39 undergraduate students (29 females, 10

males; mean age = 25 ± 5.31) at the University of Milano-Bicocca.They received class credit for participating. All participants wereCaucasian with normal or corrected-to-normal vision.

4.1.2. Stimuli and designTen versions of an androgynous avatar face were generated using

FaceGen 3.1. The ambiguous stimuli were created by setting thegender bar at the midpoint between the male and female poles. A pi-lot study with 21 participants (56% females, mean age = 22 ± 2.10)was conducted to test that the adopted avatar face conveyed similardegrees of perceived masculinity and femininity. Participants ratedslightly different versions of the androgynous face on a scale from 0(completely masculine) to 10 (completely feminine). For the study,we selected the avatar faces whose mean scores were not statisti-cally different from the middle point of the scale, t(1, 20) = 1.60,p = .13.

The avatars’ facial movements were then manipulated to gener-ate four different facial expressions: neutrality, anger, disgust andpain (see Fig. S2, see the online version at 10.1016/j.ej-pain.2011.02.006). For an in-depth investigation of the hypothesis,we also manipulated the intensity of each facial expression. Thestimuli differed according to the intensity level displayed: intensitywas categorized as low (corresponding levels of the built-in Face-Gen 3.1 controls: 0.1, 0.2, 0.3), medium (0.4, 0.5, 0.6, 0.7) or high(0.8, 0.9, 1). The crucial stimuli set consisted of three images percondition, resulting in a total of 36 trials. We also included fillers(12 male avatars and 12 female avatars, varied by expression andintensity level) to increase the variability of the stimuli shown toparticipants.

The number of ‘‘female’’ answers and the latency time weresubjected to a 3 (expression: pain, anger or disgust) � 3 (intensitylevel: low, medium or high) � 2 (participant’s gender: male vs. fe-male) ANOVA, with the first two factors varied within-subjects andthe last one between subjects. The factorial design was incompletebecause the neutral expression’s intensity was not manipulable(the nine neutral faces did not differ by degree of expressionintensity).

4.1.3. ProcedureWe employed a speeded dichotomous decision task. Respon-

dents were presented with a series of computer-generated avatarfaces displayed at the center of the screen of a 17-in. monitor, ona uniform, black background with a 75-Hz refresh rate. The levelof brightness was consistent for every image. Participants viewedthe monitor from a distance of about 50 cm without head restraint.They were asked to categorize each target as female or male bypressing two corresponding keys on the computer keyboard usingtheir left and right forefingers, respectively. The stimuli were pre-sented randomly, and participants were instructed to respond asquickly and accurately as possible.

4.2. Results

4.2.1. Gender classificationThe gender categorization of androgynous stimuli was ana-

lyzed. We assigned a score of 0 to male answers and 1 to femaleanswers.

The analysis yielded a significant main effect for expression,F(1, 37) = 58.77, p < .001, g2

p ¼ :61 (see Table 5). Supporting our pre-diction, the lowest number of female answers was associated withpain expressions (M = 0.37, SD = 0.71), followed by disgust(M = 0.99, SD = 1.05) and anger expressions (M = 1.84, SD = 1.07).The analysis also yielded a main effect for expression level of inten-sity, F(1, 37) = 9.54, p < .01, g2

p ¼ :20. Participants classified theambiguous stimuli as female more frequently at low levels of inten-sity (M = 1.30, SD = 1.02) than at medium (M = 1.02, SD = 0.94) andhigh levels of intensity (M = 0.89, SD = 0.88).

In the previous analysis, we did not consider the neutral expres-sions because their intensity could not be manipulated. However,we compared the total number of ‘‘female’’ answers by expressiontype independent of intensity level using a 4-level within-subjectANOVA (expression: neutral, anger, disgust or pain). The analysisconfirmed a significant effect for expression type, F(1, 38) = 88.99,p < .001, g2

p ¼ :70. The ambiguous stimuli showing neutral expres-sions obtained the highest number of ‘‘female’’ responses(M = 7.22, SD = 2.45) compared with the other three expressions,p < .001 (see Table 5). The ‘‘female’’ responses to the other threeexpressions remained significantly different.

The ANOVA did not reveal a main effect for participant gender,F(1, 37) = 0.02, p = .87 or interaction effects between participantgender and the other factors, Fs(1, 37) < 2.58, ps > .12.

These results replicate those of Experiments 1 and 2 and showthat decisions about a face’s gender and its emotional expressionsof pain are not independent. Therefore, we found that androgynousfaces displaying pain were considered more masculine than femi-nine when the images were controlled for other negative emotions.

5. General discussion

The detection of pain in others is a highly salient event foronlookers (Craig, 1992; Williams, 2002). However, this strong sal-ience can be moderated by the target’s social characteristics. In-deed, consistent with pain-related gender role stereotypes(Hoffmann and Tarzian, 2001) early studies suggested that gendercan actually affect the observers’ explicit judgment of pain on fe-male and male target (see Hirsh et al., 2008). Yet, past researchhas also shown that explicit social judgment might diverge fromimplicit, or reflexive, cognitive processes and associations (Green-wald and Nosek, 2008; Sloman, 1996). Whereas explicit judgmentscan be biased by a wide range of factors, such as response biases,faking, motivation and opportunity (e.g., the motivation to controlprejudice) implicit processes tend to be more free of response fac-tors such as social desirability and faking tendencies (Greenwaldet al., 2002). Moving from these premises, in the present study,

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we found that the target’s gender affected the observer’s reflexivedecoding of the facial expression of pain. More specifically, wefound that observers’ ability to detect pain in a female face waslower than their ability to detect pain in male faces. This tendencywas consistent across three experiments and represented a uniqueresponse to pain expressions compared with a baseline (e.g., neu-tral expression) or other negative facial expressions (e.g., anger anddisgust).

Overall, the results showed that male pain faces were more eas-ily processed at the reflexive level. In Experiment 1, participantsconfused the pain expression with other expressions to a higherdegree in female faces than male faces. Experiment 2 showed thatparticipants took longer to correctly identify a dynamic painexpression on a female face compared with a male face. Becausethe dynamic stimuli showed facial expressions of increasing inten-sity, a more intense pain expression was needed for participants tocorrectly identify pain on a female face, whereas lower-intensitypain expressions were sufficient to detect suffering on male faces.Finally, Experiment 3 showed that participants tended to perceivean androgynous face as less feminine when it displayed pain thanother negative emotions.

Although our experimental design did not allow us to identifythe underlying mechanism, several parallel and convergent expla-nations might be pointed out. Further research should directlyinvestigate the processes that determine the higher salience ofmale pain face at the implicit level.

First, we can speculate that the observers’ ability to decode painfaces might depend upon gender differences in pain behavior,which implies different exposures to male and female pain faces.Indeed, research has reported significant gender differences inthe perception, experience and expression of pain. A large amountof clinical literature indicates that women more frequently experi-ence and are more intensely affected by a large number of painsyndromes – both chronic and acute – and they describe the painas more intense, widespread, and of a greater duration than mendo (Dao and LeResche, 2000; Heitkemper and Jarrett, 2001; Morinet al., 2000; Robinson et al., 1998; Rollman and Lautenbacher,2001; Unruh, 1996). Experimental research has found that womenexhibit significantly greater sensitivity to pain and lower pain tol-erance (Fillingim and Maixner, 1995; Wise et al., 2002). For thatreason, even though men and women do not seem to differ in theirfacial configurations in response to pain stimuli (Kunz et al., 2006),it might be that onlookers are more frequently exposed to facialpain expressions on female rather than male faces precisely be-cause women suffer more from a larger variety of pain syndromes.Studies on pain judgment have reported the systematic tendencyof observers who are more exposed to patients in pain to underes-timate the pain those patients experience (Marks and Sachar,1973; Marquié et al., 2003). The commonly referenced socio-cogni-tive process is thought to result from habituation; that is, the con-sequence of being repeatedly exposed to others’ suffering might bea diminished sensitivity to pain in others (Prkachin et al., 2007).

Habituation has received empirical support (Prkachin and Ro-cha, 2010; Prkachin et al., 2004), although boundary conditionshave been pointed out (i.e., the observer’s accuracy can increaseafter spending more time with the person in pain) (Miaskowskiet al., 1997). Taking into account the habituation bias, a greaterexposure to females’ pain expressions might lead to a decreasedsensitivity to pain displayed by women. Indeed, Experiments 1and 2 showed that participants took longer and needed more in-tense pain expressions to correctly identify pain on female com-pared with male faces. Future studies should investigate whetherhabituation underpins observers’ impaired ability to detect femalepain compared with male pain. Observers could be experimentallyexposed either to a male or a female face for a specific length oftime, similar to the design used by Prkachin and Rocha (2010), to

determine whether length of exposure produces different out-comes. However, this possible explanation is inconsistent withour findings related to anger detection under the control condition.It is well known that people are more exposed to male expressionsof anger than female; according to the habituation hypothesis, wewould expect greater speed and accuracy in identifying anger on afemale face.

As a potential parallel to the habituation bias (e.g., decreasedsensitivity to female pain faces resulting from greater exposureto them), top-down inhibition processes might have contributedto observers’ impaired ability to decode female pain faces. Stereo-types are known to affect the way observers perceive a target face(Hugenberg and Bodenhausen, 2004; Hugenberg and Sacco, 2008).The psychology literature has widely shown that stereotypes biasthe processing of potentially ambiguous information in a stereo-type-consistent manner across multiple domains (Darley andGross, 1983; Duncan, 1976). Consistent with the notion that ex-pected gender differences in facial expressivity can be promptedby gender stereotypes, a recent theory of pain underestimationposits that female gender is an invalidating factor in pain judg-ment: given the stereotypical views of women as dramatizing,observers’ certainty about a women’s pain might be reduced (Taitet al., 2009). Accordingly, our study shows higher error rates inperceiving pain expression in females than in males.

Further evidence of gender influences in observers’ ability to de-code pain expressions comes from brain research. Simon et al.(2006) found the neurological activation of the observer’s brain de-pended upon the gender of the person expressing pain, but notupon the observer’s gender. In their study, the authors providedfMRI support for their hypothesis that the ‘‘implicit processing ofmale pain expression triggers an emotional reaction characterizedby a threat-related response’’ (p. 309). Indeed, several activationsinduced by male facial displays were significantly decreased whenobservers watched female facial displays of pain. Observing maleactors expressing pain activated several areas known for athreat-related response, like the ventromedial prefrontal cortex,SII/posterior insula and anterior insula. However, several areasactivated by male facial displays – including somatosensory areas,the amygdala and the perigenual ACC – registered a significant de-crease when observers watched female facial displays of pain. Theauthors argued that the interaction between the pattern of neuralactivation and actor’s gender might be due to the social communi-cative value of pain conveyed by male and female faces. A malepain expression may be more strongly linked with potentiallythreatening situations than a female pain expression (LeDoux,2000), activating the fight-flight system in the brain of the obser-ver. Indeed, previous scholars suggested that pain can foment adisposition towards aggression (Berkowitz, 1993), and recentempirical data confirmed that pain increases aggressive tempta-tions in humans (Riva et al., 2010). Because men are physically lar-ger and stronger and therefore more dangerous on average thanwomen, detecting a male in pain might pose a threat particularlysalient. Simon et al.’s (2008) results are consistent with our find-ings that observers are selectively (compared to neutral and angerexpression) more accurate and faster at decoding pain displayed bymale faces. Moreover, Experiment 3 showed that under conditionsof ambiguity (e.g., androgynous faces), participants perceived thepain expressions as more masculine. Again, this might be the con-sequence of a pain detection system that is selectively biased todetect any possibility of a relevant threat in the environment. Thus,the tendency to perceive a gender-ambiguous pain expression asmasculine may rely upon the higher cost of failing to detect painin a male compared with a female. In other words, because the costof overdetecting pain in a male face might be lower than the cost ofa missing it, the observer’s judgment may be biased toward deci-phering the expression of pain on an androgynous face as mascu-

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line (for a discussion of the error management system that mightfavor a bias for false alarms over misses, see Haselton and Buss,2000).

The main advantages of the study were twofold. First, the use ofimplicit methods allowed us to assess the involuntary effects of thetarget’s gender on pain detection while bypassing social desirabil-ity issues. Furthermore, in many naturalistic circumstances, paincues are unclear and treatment decisions have to be made underconditions of uncertainty or time pressure. Under these conditions,decision-makers rely on automatic, heuristic and implicit pro-cesses rather than on more laborious reasoning (Tait et al., 2009).Our methodological choice also accounted for seemingly conflict-ing past results, which were based on explicit methods and indi-cated that female faces are judged to express greater painintensity than male faces (Hirsh et al., 2008). These results are con-sistent with explicit, stereotypical views that women are moreprone to express their pain (Hoffmann and Tarzian, 2001), yet theycan diverge from the implicit and automatic cognitive processesinvolved in pain decoding. Second, the use of computer-generatedavatars allowed us to create stimuli with identical facial expres-sions but different genders. However, the use of computer-gener-ated faces might limit the generalizability of the current study’sfindings; although they allowed us to achieve greater experimentalcontrol, their use might have limited the external validity of thestudy. Thus, these findings should be interpreted with caution be-cause we do not yet know the degree to which findings from com-puterized stimuli are generalizable to human faces. Furtherreplication with different stimuli and samples is warranted. More-over, the influence of other social factors, such as age, race and eth-nicity, should also be investigated.

With regard to observers’ gender, neither main nor interactioneffects have been consistently found during the three experiments.The only analysis that showed influence of observers’ gender re-vealed that female participants were faster than males in decodinganger, disgust and neutral expression. These results are in line withthe notion that female are generally better than males at basic faceperception (McBain et al., 2009; McClure, 2000). However, in linewith previous research (Simon et al., 2006), we did not find anyinteraction between observers’ gender and the specific expressionof pain. Nevertheless, failures to find any consistent effect relatedto the participants’ sex might be due to a lack of power from aninsufficient number of subjects or to the uneven distribution ofparticipants’ sex in our samples.

In terms of clinical implications, our findings are in keepingwith evidence that women are less likely to receive treatment forpain than men (Calderone, 1990; McDonald, 1994; Hoffmann andTarzian, 2001). In this sense, observers’ impaired ability to detectthe female pain face could lead them to disregard the sufferer’sexperience and needs and fail to provide adequate care.

6. Conflict of Interest

The authors have no conflict of interest with respect to thisarticle.

Acknowledgements

We would like to thank Eric D. Wesselmann for comments onearlier versions of this paper. We also thank Shannon Lapsleyand Shajuan Jackson for proof reading the article.

Appendix A. Supplementary material

Supplementary data associated with this article can be found, inthe online version, at doi:10.1016/j.ejpain.2011.02.006.

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Table S1Frequencies and percentages (in parenthesis) of hits and errors the participants madein Experiment 2.

Overall Male target Female target

PainTotal number of responses 220 111 109Correct answers 133 (60.4%) 68 (61.2%) 65 (59.6%)Judged as anger 50 (22.7%) 29 (26.1%) 21 (19.2%)Judged as disgust 37 (16.8%) 14 (12.6%) 23 (21.1%)

AngerTotal number of responses 223 112 111Correct answers 184 (82.5%) 94 (83.9%) 90 (81.0%)Judged as pain 20 (8.9%) 9 (8.0%) 11 (9.9%)Judged as disgust 19 (8.5%) 9 (8.0%) 10 (9.0%)

DisgustTotal number of responses 220 110 110Correct answers 133 (60.4%) 67 (60.9%) 66 (60%)Judged as anger 61 (27.7%) 33 (30.0%) 28 (25.4%)Judged as pain 26 (11.8%) 10 (9.0%) 16 (14.5%)

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Fig. S1. Examples of female and male stimuli used in Experiments 1 and 2 to manipulate each facial expression (from the top: neutral, anger, disgust and pain expression).

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Fig. S2. The four androgynous faces adopted in Experiment 3.

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