Adults’ responsiveness to children’s facial expressions

Journal of Experimental Child Psychology 135 (2015) 56–71

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Adults’ responsiveness to children’s facial expressions Chinmay Aradhye ⇑, Jennifer Vonk, Danielle Arida Department of Psychology, Oakland University, Rochester, MI 48309, USA

a r t i c l e

i n f o

Article history: Received 9 July 2014 Revised 13 February 2015

Keywords: Young children’s expressions Emotion Responsiveness Cuteness Nurture Parenting

a b s t r a c t We investigated the effect of young children’s (hereafter children’s) facial expressions on adult responsiveness. In Study 1, 131 undergraduate students from a midsized university in the midwestern United States rated children’s images and videos with smiling, crying, or neutral expressions on cuteness, likelihood to adopt, and participants’ experienced distress. Looking times at images and videos along with perception of cuteness, likelihood to adopt, and experienced distress using 10-point Likert scales were measured. Videos of smiling children were rated as cuter and more likely to be adopted and were viewed for longer times compared with videos of crying children, which evoked more distress. In Study 2, we recorded responses from 101 of the same participants in an online survey measuring gender role identity, empathy, and perspective taking. Higher levels of femininity (as measured by Bem’s Sex Role Inventory) predicted higher ‘‘likely to adopt’’ ratings for crying images. These findings indicate that adult perception of children and motivation to nurture are affected by both children’s facial expressions and adult characteristics and build on existing literature to demonstrate that children may use expressions to manipulate the motivations of even non-kin adults to direct attention toward and perhaps nurture young children. Ó 2015 Elsevier Inc. All rights reserved.

⇑ Corresponding author. E-mail address: [email protected] (C. Aradhye). http://dx.doi.org/10.1016/j.jecp.2015.02.006 0022-0965/Ó 2015 Elsevier Inc. All rights reserved.

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Introduction Human children experience the longest period of dependency on caregivers of all known species (Bogin, 1997). A child’s ability to communicate with potential caregivers, therefore, is critical for survival (Bogin, 1997; Trivers, 1974) and has been studied extensively. Prior research has focused on three distinct aspects of children’s communication: gaze, vocalization, and facial expression (Yale, Messinger, Cobo-Lewis, & Delgado, 2003), with facial expression considered the most important aspect of communication (Colonnesi, Zijlstra, van der Zande, & Bögels, 2012; Messinger, 2002). The literature on children’s facial expression has focused primarily on the accurate detection of expressions, and the connection between emotions and expressions identified via specific facial patterns (Ekman & Rosenberg, 1997), whereas parents’ perception of a child’s facial expressions and the resulting effect on parents’ behavior have received relatively less attention (Bolzani-Dinehart et al., 2005; Braungart-Rieker, Garwood, Powers, & Wang, 2001). This disparity may exist because the dominant perspective in the developmental literature holds that facial expressions function as ‘‘feedback’’ signals of children’s emotional states rather than evoking behavioral motives in observers, thereby ignoring the role of observers in the function of children’s expressions (see Ekman & Fridlund, 1987; Fridlund, 1997) and children’s varying abilities to manipulate the behaviors of observers. Researchers adopting a behavioral–ethological approach (Dawkins & Krebs, 1978; Fridlund, 1997) have proposed species-specific signals that evoke behavioral motives in the receivers—in this case adults. Here we focused on the effects of children’s expressions on unrelated young adults’ attentiveness and motivation to nurture. We argue that (a) children’s expressions of emotion affect adults differently based on individual characteristics of the adults such as empathy and femininity; (b) both male and female adults are motivated to attend more to smiling children than to crying children and indicate greater desire to nurture smiling children; and (c) children’s expressions can be understood as a means by which nonrelated adults can be manipulated into providing attention and care to children. Previous research suggests that children’s ability to express emotions is critical in communicating with adult caregivers. The facial expressions of smiling and crying are understood as responses to positive and negative stimuli, respectively (Camras, 1992), communicating the emotional state of the child (Ekman & Fridlund, 1987; Fridlund, 1997), and conveying the child’s need for resources to potential caregivers (Trivers, 1974). Yale and colleagues (2003) compared the sequence and frequency of gaze, vocalizations, and facial expressions used by children toward their mothers. They found that gaze and vocalizations coordinated more strongly with facial expressions than with each other. This finding suggests a central coordinating role for facial expressions in children’s communication. Power, Hildebrandt, and Fitzgerald (1982) found that children’s expressions affected adults’ attention to children, cuteness perception, and cardiac acceleration, indicating that children’s expressions affect important aspects of adult physiology, cognition, and behavior. Although the expressions of adolescents and adults likely evoke similar responses, the effective use of expressions as a means of communication is likely most important for infants and young children. Because human children are dependent on caregiving parents, the care they receive from parents can be vital to development (Bogin, 1997; Geary & Bjorklund, 2000), making potential manipulation of caretaking behaviors an important developmental mechanism. Here, we extended previous findings to examine the effect of children’s expressions on adult nurturing motivations along with more negative outcomes such as adults’ distress (Gondoli & Silverberg, 1997). Children’s emotional expressiveness is positively associated with increased attachment with caregivers—an important aspect of parental caregiving. For instance, children’s ability to respond appropriately to affect-evoking stimuli (affect regulation) through facial cues predicts child–parent attachment over time (Braungart-Rieker et al., 2001; Cohn & Campbell, 1992; Cohn, Campbell, & Ross, 1991). Furthermore, Braungart-Rieker and colleagues (2001) compared infant crying and smiling reactions in the still-face paradigm (Tronick, Als, Adamson, Wise, & Brazelton, 1979) where parents abruptly freeze their faces, interrupting their expressions toward the infant, and then reintroduce expressions later. Children typically respond with crying to the face-freezing and with smiling on reintroduction of more dynamic cues. Braungart-Rieker and colleagues found that children with fewer crying responses

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and more smiling responses at 4 months of age showed more secure attachment at the 1-year mark compared with children with more neutral instances (when there was no reaction) and more crying instances (when there were less appropriate responses). Children’s ability to respond appropriately, therefore, was related to attachment, insinuating that the children’s appropriate emotional reactions engendered greater positive regard and bonding from adults. Although parents may be uniquely motivated to respond to children given facial similarities that promote kin recognition (Platek, Burch, Panyavin, Wasserman, & Gallup, 2002), a substantial literature has studied non-kin adults’ (hereafter adults’) perception of children’s facial cues (Lobmaier, Sprengelmeyer, Wiffen, & Perrett, 2010; Sprengelmeyer et al., 2009; Sternglanz, Gray, & Murakami, 1977). This focus has been useful in understanding basic communication mechanisms such as adults’ perception of underlying children’s emotions and cuteness as well as adults’ neural activation (Sprengelmeyer et al., 2009). For example, Bolzani-Dinehart and colleagues (2005) compared adults’ ratings of natural and digitally modified images of smiling and crying children on emotional intensity displayed from facial expressions. They found that adults use specific cues such as eye constriction, lip movement, and mouth opening to determine the intensity of specific emotions. Studying adult perception of children’s expressions can provide insights into the function of children’s expressions and can contribute to the understanding of evolutionary mechanisms involved in the child–adult relationship (Fridlund, 1997; Schmidt & Cohn, 2001). Furthermore, research on adults’ perception of children’s expressions indicates that adults might have a general responsiveness to unrelated children’s facial cues and expressions such that unrelated children also evoke caretaking mechanisms (Ainsworth, 1979; Bell & Ainsworth, 1972; Kringelbach et al., 2008; Sprengelmeyer et al., 2009) and smiling and crying convey positive and negative feelings, respectively (Hildebrandt, 1983; Messinger, Fogel, & Dickson, 2001). It is noteworthy that these signals of emotional states were recognized and responded to by unrelated adults. Because facial resemblance acts as an investment motivator in parents, especially in men (Volk & Quinsey, 2002), such that one’s own kin may evoke a stronger protective response over and above a general protective reaction toward young children (Sanefuji, Ohgami, & Hashiya, 2007), the effects of a child’s emotional expressions on nurturing behavior could be most pronounced when responding to one’s own child. However, consistent with the idea that children’s expressions are recognized and responded to by non-kin adults as well (Glocker et al., 2009a,b; Kurdahi Badr & Abdallah, 2001), we argue that mechanisms for manipulating adults’ motivations to engage in caretaking should be target general rather than aimed specifically at kin only. Human societies are clearly unique in the degree of shared caretaking given child-care facilities and adoption practices; thus, human children may be uniquely geared to soliciting care from non-kin as well as kin. In support of this idea, using functional magnetic resonance imaging (fMRI), Glocker and colleagues (2009b) found that nulliparous women showed increased brain activity in the mesocorticolimbic system (reward center) when viewing unfamiliar, digitally enhanced cute children’s faces. Similarly, Kringelbach and colleagues (2008) used magnetoencephalography (MEG) to identify specific activation in the medial orbitofrontal cortex (involved in rewarding behavior) when exposed to unfamiliar children’s faces but not when exposed to adult faces, suggesting that adult responsiveness mechanisms may be activated even by non-kin children, although possibly to a weaker extent (Leibenluft, Gobbini, Harrison, & Haxby, 2004). Thus, the general mechanism by which children can use expressions to manipulate the behavior of adults is not limited to caregivers. We wished to find further support for this general mechanism by examining responsiveness in adults generally. However, the possibility of enhanced effects in parents specifically cannot be ruled out. We attempted to assess adult responsiveness using a broad spectrum of relevant measures. Cuteness is one of the most studied aspects in adults’ perception of children. The fact that young children are widely perceived as ‘‘cute’’ is one attribute that has endeared them to adults generally. Cuteness is regarded as an evolutionary fitness signal displayed by children (Lorenz, 1943; Sprengelmeyer et al., 2009). Humans have also been known to find non-kin children cute (Karraker & Stern, 1990; Luo, Li, & Lee, 2011) and to find other species cute (Gould, 1980) based on body proportions and patterns, indicating that cuteness perception extends beyond one’s kin. Perception of cuteness triggers neurological (‘‘releasing’’) mechanisms in adults (Kringelbach et al., 2008), leading to behavioral ‘‘fixed action patterns’’ (Lorenz, 1937), and is associated with nurturing motivations

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(Glocker et al., 2009a, 2009b; Sternglanz et al., 1977). In the current study, we used cuteness perception along with a ‘‘likely to adopt’’ measure that gauged adults’ adoption preferences (Platek et al., 2002; Volk, Darrell-Cheng, & Marini, 2010; Volk & Quinsey, 2002) as proxies for motivations for nurturing. We anticipated that cuteness would be influenced by children’s expressions of emotion when the physical features of children were held constant by balancing the emotion expressions across the same children, thereby allowing us to separate the effects of emotion from physical features on cuteness ratings. Individuals may also experience negative affect from children’s facial expressions. Langlois, Ritter, Casey, and Sawin (1995) found that mothers’ perception of children’s attractiveness predicted caretaking behaviors and attitudes toward the children. Parents’ sensitivity to children’s distress predicts attachment between children and mothers (Leerkes, Blankson, & O’Brien, 2009; Mesman, Oster, & Camras, 2012) such that mothers’ sensitivity to children’s distress is likely to result in better social–emotional attachment for children (McElwain & Booth-LaForce, 2006). Thus, we measured feelings of distress reported by adults as they viewed different children’s expressions. In natural conditions, feelings of distress at a child’s negative feelings may motivate the adult to potentially remedy the cause of distress. Attention is a more direct measure that has been used to gauge preference for children’s faces (Gould, 1980; Hildebrandt, 1983). Hildebrandt and Fitzgerald (1978), Hildebrandt and Fitzgerald (1981) tested whether adults preferred children’s pictures to adult pictures and found that adults were more likely to smile while looking toward a child than while looking toward an adult. Other studies have also found a general adult visual preference for children’s faces compared with adult faces (Berman, Cooper, Mansfield, Shields, & Abplanalp, 1975; Fullard & Reiling, 1976). Power and colleagues (1982) found that smiling children were viewed for longer times than crying children and that children rated as cuter were viewed for longer times than those rated as less cute. Given that attention is used to gauge preference, we used attention, measured as looking time, toward children displaying different facial expressions as an implicit measure of preference. We hypothesized that smiling children would be rated as cuter, and more adoptable, relative to crying and neutral children. We also expected that crying children would evoke more distress compared with neutral and smiling children. Smiling children, therefore, should be viewed for longer times than crying and neutral children. We predicted that each of these behavioral outcomes should be influenced in similar ways across related and unrelated adults by the expression of children in both still images and dynamic videos. However, we also believed that individuals would differ to some degree in the extent to which they were responsive to cues such as children’s needs. For example, caretaking has been differentially associated with men and women (Platek et al., 2002) along with differences in physiological responses to viewing children (Power et al., 1982). However, although sex differences might predict differential responses to children’s expressions, we believe that characteristics associated with masculine and feminine features of both male and female personalities might play a stronger role than sex in mediating associations between children’s expressions and adult behaviors (Renk et al., 2003). Previous studies have found that femininity predicts empathy and perspective taking regardless of the sex of the participant (Vonk, Mayhew, & Zeigler-Hill, in press). Thus, we included Bem’s Sex Role Inventory (BSRI; Bem, 1974) to provide assessments of traditional gender roles of masculinity and femininity rather than measuring sex alone (Geary, 2010). In general, feminine characteristics as defined by the BSRI should be more strongly associated with nurturing feelings, as indicated by higher likely to adopt and cuteness ratings, compared with masculine characteristics. Potentially mediating the sex difference in responses to children’s emotional states are the psychological characteristics of empathy and perspective taking. Females generally report higher levels of empathy (Auyeung et al., 2009; Carroll & Yung, 2006; Voracek & Dressler, 2006; Wakabayashi, Sasaki, & Ogawa, 2012) and exhibit slightly superior perspective-taking skills (Alaerts, Nackaerts, Meyns, Swinnen, & Wenderoth, 2011; Baron-Cohen, 2002), suggesting that they may be more accurate at assessing the emotional states of even young children and, thus, more attuned to comforting children in distress. Attachment theorists have emphasized the role of empathy in caregiving motivations, highlighting the importance of the parent’s capacity to see things from the child’s perspective (Ainsworth, Bell, & Stayton, 1974). This capacity, perspective taking, may produce feelings of empathy leading to greater sensitivity to children’s feelings and, therefore, better caregiving. Because individual

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differences in observing adults’ capacity for empathy could affect their motivation to nurture (Psychogiou, Daley, Thompson, & Sonuga-Barke, 2008), we included Baron-Cohen’s widely used Empathy Quotient (Baron-Cohen & Wheelwright, 2004) and Davis’s (1983) popular Interpersonal Reactivity Index to measure empathy levels of adult participants. To assess perspective taking, we used the popular Reading the Mind in the Eyes Test (Baron-Cohen, Wheelwright, Hill, Raste, & Plumb, 2001). We hypothesized that individuals higher in empathy and perspective taking, regardless of sex, would show stronger effects of children’s emotion expressions on their viewing time and motivations to adopt. Although adults’ ratings of children expressing various emotions have been studied previously, no prior studies have examined the individual characteristics of adults predicting their responsiveness or used such a complete list of measures of nurturing motivations. Study 1 Method Participants The participants were recruited from the undergraduate participant pool of the psychology department at Oakland University. A total of 131 participants completed the looking time task; however 6 participants (2 men) failed to complete all of the Likert scales, thereby leaving 125 participants who completed all other rating scales. All 131 participants (96 women) were 18 years or older and without any serious visual impairment. Each student received course credit equivalent to an hour’s worth of participation. Materials A total of 45 images of 15 young children (three expressions for each child: smiling, crying, and neutral) were obtained from consenting parents or relatives of children. The children ranged in age from 1 to 3 years (7 girls). The 45 images were divided into three sets (A, B, and C) of 15 images each. Each child’s three photos were assigned to three different sets, with a different emotion expression being depicted in each set. Within each of the three sets, each emotion was expressed five times— involving a different child each time. The photographs were presented in an approximately 4  4-inch frame size on the computer screen. Only pictures from well-lit backgrounds, cropped to the child’s face, were used. Backgrounds were natural (i.e., not edited or standardized). Children represented both sexes and various ethnic backgrounds. The only restrictions were on image quality and orientation and fit with the appropriate emotional category. By using images of different emotions from the same children, we controlled for differential cuteness across children such that any effect of expression would be independent of inherent cuteness of the child. By exposing participants to only one image of each child, we eliminated any effects of prior exposure or response history to a particular child. In addition, nine copyright-free videos containing three different expressions (3  3) were obtained from publicly shared online videos. These videos showed young children smiling, crying, or displaying a neutral expression. The children depicted in the videos ranged in age from 3 weeks to 2 years, and the videos included both male and female children, although the sex of 4 children was unknown. The videos were edited in 10-s loops to last for a total of approximately 3 min. A desktop computer with E-Prime software installed was used to display pictures and videos on a 19-inch flat-screen monitor. A total of five rating scales were used for the study: one each for cuteness and sweetness in Part 2 of the laboratory experiment, followed by likelihood of adopting, level of distress, and level of uneasiness. Each scale had 15 ratings—one for each of the 15 images (or videos) shown to participants—on a Likert scale format from 1 (lowest) to 10 (highest). Procedure For the experimental tasks, participants were brought into the testing room individually and seated in front of a computer. Participants read instructions from a sheet of paper given to them at the beginning of the experiment. Participants were randomly assigned to view one of the three sets, each containing 15 images and 9 videos. The images (Part 1) and videos (Part 2) were both displayed in

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random order without replacement using E-Prime software. The order of presentation of image tasks and video tasks was counterbalanced in each set across participants to control for any sequence or carryover effects. Part 1: Looking time measure. Each participant was shown the assigned set of images. Participants began by selecting an orienting button on the screen with the mouse to begin the session after they had read the instructions. Participants were given a practice trial with three copyright-free images (a cat, a dog, and a horse) obtained from Google Images to ensure that they understood the task. Participants used the spacebar to proceed through the experiment. Participants were instructed to look at the images appearing on the screen. When they wanted to change the image on the screen, they pressed the spacebar. Pressing the spacebar displayed the rest of the 15 images on the screen one at a time. Once the actual experiment began, their looking time was measured as the interval between two spacebar presses, as measured by the E-Prime software, measuring the exposure time of each individual child image. Part 2: Rating scales. Following the assessment of looking time to each image, the images were presented again. This time, participants were administered a cuteness rating scale on which they rated each child image for its cuteness. The scale had ratings from 1 to 10, with 1 being the least cute and 10 being the cutest. This was followed by rating a randomized presentation of the same set for sweetness on a similar scale. This measure was included to measure internal consistency of cuteness ratings, in other words, to test whether the related construct of sweetness would obtain similar scores as cuteness on the same set of faces. Finally, participants rated their hypothetical likelihood of adopting each child in the set in the same format of a Likert-type scale. Part 3: Video viewing time. Participants were asked to watch nine videos, three belonging to each crying, neutral, and smiling child condition. Each video was looped to last for approximately 3 min by repeating a 10-s expression clip. Participants were asked to click and stop the video when they did not wish to watch it further. The duration of time for which each video was viewed by the participant was measured as the viewing time of the participant. Part 4: Video ratings of distress. Participants were once again shown the videos they rated earlier and were asked to rate their own level of distress while watching the videos, presented in random order. They could stop the videos whenever they wanted, as in Part 3. The ratings ranged from 1 (no distress) to 10 (highest distress level tolerable) and were to be collected in real time as participants watched the video. Distress ratings were followed by ratings on uneasiness, using the same scale, for calculating internal consistency. Results Correlations Cuteness ratings for neutral, crying, and smiling images were correlated with corresponding sweetness ratings (rs = .78, .80, and .71, respectively), and distress ratings for neutral, crying, and smiling videos were correlated with uneasiness ratings for corresponding videos (rs = .72, .70, and .59, respectively). Therefore, we considered only cuteness and distress, along with likelihood to adopt, in the following analyses rather than also including sweetness and uneasiness as variables. Probability values for repeated-measures analyses of variance (ANOVAs) are reported with Greenhouse–Geisser corrections to avoid errors due to sphericity violations. Effects of emotion expressions Looking times. We conducted repeated-measures ANOVA on looking times with expression (crying vs. neutral vs. smiling) and medium (images vs. videos) as within-participants factors and sex of the participants as a between-participants factor. We observed a significant difference in looking times across expressions, F(1.445, 182.031) = 69.365, p < .001, g2 = .36, but there was also a significant interaction between expression and medium, F(1, 126) = 4.429, p < .001, g2 = .20, indicating that the effect of

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expression on looking times differed between images and videos. We then conducted ANOVAs separately for videos and images with expression and sex as factors and found the effect of expression to be significant only for videos, F(1.4, 179.170) = 3.849, p = .04, g2 = .03 (see Fig. 1). Planned simple contrasts indicated that looking times for crying versus smiling videos differed significantly, F(1, 128) = 5.51, p = .02, g2 = .04, whereas the difference between crying and neutral images was not significant, F(1, 128) = 2.15, p = .15. Looking times also did not differ significantly between smiling and neutral children, F(1, 128) = 3.48, p = .07. There were no significant effects or interactions involving sex in any of the analyses.

Cuteness. A repeated-measures ANOVA of perceived cuteness for images with expression as a within-participants factor and sex as a between-participants factor revealed a significant effect of expression, F(1.911, 237.34) = 106.24, p < .001, g2 = .46. Within-participants simple contrasts indicated that neutral children images (M = 4.97, SEM = 0.19), F(1, 124) = 31.07, p < .001, g2 = .20, and smiling children images (M = 6.90, SEM = 0.18), F(1, 124) = 171.36, p < .001, g2 = .58, received significantly higher cuteness ratings than crying children images (M = 3.85, SEM = 0.18) for both men and women. Cuteness ratings were also higher for smiling children than for neutral children, F(1, 124) = 93.83, p < .001.

Likelihood to adopt. A repeated-measures ANOVA of ‘‘likely to adopt’’ ratings with expression as a within-participants factor and sex as a between-participants factor revealed that likely to adopt ratings also differed significantly across expressions, F(1.59, 198.27) = 133.52, p < .001, g2 = .52. The pattern was the same as for cuteness ratings; within-participants simple contrasts revealed that neutral children images (M = 5.46, SEM = 0.20), F(1, 124) = 86.44, p < .001, g2 = .41, and smiling children images (M = 7.16, SEM = 0.21), F(1, 124) = 178.05, p < .001, g2 = .59, received significantly higher likely to adopt ratings than crying children images (M = 3.75, SEM = 0.21) for both men and women. Likely to adopt ratings were also higher for smiling children than for neutral children, F(1, 124) = 91.77, p < .001. To determine whether emotion had an effect on likely to adopt ratings above and beyond that of cuteness, we conducted an analysis of covariance (ANCOVA) with cuteness ratings as a covariate and still obtained a significant effect of emotion, F(1.61, 197.71) = 103.18, p < .001, g2 = .46. Thus, the effects of emotion appear to convey information in addition to that provided by differential cuteness perceptions.

Fig. 1. Average looking times (in milliseconds) for expression by medium.

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Distress. A repeated-measures ANOVA of distress ratings during videos with expression as a withinparticipants factor and sex as a between-participants factor revealed that different expressions elicited significantly different distress ratings from participants, F(1.19, 141.89) = 131.09, p < .001, g2 = .52. Within-participants simple contrasts revealed that crying children images (M = 6.40, SEM = 0.26) were rated as significantly more distressing than neutral children images (M = 2.35, SEM = 0.18), F(1, 119) = 154.54, p < .001, g2 = .57, and smiling children images (M = 2.02, SEM = 0.20), F(1, 119) = 130.99, p < .001, g2 = .52, for both men and women. Distress ratings were also higher for neutral children than for smiling children, F(1, 124) = 5.76, p = .02. Discussion The results of Study 1 established that even non-kin adults are affected by the expressions portrayed by children. Not surprisingly, adults rated smiling and neutral children as cuter, more adoptable, and less distressing than crying children. When viewing videos, but not images, individuals viewed smiling children for longer times than they viewed crying children. It is probable that an even stronger effect would be observed for live children. These results affirm our hypothesis that children’s expressions elicit differing motivations to nurture even in nonrelatives. Although we hypothesized that this mechanism of children’s manipulation of adult behavior should be general rather than specific to kin, we also hypothesized that susceptibility to such manipulation might vary as a function of adults’ individual levels of nurturing qualities. Study 2 was designed to assess this aim. Study 2 Having established that the expressions of unrelated children affected the nurturing motivations of young adults, we wished to examine further differences in these adults that might better predict their responses to children’s expressions. Past research has shown that empathy and perspective taking are significant predictors of individual differences in the quality of caregiving to individuals with disabilities (Giesbrecht, 2008; Lobchuk, 2006), but we predicted that the same would be true of caregiving for typically developed children. Consistent with this prediction, empathy and perspective taking were also associated with the number of projected causes and caregiving strategies given by first- to seventh-grade children in response to crying infants (Catherine & Schonert-Reichl, 2011). Here, we assessed the relationship among femininity, masculinity, empathy, and perspective taking and adults’ responsiveness to children’s facial expressions using the outcomes measured in Study 1. Method Participants A total of 101 (78 women) of the participants who completed Study 1 participated in this study. They received additional credit for the research requirement in their entry-level psychology course. Measures Following participation in the lab experiment, participants were directed to a secure online survey site (http://www.surveymonkey.com), where they completed the Interpersonal Reactivity Index (IRI) developed by Davis (1983), the Empathy Quotient (EQ; Baron-Cohen & Wheelwright, 2004), Reading the Mind in the Eyes Test (RMET; Baron-Cohen et al., 2001), and Bem’s Sex Role Inventory (BSRI; Bem, 1974), respectively, to allow us to assess empathy, perspective taking, and gender roles as predictors for differing levels of emotional responsiveness. The IRI (Davis, 1983) was designed to assess four separate but related measures of empathy (the ability to experience what another is feeling) with four subscales: fantasy (the ability to place oneself into the feelings and actions of fictional characters in books, movies, and plays; e.g., ‘‘I really get involved with the feelings of the characters in a novel’’; a = .77), perspective taking (the ability to take the point of view of other people in daily life; e.g., ‘‘I try to look at everybody’s side of a disagreement before I make a decision’’; a = .69), empathic concern (the ability to feel compassion for others; e.g.,

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‘‘When I see someone being taken advantage of, I feel kind of protective toward them’’; a = .78), and personal distress (one’s own discomfort in reaction to others’ emotions; e.g., ‘‘When I see someone get hurt, I tend to remain calm’’; a = .64). Each scale consisted of seven items that participants rated to what degree the statements described themselves (from not at all like me to very much like me). The EQ (Baron-Cohen & Wheelwright, 2004) assesses self-reported empathy. It contains 40 empathy items and 20 filler/control items. Examples of statements include ‘‘I can pick up quickly if someone says one thing but means another’’ and reverse scored items such as ‘‘Seeing people cry doesn’t really upset me.’’ Participants were required to rate their level of agreement to each statement on a 4-point scale ranging from definitely agree to definitely disagree (a = .88). The RMET (Baron-Cohen et al., 2001) involves inferring the mental state of a person solely from the information in photographs of the person’s eyes. It consists of 36 images of the part of the face showing both eyes alone. Participants are asked to choose the best response to describe the feeling or thought projected in the image from four alternatives. Participants select the word describing the correct state of mind from four options (e.g., dominant, guilty, friendly, and horrified). Participants receive 1 point for each correct response. The sum of all correct answers indicates proficiency at the task (a = .71). The RMET has been shown to have adequate psychometric properties with typical adults (Girli, 2014; Vellante et al., 2013). The BSRI (Bem, 1974) was designed to categorize individuals as masculine, feminine, ‘‘androgynous,’’ or ‘‘undifferentiated’’ on the basis of personality characteristics. It can also be scored to yield a continuous variable for femininity and masculinity, as was done here, and is appropriate for both sexes. The masculinity scale and femininity scale contain 20 personality characteristics each. For example, the masculinity scale contains items such as aggressive, ambitious, and competitive, and the femininity scale contains items such as affectionate, gentle, and tender. Neutral characters are those that are applicable to either of the groups such as likeable, jealous, and moody. The BSRI asks the person to indicate on a 7-point scale how well each of the 60 masculine, feminine, and neutral personality characteristics describes himself or herself. The scale ranges from 1 (never or almost never true) to 7 (always or almost always true) and is labeled at each end point. Internal consistency is typically more than acceptable (Bem, 1974). We scored continuous measures of masculine and feminine qualities (a = .82). Results We calculated bivariate correlations to investigate zero-order associations between predictor variables (sex, femininity, masculinity, empathy, and perspective taking) from Study 2 and outcome variables (looking time, cuteness ratings of images, likelihood to adopt, and distress ratings of videos) from Study 1. We found that ‘‘likely to adopt’’ ratings on neutral and smiling images were moderately correlated with the IRI empathic concern subscale (r = .45, p < .001, and r = .48, p < .001, respectively) and with femininity from the BSRI (r = .44, p < .001, and r = .46, p < .001, respectively), indicating that people high in empathic concern and femininity considered neutral and smiling children to be more adoptable (see Table S1 in online supplementary material). We conducted hierarchical linear regressions with image looking time, cuteness ratings, video looking time, and distress ratings as the outcomes for each expression and the following as predictors in the model: RMET scores, EQ scores, continuous masculinity and femininity scores from the BSRI, and scores on the IRI subscales (perspective taking [PT], fantasy [F], empathic concern [EC], and personal distress [PD]). Sex of the participant was entered on Step 1, separate scores for masculinity and femininity for each participant were entered on Step 2, and all two-way interactions between masculinity, femininity, and sex, along with the other predictors, on Step 3, with the three-way interaction among sex, masculinity, and femininity being entered on Step 4. We report the regression coefficients for all variables at each step of the models in Table 1. To control for the family-wise error rate, alpha was set to p = .003 for all analyses. Images The only significant finding for images involved the likely to adopt measure and crying faces. Masculinity and femininity interacted to predict likely to adopt ratings for crying faces (b = .38,

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Table 1 Hierarchical multiple regression analyses of the eyes test (RMET), Empathy Quotient (EQ), Sex Role Inventory (BSRI), and Interpersonal Reactivity Index (IRI) perspective taking (PT), fantasy (F), empathic concern (EC), and personal distress (PD) subscales with looking time, cuteness ratings, and ‘‘likely to adopt’’ ratings for images and looking time and distress ratings for videos as the outcome. LT Image C Step 1 Sex Step 2 Masculinity Femininity Step 3 Masculinity  Femininity Masculinity  Sex Femininity  Sex Eyes test Empathy Quotient Perspective taking Empathic concern Fantasy Personal distress Step 4 Masculinity  Femininity  Sex

.00 .09 .02 .02 .21 .02 .02 .77 .56 .02 .07 .00 .21 .09 .05 .03 .38

LT Image N .00 .08 .02 .02 .09 .08 .01 .56 .63 .06 .04 .03 .22 .01 .01 .07 .80

LT Image S .00 .11 .01 .06 .07 .06 .08 .13 .37 .11 .07 .05 .19 .06 .12 .04 1.03

LT Video C .01 .14 .01 .11 .02 .14 .01 .09 .14 .14 .49* .09 .20 .10 .28* .01 .20

LT Video N .00 .11 .04 .05 .22* .12 .26* .88 .46 .24 .18 .26 .23 .16 .12 .11 .21

LT Video S .32* .21* .09* .17 .18 .16* .02 .83 .85 .15 .08 .25 .24 .05 .11 .24** 1.65**

Note. Numbers represent standardized betas and adjusted R2 values. C, crying; N, neutral; S, smiling; LT, looking time. * p < .05. ** p < .01.

t = 3.49, p = .001). Simple slopes tests were conducted to determine the effects of femininity on individuals both low and high in masculinity separately. Those low in masculinity (b = .47, t = 3.49, p = .001) and those high in masculinity (b = .55, t = 3.49, p = .001) reported a higher likelihood to adopt for crying children’s images if they were high in femininity compared with if they were low in femininity (although the effect of femininity was more pronounced for individuals high in masculinity, as seen in Fig. 2). Masculine individuals were least likely to want to adopt crying children compared with feminine and androgynous individuals. Videos The only significant effect for videos involved looking time for smiling children. Masculinity interacted with femininity and sex to influence looking time at smiling videos (b = –1.60, t = –3.16,

Fig. 2. Predicted values for ‘‘likely to adopt’’ ratings. The values illustrate the interaction of femininity (Fem) and masculinity (Masc) at values that are 1 standard deviation above and below their respective means.

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p = .003). We conducted separate regressions for male and female participants and found that the interaction of masculinity and femininity differed for men and women. The interaction was nonsignificant for both sexes, but Fig. 3 depicts that the effects were in the opposite directions, explaining the three-way interaction. Men higher in masculinity had longer looking times if they were also high in femininity rather than low in femininity. Women higher in femininity had longer looking times if they were lower in masculinity compared with those higher in masculinity (see Fig. 3). That is, androgynous men and feminine women looked longer at smiling videos compared with masculine men and androgynous women. However, the lack of significance may be due to the small number of men in the sample, and these results should be interpreted with caution.

Discussion Our predictor variables were not strongly associated with our outcomes from Study 1. Given this finding, it is possible that the mechanism by which children manipulate adult behaviors is generalized such that it is not under the control of individual variation in empathy and nurturing. The only statistically significant finding regarding sex roles was that femininity and masculinity interacted to predict likelihood to adopt. That is, participants higher in femininity reported higher likelihood to adopt regardless of their masculinity scores. More masculine participants scoring low in femininity tended to report lower likelihood to adopt crying children. There was also a trend for androgynous

Male Looking Time at Smiling Infant Videos (milliseconds)

19000 14000 9000 Low Masc

4000

High Masc -1000 -6000 -11000 Low Fem

High Fem

Female Looking Time at Smiling Infant Videos (milliseconds)

19000 17000 15000 13000 11000 9000

Low Masc

7000

High Masc

5000 3000 1000 -1000 Low Fem

High Fem

Fig. 3. Predicted values for looking time at smiling videos. The values illustrate the interaction of femininity (Fem) and masculinity (Masc) for men (top) and women (bottom) at values that are 1 standard deviation above and below their respective means.

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men and feminine women to look longer at smiling videos compared with masculine men and androgynous women. Although we had expected to find more of an influence of these variables, the findings support our notion of a generalized mechanism for manipulating the behavior of adults in general rather than those related to children or otherwise especially vulnerable to children’s manipulations, for example, due to high levels of empathy.

General discussion The aim of this research was to test whether adult responsiveness with regard to motivation to nurture differs as a function of children’s facial expressions (crying, smiling, and neutral). Whereas previous research has already identified that both related and unrelated adults are sensitive to the emotions expressed by human children, researchers had not previously examined the outcome of motivation to nurture across an array of measures. We hypothesized that young adults would respond differently to different basic expressions of unrelated children on selected measures of motivation to nurture. Previous studies have indicated that unrelated caregivers higher in empathy and perspective taking may engage in more effective caregiving strategies for children and disabled individuals (Catherine & Schonert-Reichl, 2011; Giesbrecht, 2008; Lobchuk, 2006), supporting the idea that these individual differences may importantly predict willingness to nurture unrelated dependents. Thus, we extended previous studies to examine the role of empathy, nurturing, and perspective taking in predicting adult responsiveness to typically developed young children. We speculated that more nurturing and empathic individuals could find crying children to be more distressing, which may decrease looking times to both images and videos of crying children. In the real world, the same effect could make unrelated adults more or less likely to adopt crying children depending on the manner in which empathy and nurturing are evoked. That is, individuals higher in empathic concern but low in personal distress may be inclined to adopt children who express special need. Others high in personal distress but low in sympathy may be less inclined to adopt children perceived to be needy. Subscales from Davis’s (1983) IRI were used here to provide clarity on this issue given that the scale includes assessments of positive (empathic concern and perspective taking) and negative (personal distress) aspects of empathy. It is possible that the relatively liberal age range of the children (1–3 years) depicted in our current study mitigated against stronger responses to crying children or stronger connections to individual levels of empathy and perspective taking. However, that we did not find strong associations of these subscales with individuals’ looking times, cuteness ratings, likelihood to adopt, or distress ratings might suggest the hegemonic and unifying nature by which children can manipulate the nurturing behaviors of even unrelated adults. Although individual differences in empathy did not predict attention or nurturing toward children, high femininity predicted higher likelihood to adopt children across the three expressions. In cases where femininity and masculinity interacted, participants higher in femininity reported higher likelihood to adopt regardless of their masculinity scores. More masculine participants scoring low in femininity tended to report lower likelihood to adopt crying children. This finding was consistent with previous research suggesting that feminine qualities are generally associated with more nurturing and caregiving behaviors (Feingold, 1994; Frodi, Lamb, Leavitt, Donovan, Neff, et al., 1978). The current results also corroborate previous findings that sex roles may be a better predictor of self-reported nurturing motivation than actual sex of the participant (Frodi & Lamb, 1978; Vonk et al., in press). This is important in the current study where we were limited by the small number of men in our sample, which may have precluded our ability to find sex differences. Nonetheless, we think that changing roles for men and women to some extent mitigate against the role of sex differences and make it sensible to instead focus on individual differences in nurturing and empathy as well as other qualities that might relate to caretaking. The finding that responses to children’s expressions may be ubiquitous across individual levels of empathy and nurturing, even in young adults unrelated to the children in question, provides support for our hypothesis that the expression of emotions in children serves as a general mechanism for manipulating the behavior of any adult who could potentially provide care. In general, the results supported our broader hypothesis about the effect of facial expressions on adults’ responsiveness.

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Consistent with previous research (Messinger, 2002; Power et al., 1982), images and videos of smiling children generally received responses that were positive and images and videos of crying children generally received responses that were negative. Although previous research has shown adults to respond positively to children’s smiles and negatively to children’s cries, here we also show links between children’s expressions and motivation to nurture with both self-reported measures of cuteness, distress, and likelihood to adopt and observed behaviors such as looking time. Despite the repetitive nature of completing the same Likert scales for multiple overlapping variables in the current study, observed measures converged with self-reports in that smiling children were viewed for longer times than crying children in the video condition and were rated to be cuter and more likely to be adopted compared with neutral and crying children. These results suggest participants’ strong inclination to nurture smiling children rather than crying children. These trends are interesting because although positive and negative feelings evoked by smiling and crying children, respectively, have been reported (Frodi, Lamb, Leavitt, & Donovan, 1978; Hildebrandt, 1983; Messinger et al., 2001), the link between these feelings and motivation to nurture has not been investigated. It could be argued that crying children could have received higher likelihood to adopt and cuteness ratings with the rationale that they might be more in need of attention and caretaking. Rather, we determined that crying children evoked more distress in participants compared with neutral and smiling children as well as lower ratings of likelihood to adopt and perceived cuteness. These apparently negative feelings do not seem to translate into nurturance either directly by increasing the need to adopt the children or indirectly by increasing the cuteness (or Kindenschema) of the children, which has been well known to evoke nurturance in adults (Glocker et al., 2009a, 2009b; Sprengelmeyer et al., 2009; Volk & Quinsey, 2002). Our findings are consistent with the notion from attachment theory (Bowlby, 1980) that smiling and laughter may serve the adaptive function of solidifying bonds between caretakers and children, but they are inconsistent with the notion that crying is also useful in strengthening an attachment bond between primary caregivers and children. It is possible that higher levels of distress in response to crying children do translate into real action toward participants with an actual crying child versus a static image or video of a crying child. By presenting images and videos to participants, we restricted the range of responses they could engage in when presented with children displaying various emotional states. Because participants could not soothe actual children, perhaps their behavior was logically directed toward reducing their own distress instead. Shorter looking times at crying videos could reflect that goal and could also be an indicator of less desire to devote attention to crying children. Future research should investigate adults’ motivation to spend time with children displaying different emotions. Although attachment theory (Ainsworth, 1979; Bowlby, 1980) focuses on the importance of the use of children’s emotions to solidify bonds with caretakers, manipulation can also contribute to this bond in important ways (Trivers, 1974). Because attachment theory focuses on the proximate mechanism rather than the evolutionarily significant mechanism behind children’s facial expressions (Chisholm, 1996; Fridlund, 1997), we used the intraspecific manipulation mechanism proposed by Dawkins and Krebs (1978) to fill this gap. Accordingly, we assumed that if children’s facial expressions function as a manipulator of adult behavior, adults will possess a mechanism that evokes differential responding to different children’s expressions such as actions to reduce distress caused by crying children. Such a system might be similar to nestlings signaling hunger to parent birds (Godfray, 1991). We do not argue that this phenomenon is unique to expressions of young children. Rather, we expect that the finding will carry over into interactions with other adults and adolescents. Crying individuals may produce more distress, and happy individuals may increase bonding at any age, but it is the adaptive function of such differentiation that we are interested in here, providing the impetus for the study of adults’ responses to young children. This proposed mechanism can be investigated in future research, possibly by using more advanced techniques of detailed and controlled imaging of children’s faces and using more accurate reaction time software and eye-tracking data. One surprising finding was that we did not find significantly different looking times between expressions in the image condition. This might have to do with the overall longer looking times of videos compared with images, such that the differences were exaggerated over longer durations but were not noticeable over smaller durations. This could also be a result of videos capturing more dynamic real-life versions of expressions that might have given participants

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a more complete experience of expressions as opposed to the mere nature of images. A relatively small image size (4  4 inches) could be another possible limitation in evoking stronger responses. In addition, presenting images of exclusively younger children may have also inspired stronger responses. Previous research has successfully used adult perception to investigate the functioning of children’s cues (Bolzani-Dinehart et al., 2005; Kringelbach et al., 2008; Sprengelmeyer et al., 2009). Because studying the receivers’ responses can lead to insights into the functioning of cues or signals, we used the same paradigm. Overall, these findings shed some light on the effect of children’s expressions on non-kin adults’ responsiveness and motivation to nurture. Results of this research build on previous research on responsiveness to children’s expressions and extend the patterns to new areas of research using multiple variable measures. 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