FACIAL FEEDBACK AND DEMAND CHARACTERISTICS 1

Demand characteristics moderate, but do not fully account for, the effects of facial feedback on emotional experience

Nicholas A. Coles1,2 (email), Lowell Gaertner2, Brooke Frohlich2, Jeff T. Larsen2, Dana M. Basnight-Brown3

1Harvard Kennedy School, Harvard University, Cambridge, USA 2Department of Psychology, University of Tennessee, Knoxville, USA 3United States International University–Africa, Nairobi, Kenya

Note: This article is a pre-print that is still being evaluated by peer reviewers. Please cite responsibly.

FACIAL FEEDBACK AND DEMAND CHARACTERISTICS 2

Abstract The facial feedback hypothesis suggests that an individual’s facial expressions can influence their emotional experience (e.g., that smiling can make one feel happier). However, a reoccurring concern is that demand characteristics drive this effect. Across three experiments (n = 250, 192, 131), university students in the United States and Kenya posed happy, angry, and neutral expressions and self-reported their emotions following a demand characteristics manipulation. To manipulate demand characteristics we either (a) told participants we hypothesized their poses would influence their emotions, (b) told participants we hypothesized their poses would not influence their emotions, or (c) did not tell participants a hypothesis. Results indicated that demand characteristics moderated the effects of facial poses on self-reported emotion. However, facial poses still influenced self-reported emotion when participants were told we hypothesized their poses would not influence emotion. These results indicate that facial feedback effects are not solely an artifact of demand characteristics.

Keywords: facial feedback hypothesis, demand characteristics, emotion, embodiment

OSF page: https://osf.io/mdfu4/ FACIAL FEEDBACK AND DEMAND CHARACTERISTICS 3

One of the fundamental goals of the social and affective sciences is to understand the nature of emotional experience. According to many modern theories, emotional experience is partially built off afferent signals from the peripheral nervous system (Cacioppo, Berntson, & Klein, 1992; Damasio & Carvalho, 2013; Laird & Bresler, 1992; Levenson, Ekman, & Friesen, 1990; Russell, 1980; Scherer & Moors, 2019; Tomkins, 1962). If these theories are true, it should be possible to modify people’s emotional experiences by altering their peripheral nervous system activity. One of the most well-known demonstrations of this possibility comes from research examining the effects of sensorimotor facial feedback on emotional experience—i.e., the facial feedback hypothesis (Coles, Larsen, & Lench, 2019; Coles, March, et al., 2019; C. Izard, 1979; Strack, Martin, & Stepper, 1988). For example, previous research suggests that posing happy expressions causes people to feel happier and posing sad expressions causes them to feel sadder (Flack, 2006). Research on the facial feedback hypothesis is consistent with theories that posit afferent signals from the peripheral nervous system influence emotional experience. Based on this mechanism, many researchers suggest that facial feedback plays a critical role in in a variety of social and emotional processes, including: emotion recognition (Marmolejo-Ramos et al., 2020; Niedenthal, Mermillod, Maringer, & Hess, 2010; Wood, Rychlowska, Korb, & Niedenthal, 2016), social perception (Niedenthal, Barsalou, Lawrence Winkielman, Krauth-Gruber, & Ric, 2005), the experience of empathetic and vicarious emotions (Hatfield, Cacioppo, & Rapson, 1992; Hoffman, 2001; Holland, Connell, & Dziobek, 2020), attitude contagion (Skinner, Osnaya, Patel, & Perry, 2020), the processing of emotional words and concepts (Neal & Chartrand, 2011; Niedenthal, 2007; Niedenthal, Winkielman, Mondillon, & Vermeulen, 2009; Winkielman, Coulson, & Niedenthal, 2018), and decision making (Carpenter & Niedenthal, 2020). However, a reoccurring concern is that facial feedback effects are merely an artifact of demand characteristics: cues that participants use to guess and adjust their behavior to fit the researcher’s hypothesis (Buck, 1980; Coles, Larsen, Kuribayashi, & Kuelz, 2019; Dimberg & Söderkvist, 2011; R. J. Larsen, Kasimatis, & Frey, 1992; Schimmack & Chen, 2017; Strack et al., 1988). For example, even if smiling does not increase happiness, participants may guess the hypothesis and adjust their happiness reports accordingly. We refer to this as the demand characteristics account. FACIAL FEEDBACK AND DEMAND CHARACTERISTICS 4

Demand characteristics are particularly concerning in facial feedback research because a recent meta-analysis indicated that 83% of studies have featured overt facial pose manipulations that may lead participants to guess the purpose of the study (Coles, Larsen, & Lench, 2019). For example, Schnall and Laird (2003) instructed participants to “Produce the facial expression that you would have when you were extremely happy,” which may have led participants to guess the purpose of the study and adjust their ratings accordingly (p. 790). To limit the impact of demand characteristics, the other 17% of studies have featured unobtrusive facial pose manipulations designed to hide the purpose of the study. For example, in a study ostensibly about psychomotor coordination, Strack, Martin, and Stepper (1988) had participants view humorous cartoons while holding a pen in their mouth in a manner that either induced or inhibited smiling. Similarly, in a study ostensibly about facial muscle reaction times, Dimberg and Söderkvist (2011) asked participants to react to visual stimuli by contracting their zygomaticus major (smiling) or corrugator supercilii (scowling) facial muscles. In both studies, participants reported higher levels of positive emotions after smiling despite apparently being unaware of the purpose of the study. Studies that have featured unobtrusive facial pose manipulations, however, have yielded mixed results. For example, a collaborative effort involving 17 replication teams recently failed to replicate the pen-in-mouth effect (Wagenmakers et al., 2016; cf. Marsh et al., 2018; Noah et al., 2018). Furthermore, in a conceptual replication of Dimberg and Söderkvist (2011), Söderkvist, Ohlén, and Dimberg (2018) did not find that participants reported more positive reactions to pleasant stimuli after completing an unobtrusive smiling task. More broadly, even though Coles et al.’s (2019) meta-analysis indicated that facial feedback has a small effect on self-reported emotional experience, a subgroup analysis did not provide significant evidence of facial feedback effects among studies that featured unobtrusive facial pose manipulations. Consequently, concerns about the demand characteristics account persist. Overview of Present Studies Whereas previous facial feedback researchers have strived to limit demand characteristics, the present set of studies feature a different tactic: the manipulation of demand characteristics (J. T. Larsen & McGraw, 2011; Nichols & Maner, 2008; Veitch, Gifford, & Hine, 1991). Prior to posing happy, angry, and neutral expressions and self-reporting their emotions, participants across three studies were randomly assigned to one of three demand characteristics FACIAL FEEDBACK AND DEMAND CHARACTERISTICS 5

conditions. In the positive expectation conditions, we told participants we expected their facial poses to influence their emotions. In the null expectation conditions, we told participants we did not expect their facial poses to influence their emotions (i.e., that we expect null effects). In the control conditions, we did not provide participants with information about a hypothesis. According to the facial feedback hypothesis, participants should report feeling (a) happier after posing happy vs. angry or neutral expressions, and (b) angrier after posing angry vs. happy or neutral expressions. According to the demand characteristics account, the effects of posed expressions on emotion will be largest in the positive expectation condition and non-existent in the null expectation condition. Another possibility is that both facial feedback and demand effects occur in concert, wherein the effects of posed expressions on emotion are attenuated but significant in the null expectation condition. In other words, demand characteristics may moderate the effects of posed expressions on reported emotion but not be necessary for these facial feedback effects to emerge. We pre-registered predictions consistent with the demand characteristics account in Study 1, expecting to not find evidence of facial feedback effects in the null expectation condition. Based on contradictory evidence in Study 1, we pre-registered predictions in Studies 2 and 3 that were consistent with the notion that facial feedback and demand characteristics effects occur in concert. There are three reasons we manipulated demand characteristics in the current set of studies. First, amid ongoing debates about the replicability of studies that have featured unobtrusive facial feedback manipulations, we sought a novel and potentially more robust approach for studying the role of demand characteristics. Second, manipulating demand characteristics allowed us to assess causality. Consistent with the demand characteristics account, previous correlational research suggests that the effects of posed expressions on emotion are larger among participants who are aware of the study purpose (Coles, March, et al., 2019). Alternatively, though, experiencing stronger facial feedback effects may cause participants to be more likely to realize the true purpose of the study (i.e., reverse causality). Furthermore, it is possible that the observed relationship between facial feedback effects and awareness of the study purpose is spurious. For example, it is possible that higher levels of focus cause participants to exhibit larger facial feedback effects and be more likely to detect the true purpose of the study. FACIAL FEEDBACK AND DEMAND CHARACTERISTICS 6

Third, manipulating demand characteristics allowed us to conduct severe tests of the facial feedback hypothesis. Mayo (1991) argues that the strength of evidence is a function of test severity: the probability of falsely yielding hypothesis-consistent results. For example, compared to non-blinded trials, double-blind trials of ineffective drugs are less likely to falsely indicate the drugs are effective because they limit participant and experimenter expectancy effects. Consequently, double-blinded medical trials are considered more severe tests of new interventions than non-blinded medical trials. Similarly, if the facial feedback hypothesis is false, researchers are less likely to falsely observe a facial feedback effect in a context where demand characteristic effects work against the facial feedback hypothesis vs. are minimized. Consequently, the null expectation conditions in our three studies provide a more severe test of the facial feedback hypothesis than previous work that has featured unobtrusive facial feedback manipulations designed to minimize the effects of demand characteristics. Study 1 In Study 1, we pre-registered predictions consistent with the demand characteristics account. Based on a power simulation that assumed the effects of posed expressions on emotion would be large in the positive expectation condition and non-existent in the null expectation condition, we planned to recruit at least 250 participants to achieve (a) ≈ 99% power to detect the hypothesized Pose by Demand interaction, and (b) > 70% power to detect our hypothesized Pose effects within the positive expectation and control conditions (see https://osf.io/rhz42 for more details). Between October 29th, 2019 and March 10th, 2020, 251 psychology students from a large southeastern university in the United States completed the study in exchange for course credit. Per our pre-registered analysis plan, we excluded one participant who did not respond to the emotion self-report measures. This left us with data from 250 participants (67% women, 33% men, Mage = 18.94, SDage = 2.37). Upon arriving to a laboratory, trained experimenters told participants the experiment examined how muscle movements and fluctuations in concentration/mood influence galvanic skin response and that they were assigned to move the muscles in their face. We provided this cover story to ensure that participants in all demand conditions were aware of our interest in facial movements. Next, experimenters attached two electrodes to participants’ left hands. These electrodes were connected to a visibly active Biopac MP36, although no physiological FACIAL FEEDBACK AND DEMAND CHARACTERISTICS 7

recordings were actually taken. Afterwards, experimenters informed participants that they would complete six timed facial movements and answer questions about their concentration and mood. Participants were randomly assigned to one of three demand conditions using the random assignment feature in Qualtrics1. Participants in the control condition (n = 77) began the facial expression poses without further instruction. Participants in the positive expectation (n = 86) and null expectation conditions (n = 87) received the following message from the experimenter (null expectation message in brackets):

“Researchers know that facial expressions influence skin responses. However, they are not sure why. Some researchers think that this happens because posing a facial expression of emotion causes you to feel that emotion. That is, smiling may make you happier and scowling may make you angrier. We believe this is [not] true. We are running this study to prove our hypothesis that posing facial expressions [does not] cause you to experience that emotion.”

If the participant indicated that they did not understand the above message, experimenters repeated the information with a visual aid (Figure S1 in Supplemental Materials). Experimenters then left the participant alone in the laboratory to complete the experiment via Qualtrics. Participants first completed two blocks of poses, each of which featured the same instructions to pose happy, angry, and neutral facial expressions in randomized order. For happy poses, participants were instructed to move the corner of their lips towards their ears, elevating their cheeks (Dimberg & Söderkvist, 2011). For angry poses, participants were instructed to move their eyebrows down towards their nose. For neutral poses, participants were instructed to maintain a blank expression. Participants were asked to hold each pose for 5 seconds in conjunction with an on-screen timer. After each pose, participants completed a modified Discrete Emotions Questionnaire (C. Harmon-Jones, Bastian, & Harmon-Jones, 2016), which included three averaged items measuring happiness (happiness, satisfaction, and enjoyment; overall ɑ = .88), three averaged items measuring anger (irritation, aggravation, and annoyance; overall ɑ = .85), and three averaged filler items measuring fear (alarmed, scared, and fear; 0 = “not at all” to

1 In all three studies, participants were assigned to demand condition using the random assignment feature in Qualtrics. However, quota counts were not used to force an equal distribution of participants across conditions. Consequently, sample sizes differ slightly across conditions. FACIAL FEEDBACK AND DEMAND CHARACTERISTICS 8

6 = “an extreme amount”) 2. Participants also reported how difficult they found the posing task and how concentrated they felt. Exploratory analyses related to self-reported difficulty did not change our conclusions and are not discussed further. After the two blocks of poses, participants completed the Multidimensional Assessment of Interoceptive Awareness (Mehling et al., 2012), although we did not pre-register any hypotheses for this measure and do not report analyses here. After completing the main portion of the study, the participant pressed a button that notified the experimenter to return and begin a funnel debriefing. In the funnel debriefing, the experimenter asked participants questions about what they believed our hypothesis was and whether they believed their facial expressions would influence their emotions. Based on these funnel debriefings, the experimenter provided ratings of the degree to which the participant was aware we were testing facial feedback effects (0 = “not at all aware” to 4 = “completely aware”). We included this measure to check that participants were more aware we were testing facial feedback effects in the positive and null expectation conditions (vs. control condition). Taken together, the experiment featured a 3 (Pose: happy, angry, neutral) x 2 (Block: first or second) x 3 (Demand: positive expectation, null expectation, control) mixed design, with demand characteristics manipulated between-subjects. The University of Tennessee-Knoxville Institutional Review Board approved the experimental protocol. Results Awareness of interest in testing facial feedback effects. To examine the efficacy of the demand characteristics manipulation, we tested whether participants were rated as more aware that we were testing facial feedback effects in the positive and null expectation conditions (vs. the control condition). These ratings should be interpreted with some caution since experimenters were aware of the participant’s condition, which could have influenced their interview behavior and interpretation of the participant’s responses (Robert Rosenthal & Fode,

2 The original Discrete Emotions Questionnaire contains four items measuring happiness, anger, fear, disgust, anxiety, sadness, desire, and relaxation. Given our interest in happiness and anger, we excluded all other subscales besides fear, which was used as filler items. To shorten the scale, we used three (as opposed to four) items in each remaining subscale. The original anger subscale contains the following items: anger, rage, mad, and pissed off. We felt that these only captured high-intensity anger and instead used irritation, aggravation, and annoyance. Cronbach alphas were calculated separately for each pose and block combination and then averaged. FACIAL FEEDBACK AND DEMAND CHARACTERISTICS 9

1963). With this caveat in mind, these ratings provide an opportunity to examine whether the demand characteristics manipulation was effective. If the demand characteristics manipulation was effective, participants should have been more aware we were testing facial feedback effects in the positive and null expectation conditions (vs. the control condition). Results from a one-way ANOVA confirmed that awareness ratings varied by expectation condition, F(2, 247) = 31.07, p < .001. Follow-up pairwise comparisons indicated that, compared to participants in the control condition (M = 1.39, SD = 1.22), participants were more aware we were testing facial feedback effects in the positive expectation (M = 2.93, SD = 1.21), t(247) = 7.66, p < .001, Mdiff 95% CI [1.14, 1.94], and null

expectation conditions (M = 2.52, SD = 1.38), t(247) = 5.62, p < .001, Mdiff 95% CI [0.73, 1.52]. Less critically, results also indicated that participants were slightly more aware we were testing facial feedback effects in the positive expectation vs. null expectation conditions, t(247) = 2.12, p

= .04, Mdiff 95% CI [0.03, 0.80]. Self-reported emotional experience. Due to the nested structure of the data (emotion reports nested within participants), we modeled emotion reports using linear mixed-effects regression via the lme4 package in R (Bates, Maechler, Bolker, & Walker, 2015). Models were fit with Pose, Block, and Demand entered as effects-coded factors, all higher-order interactions, compound symmetry covariance structures, random-intercepts for each participant, and Type III Sums of Squares. F-values are from ANOVA tables with Satterthwaite degrees of freedom. The primary analysis of interest was the Pose by Demand interaction, but we report results from the full model in the Supplemental Materials (Tables S1 and S2). We used model-estimated marginal means for all pairwise comparison tests, and we report pairwise comparison confidence intervals and effect sizes in Table S3. To quantify the size of the Pose effect in the positive expectation, control, and null

expectations conditions, standardized mean difference scores were computed (Cohen’s drm; Cohen, 1988). For ease of comprehension, self-reported emotional experience was averaged across block and effect sizes were calculated as the mean difference between (a) the emotion- congruent pose and (b) the average of the emotion-incongruent poses. For example, to calculate the size of the effect of pose on happiness, we compared happiness scores between (a) the average of the two happy pose trials, and (b) the average of the two neutral pose and two angry pose trials. FACIAL FEEDBACK AND DEMAND CHARACTERISTICS 10

Self-reported happiness. Consistent with the demand characteristics account, there was a significant Pose by Demand interaction, F(4, 1235) = 24.60, p < .001. As depicted in the top panel of Figure 1, the effect of Pose on self-reported happiness was largest in the positive

expectation condition, F(2, 1235) = 191.47, p < .001, drm = 1.19, second largest in the control

condition, F(2, 1235) = 85.53, p < .001, drm = 0.92, and smallest in the null expectation condition

F(2, 1235) = 16.79, p < .001, drm = 0.51. Follow-up pairwise comparisons indicated that participants reported more happiness after posing happy vs. neutral or angry expressions in all three Demand conditions (see Figure 1). In other words, contrary to our own initial predictions, participants exhibited significant facial feedback effects even when we explicitly informed them that the purpose of the study was to demonstrate that facial poses do not affect emotions. These results indicate that demand characteristics moderate, but do not fully account for, the effects of posed expressions on happiness. Self-reported anger. Consistent with the demand characteristics account, there was a significant Pose by Demand interaction, F(4, 1235) = 17.55, p < .001. As depicted in the bottom panel of Figure 1, the effect of Pose on self-reported anger was largest in the positive expectation condition, F(2, 1235) = 116.86, p < .001, drm = 0.75, second largest in the control condition, F(2,

1235) = 54.42, p < .001, drm = 0.69, and smallest in the null expectation condition F(2, 1235) =

8.89, p < .001, drm = 0.34. Follow-up pairwise comparisons indicated that participants reported more anger after posing angry vs. neutral or happy expressions in all three Demand conditions (see Figure 1). Similar to the findings with happiness, these results indicate that demand characteristics moderate, but do not fully account for, the effects of posed expressions on anger. Discussion Study 1 provides the first experimental evidence that facial feedback effects are moderated by demand characteristics. However, contrary to the demand characteristics account and our own initial predictions, posed happy and angry facial expressions influenced emotional experience even when we explicitly informed participants that the purpose of the study was to demonstrate that facial poses do not affect emotions. These results suggest that demand characteristics can moderate—but are not necessary for—facial feedback effects. Study 1 features two main limitations. The first limitation involves the fact that our experimenters were not blind to participants’ randomly assigned demand condition. Consequently, experimenters may have unknowingly exhibited experimenter bias (Robert FACIAL FEEDBACK AND DEMAND CHARACTERISTICS 11

Rosenthal & Fode, 1963). This experimenter bias could have been particularly problematic for the null expectation condition. More specifically, if experimenters privately believed that facial feedback effects are real, they may have unknowingly exhibited behavior that signaled they disagreed with the prediction communicated in the null expectation condition. Thus, participants in this condition may not have believed that the experimenter did not expect facial poses to impact emotion. Experimenter bias could have also undermined the validity of the measure of participants’ awareness of the stated hypothesis. For example, experimenters may have expected or wanted participants to understand the hypothesis communicated in the critical demand characteristics manipulation. Consequently, these expectations may have biased how experimenters (a) conducted the funnel debriefing, and (b) interpreted participants’ responses. The second limitation involves the fact that participants in the null expectation condition were led to believe that researchers disagreed about facial feedback effects. Participants in the null expectation condition were told that (a) some researchers believe that posing facial expressions influences emotion, but that (b) we believed this was not true. When faced with this disagreement, some participants may have opted to believe other researchers’ hypothesis (rather than our contradictory hypothesis). This could have undermined the effectiveness of the null expectation manipulation, causing us to erroneously observe evidence for facial feedback effects in this condition. We address these two limitations in Study 2.

FACIAL FEEDBACK AND DEMAND CHARACTERISTICS 12

Fig. 1. Study 1 self-reported happiness and anger after participants posed happy, neutral, and angry facial expressions in the positive expectation, control, and null expectation conditions. Bar heights represent estimated marginal means and error bars represent one standard error. Emotion reports were measured on a 7-point scale, but the y-axis is truncated for ease of visualization. Full brackets represent hypothesis-relevant directional pairwise comparisons and dotted brackets represent non-directional pairwise comparisons that facial feedback theorists do not make clear predictions about. All pairwise comparison tests had 1235 degrees of freedom. **p < .01; ***p < .001; exact values are reported for non-significant p-values. FACIAL FEEDBACK AND DEMAND CHARACTERISTICS 13

Study 2 We made three changes in Study 2. First, based on surprising results from Study 1, we pre-registered our prediction that demand characteristics and facial feedback effects would occur in concert. Thus, we predicted that facial feedback effects would emerge in the null expectation condition. Second, to prevent potential experimenter bias, we had participants complete the study online. Third, to improve the null expectation condition, information about other researchers’ beliefs was removed from the demand characteristics manipulation. The experiment featured the same 3 (Pose: happy, angry, neutral) x 2 (Block: first or second) x 3 (Demand: positive expectation, null expectation, control) mixed design used in Study 1, with demand characteristics manipulated between-subjects. Method Based on a power simulation with conservative effect size estimates from Study 1, we planned to recruit at least 180 participants to achieve (a) ≈ 99% power to detect our hypothesized Pose by Demand interaction, and (b) > 80% power to detect our hypothesized Pose effects within each level of Demand (see https://osf.io/j52w7 for more details). Between July 29th, 2020 and August 30th, 2020, 197 psychology students from a large southeastern university in the United States completed the online experiment in exchange for course credit. Per our pre-registered analysis plan, 5 participants who failed attention checks were excluded. This left us with data from 192 participants (71% women, 28% men, <1% gender variant/non-conforming, <1%

declined to answer; Mage = 18.60, SDage = 1.27). Participants completed the online study via Qualtrics. Participants were told that they were participating in a study on physical movements and that they were randomly assigned to complete six face movements. In actuality, all participants completed the same six facial movements but were randomly assigned to one of three demand conditions using the random assignment feature in Qualtrics. Participants in the control condition (n = 65) began the facial expression poses without further instruction. Participants in the positive expectation (n = 67) and null expectation (n = 60) conditions received the following message (null expectation message in brackets):

“We believe that emotions cause facial expressions--but that posing facial expressions can also [can not] cause emotions. For example, happiness makes you smile, but smiling can also [can not] make you happy. Likewise, anger makes you scowl, but scowling can also [can not] make FACIAL FEEDBACK AND DEMAND CHARACTERISTICS 14

you angry. The purpose of this study is to generate scientific evidence that facial expressions can [can not] cause emotions.”

Like Study 1, participants completed two blocks of poses, each which included the same instructions to pose happy, angry, and neutral facial expressions in randomized order. After each pose, participants completed the modified Discrete Emotions Questionnaire featured in Study 1 (overall happiness ɑ = .88; overall anger ɑ = .88), completed filler items about how difficult they found the posing task and how concentrated they felt, and completed an attention check item that instructed participants to choose a specific response option. After the two blocks of poses, participants were asked to describe the purpose of the study. Two independent coders who were blind to participants’ condition and the results of the study reviewed these responses and rated the degree to which each participant was aware we were testing facial feedback effects (0 = “not at all aware” to 4 = “completely aware”; ICC = .77, 95% CI [.70, .82]). We averaged the two coders’ ratings to form an awareness score. Results Awareness of interest in testing facial feedback effects. Like Study 1, results from a one-way ANOVA confirmed that awareness ratings varied by expectation condition, F(2, 189) = 6.81, p = .001. Follow-up pairwise comparisons indicated that, compared to participants in the control condition (M = 2.63 , SD = 1.69), participants were more aware we were testing facial

feedback effects in the positive expectation (M = 3.33, SD = 1.28), t(189) = 2.85, p = .005, Mdiff 95% CI [0.21, 1.18], and null expectation conditions (M = 3.50, SD = 1.19), t(189) = 3.45, p <

.001, Mdiff 95% CI [0.37, 1.37]. Inconsequentially, we did not find significant evidence that awareness differed between the positive vs. null expectation conditions, t(189) = -0.69, p = .49,

Mdiff 95% CI [-0.67, 0.32]. Self-reported emotion experience. Like Study 1, we modeled emotion reports using linear mixed-effects regression using the lme4 package in R. Models were fit with Pose, Block, and Demand entered as effects-coded factors, all higher-order interactions, compound symmetry covariance structures, random-intercepts for each participant, and Type III Sums of Squares. F- values are from ANOVA tables with Satterthwaite degrees of freedom. The primary analysis of interest was the Pose by Demand interaction, but we report results from the full models in Tables S4 and S5. Like Study 1, we used model-estimated marginal means for all pairwise comparison tests, and we report the pairwise comparison confidence intervals and effect sizes in Table S6. FACIAL FEEDBACK AND DEMAND CHARACTERISTICS 15

Self-reported happiness. Consistent with the demand characteristics account, results indicated that there was a significant Pose by Demand interaction, F(4, 945) = 3.18, p = .01. As depicted in the top panel of Figure 2, the effect of Pose on self-reported happiness was largest in

the positive expectation condition, F(2, 945) = 96.83, p < .001, drm = 1.14, second largest in the

control condition, F(2, 945) = 55.75, p < .001, drm = 1.00, and smallest in the null expectation

condition F(2, 945) = 36.35, p < .001, drm = 0.84. Follow-up pairwise comparisons indicated that participants reported more happiness after posing happy vs. neutral and angry expressions in all three Demand conditions (see Figure 2). Replicating Study 1, the significant facial feedback in the null expectation condition indicates that demand characteristics are not necessary for facial feedback effects to emerge. Self-reported anger. Results did not provide significant evidence of a Pose by Demand interaction, F(4, 945) = 1.58, p = .18. Nevertheless, consistent with the demand account, the effect of Pose on self-reported anger was numerically largest in the positive expectation

condition, F(2, 945) = 50.21, p < .001, drm = 0.94, second largest in the control condition, F(2,

945) = 43.74, p < .001, drm = 0.79, and smallest in the null expectation condition F(2, 945) =

19.88, p < .001, drm = 0.66 (see bottom panel of Figure 2). Follow-up pairwise comparisons indicated that participants reported more anger after posing angry vs. neutral and happy expressions in all three Demand conditions (see Figure 2). Taken together, these results do not provide clear evidence that the effect of posed expressions on anger is moderated by demand characteristics. More importantly, though, the observed effect of Pose in the null expectation condition provides clear evidence that the effects of posed expressions of anger is not entirely driven by demand characteristics. Discussion Study 2 partially replicate the finding from Study 1 that facial feedback effects are moderated by demand characteristics. Demand characteristics significantly moderated the effects of posed expressions on happiness, but we did not uncover significant evidence that demand characteristics moderated the effects of posed expressions on anger. Most importantly, though, Study 2 consistently indicated that the effects of posed expressions on happiness and anger were significant in all demand conditions, including the condition where participants were told that we did not expect their posed expressions to impact their emotions. Furthermore, Study 2 suggests that these results are not driven by experimenter bias or ambiguity in the null expectation FACIAL FEEDBACK AND DEMAND CHARACTERISTICS 16

demand characteristics instructions. However, our reliance on samples of university students from the Southeastern United States limits the generalizability of these findings (Henrich, Heine, & Norenzayan, 2010). We address this limitation in Study 3.

FACIAL FEEDBACK AND DEMAND CHARACTERISTICS 17

Fig. 2. Study 2 self-reported happiness and anger after participants posed happy, neutral, and angry facial expressions in the positive expectation, control, and null expectation conditions. Bar heights represent estimated marginal means and error bars represent one standard error. Emotion reports were measured on a 7-point scale, but the y-axis is truncated for ease of visualization. Full brackets represent hypothesis-relevant directional pairwise comparisons and dotted brackets represent non-directional pairwise comparisons that facial feedback theorists do not make clear predictions about. All pairwise comparison tests had 945 degrees of freedom. *p < .05; **p < .01; ***p < .001; exact values are reported for non-significant p-values. FACIAL FEEDBACK AND DEMAND CHARACTERISTICS 18

Study 3 To investigate the cross-cultural generalizability of the Study 2 results, we conducted Study 3 with a university sample in Kenya. To maximize comparability, Studies 2 and 3 were run at roughly the same time, meaning that the results of Study 2 were not known when Study 3 commenced. All procedures were identical except two deviations. First, we replaced the word “scowl” with the phrase “scowl or frown” throughout the experiment. We made this change because two classroom pilot surveys (n = 21 and 29) indicated that many participants in this population use the terms “scowl” and “frown” interchangeably to describe the expression associated with anger. Second, to maintain high statistical power with a relatively small participant pool, we did not include a control condition in Study 3. Thus, the experiment featured a 3 (Pose: happy, angry, neutral) x 2 (Block: first or second) x 2 (Demand: positive expectation or null expectation) mixed design, with demand characteristics manipulated between-subjects. Removing the control condition precluded us from confirming that our manipulation led to increased awareness of our interest in testing facial feedback effects. Nevertheless, given that Studies 1 and 2 provided consistent evidence of the efficacy of the manipulation, we believe that this tradeoff was reasonable. Method Based on a power simulation with conservative parameter estimates from Study 1, we planned to recruit at least 120 participants to achieve (a) 99% power to detect our hypothesized Pose by Demand characteristics interaction, and (b) > 80% power to detect our hypothesized Pose effects within each level of Demand (see https://osf.io/j52w7 for more details). Between August 8th, 2020, and August 17th, 2020, 150 students from a university in Kenya completed the experiment online. After excluding 19 participants who failed attention checks, we were left with data from 131 participants (npositive expectation = 62, nnull expectation = 69, 76% women, 22% men, 2% undisclosed gender, Mage = 26.80, SDage = 8.07; see https://osf.io/mdfu4/ for details regarding power analysis). All procedures (except the two noted exceptions) were the same as those used in Study 2. Results Like Studies 1 and 2, we modeled self-reported happiness (overall ɑ = .84) and anger (overall ɑ = .86) using linear mixed-effects regression using the lme4 package in R. Models were fit with Pose, Block, and Demand entered as effects-coded factors, all higher-order interactions, FACIAL FEEDBACK AND DEMAND CHARACTERISTICS 19

compound symmetry covariance structures, random-intercepts for each participant, and Type III Sums of Squares. F-values are from ANOVA tables with Satterthwaite degrees of freedom. The primary analysis of interest was the Pose by Demand interaction, but we report results from the full models in Tables S7 and S8. Like Studies 1 and 2, we used model-estimated marginal means for all pairwise comparison tests, and we report the pairwise comparison confidence intervals and effect sizes in Table S9. Self-reported happiness. Consistent with the demand characteristics account, there was a significant Pose by Demand interaction, F(2, 645) = 5.92, p = .003. As depicted in the top panel of Figure 3, the effect of Pose on self-reported happiness was larger in the positive

expectation condition, F(2, 645) = 62.00, p < .001, drm = 0.96, than in the null expectation condition F(2, 645) = 23.74, p < .001, drm = 0.62. Follow-up pairwise comparisons indicated that participants reported more happiness after posing happy vs. neutral and angry expressions in both Demand conditions (see Figure 2). As with the U.S. samples in Studies 1 and 2, these results with a Kenyan sample confirm that demand characteristics moderated, but did not fully account for, the effects of posed expressions on happiness. Self-reported anger. Consistent with the demand characteristics account, results indicated that there was a significant Pose by Demand interaction, F(2, 645) = 10.46, p < .001. As depicted in the bottom panel of Figure 3, the effect of Pose on self-reported anger was larger in the positive expectation condition, F(2, 645) = 85.22, p < .001, drm = 0.96, than in the null expectation condition F(2, 645) = 26.45, p < .001, drm = 0.58. Once again, pairwise comparisons indicated that participants reported more anger after posing angry vs. neutral and happy expressions in both Demand conditions (see Figure 2). These results confirm that the effects of posed expressions on anger are not entirely driven by demand characteristics. FACIAL FEEDBACK AND DEMAND CHARACTERISTICS 20

Fig. 3. Study 3 self-reported happiness and anger after participants posed happy, neutral, and angry facial expressions in the positive and null expectations conditions. Bar heights represent estimated marginal means and error bars represent one standard error. Emotion reports were measured on a 7-point scale, but the y-axis is truncated for ease of visualization. Full brackets represent hypothesis-relevant directional pairwise comparisons and dotted brackets represent non-directional pairwise comparisons that facial feedback theorists do not make clear predictions about. All pairwise comparison tests had 645 degrees of freedom. *p < .05; **p < .01; ***p < .001; exact values are reported for non-significant p-values. FACIAL FEEDBACK AND DEMAND CHARACTERISTICS

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General Discussion Consistent with the facial feedback hypothesis, decades of research has indicated that posing facial expressions of emotion can influence emotional experience (for reviews, see Adelmann & Zajonc, 1989; Coles, Larsen, & Lench, 2019; Laird & Strout, 2007). Throughout these decades of research, however, researchers have continually been concerned with an alternative demand characteristics account: that supposed facial feedback effects are merely an artifact of demand characteristics (Buck, 1980; Dimberg & Söderkvist, 2011; R. J. Larsen et al., 1992; McIntosh, Zajonc, Vig, & Emerick, 1997; Schimmack & Chen, 2017; Strack et al., 1988). At one point, this demand characteristics account was seemingly refuted by studies that featured unobtrusive facial pose manipulations designed to limit the role of demand characteristics (R. J. Larsen et al., 1992; McIntosh et al., 1997; Strack et al., 1988). However, concerns about demand characteristics have recently been revived by difficulties replicating studies featuring unobtrusive facial pose manipulations (e.g., Wagenmakers et al., 2016; cf. Marsh et al., 2018; Noah et al., 2018). Further contributing to concerns about demand characteristics, a recent meta-analysis indicated that the majority of evidence for facial feedback effects is based on studies that featured overt facial expression manipulations that may lead participants to guess the purpose of the study (Coles, Larsen, & Lench, 2019). Furthermore, when analyses are limited to studies that feature unobtrusive facial pose manipulations, there is no longer significant evidence of facial feedback effects. To test the demand characteristics account, we experimentally manipulated demand characteristics by altering the hypothesis communicated to participants. We originally pre- registered predictions consistent with the demand characteristics account. However, across three studies conducted in the United States and Kenya, we consistently found that demand characteristics did not fully account for observed facial feedback effects. (For a more extensive ongoing cross-cultural investigation of facial feedback effects, see Coles, March, et al., 2019.) Our results indicated that demand characteristics can indeed moderate facial feedback effects. Notably, though, our results indicated that participants exhibit facial feedback effects even when they are explicitly told we expect null effects. This latter observation is notable given that is based on a condition where demand characteristics work against potential facial feedback effects. According to Mayo’s (1991) severe testing philosophy, the strength of evidence should FACIAL FEEDBACK AND DEMAND CHARACTERISTICS

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be considered a function of the probability of falsely yielding hypothesis-consistent results. If the facial feedback hypothesis is false, researchers are less likely to falsely observe a facial feedback effect in a context where demand characteristics work against the facial feedback hypothesis (vs. are minimized, as done in previous research). Thus, based on this severe testing philosophy, our results provide the strongest evidence to date in favor of the facial feedback hypothesis. Although not frequently discussed in the facial feedback literature, it is possible that our observed effects were driven by other study artifacts, such as participants’ personal beliefs about facial feedback effects (i.e., placebo effects; Crum & Phillips, 2015). It is often assumed that demand characteristics influence behavior because participants want to help confirm the study hypothesis (Orne, 1962) or appear favorable to the experimenter (Rosenberg, Rosenthal, & Rosnow, 1969)—not because they believe the experimenter’s hypothesis. Nevertheless, although our studies were not designed to assess placebo effects, we collected data on participants’ stated beliefs about facial feedback at the end of each study. Exploratory analyses revealed that facial feedback effects were indeed larger amongst participants who believed in the facial feedback hypothesis. Notably, however, we still observed significant facial feedback effects amongst participants who did not believe in the facial feedback hypothesis (see Supplemental Materials). These results provide further evidence that facial feedback effects are not solely driven by study artifacts. Understanding the role of facial feedback in emotion Our evidence of a non-artifactual facial feedback effect is notable given that researchers have posited that facial feedback plays an important role in a variety of social and emotional processes. This includes emotion recognition (Marmolejo-Ramos et al., 2020; Niedenthal et al., 2010; Wood et al., 2016), social perception (Niedenthal, Barsalou, Lawrence Winkielman, et al., 2005), the experience of empathetic and vicarious emotions (Hatfield et al., 1992; Hoffman, 2001; Holland et al., 2020), attitude contagion (Skinner et al., 2020), the processing of emotional words and concepts (Neal & Chartrand, 2011; Niedenthal, 2007; Niedenthal et al., 2009; Winkielman et al., 2018), and decision making (Carpenter & Niedenthal, 2020). For example, embodied facial mimicry is one mechanism—among many—that some researchers believe guides emotion recognition (see Wood et al., 2016b for a complete model). According to this mechanistic account, people automatically mimic facial expressions they observe in others. This FACIAL FEEDBACK AND DEMAND CHARACTERISTICS

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mimicry, in turn, produces facial feedback, which is posited to partially re-active the neural system(s) responsible for emotional experience. For example, observing an individual express happiness can cause us to automatically mimic the individual’s expression of happiness. Facial feedback from this mimicked expression of happiness is expected to subsequently cause us to feel happy. Our observation that posing happy and angry facial expressions can impact self- reported happiness and anger lends support to this embodied facial mimicry account. More broadly, our results support theories that posit emotional experience is partially built off afferent signals from the peripheral nervous system (Cacioppo et al., 1992; Damasio & Carvalho, 2013; Laird & Bresler, 1992; Levenson et al., 1990; Russell, 1980; Scherer & Moors, 2019; Tomkins, 1962). This observation is notable because it suggests that facial feedback can be used to test models of the relationship between the peripheral nervous system and emotional experience. Compared to other components of the peripheral nervous system, there are at least three reasons why we think facial feedback may be a particularly useful approach for testing these theoretical models. First, compared to other components of the peripheral nervous system, the face appears to better map on to discrete emotional states, such as anger and sadness (Allport, 1922, 1924; Keltner, Ekman, Gonzaga, & Beer, 2003). It is difficult to identify which discrete emotion people are experiencing by examining other aspects of peripheral nervous system activity, such as heart rate and/or heart rate variability alone (Siegel et al., 2018). However, researchers have had more success identifying which discrete emotion people are experiencing by analyzing facial expression activity (Lewinski, den Uyl, & Butler, 2014b, 2014a; Skiendziel, Rösch, & Schultheiss, 2019). Second, compared to other components of the peripheral nervous system, the face appears to be a particularly potent source of bodily feedback in emotion. When asked which regions of the body are most active during an emotional episode, people most frequently and strongly emphasize changes in the face (Hietanen, Glerean, Hari, & Nummenmaa, 2016; Nummenmaa, Glerean, Hari, & Hietanen, 2014). Third, compared to other regions of the peripheral nervous system, it is easier, cheaper, and arguably more ethical to manipulate facial feedback. Experimentally manipulations of other aspects of the peripheral nervous system—such as heart rate variability biofeedback (Lehrer & Gevirtz, 2014), intensive exercise (Martin, Harlow, & Strack, 1992), epinephrine injections (Schachter & Singer, 1962), and fasting FACIAL FEEDBACK AND DEMAND CHARACTERISTICS

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(MacCormack & Lindquist, 2019)—can involve extensive training, require expensive equipment, and/or face ethical constraints. Facial feedback manipulations, on the other hand, can be completed voluntarily, precisely, and safely by most healthy participants—without any specialized equipment (Rinn, 1984). Although our research suggests that facial feedback may be one aspect of the peripheral nervous system that informs emotional experience (Allport, 1922; C. Izard, 1979; C. E. Izard, 2007; Laird, 1974; McIntosh et al., 1997; Schnall & Laird, 2003; Tomkins, 1962; Zajonc, 1985), it is possible that facial feedback has other roles in emotion. For example, some researchers have suggested that facial feedback can impact other emotion-related cognitive processes, such as memory or the processing of emotional information (Forster and Strack, 1996; Niedenthal et al., 2005; Scherer, 2009; Schnall and Laird, 2003; Smith and Kirby, 2004; Strack et al., 1988; Wood et al., 2016). Other researchers have suggested that facial feedback can impact other components of the peripheral nervous system, such as heart rate (Ekman, Levenson, & Friesen, 1983; Kraft & Pressman, 2012; Levenson, Carstensen, Friesen, & Ekman, 1991; Levenson et al., 1990; Levenson, Ekman, Heider, & Friesen, 1992). Last, some researchers have suggested that facial feedback can create changes in motivational states (Coan, Allen, & Harmon-Jones, 2001; E. Harmon-Jones, Gable, & Price, 2011; Wansink, Zampollo, Camps, & Shimizu, 2014). The next important step for researchers will be to model the cognitive, neural, and social processes that underlie facial feedback effects, all while keeping at bay the potentially large influence of demand characteristics.

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Author Contributions N. A. Coles developed the initial study concept. N. A. Coles, L. Gaertner, B. Frohlich, J. Larsen, and D. Basnight Brown developed the methodology. N. A. Coles and B. Frohlich programmed the study. N. A. Coles, B. Frohlich, J. Larsen, and D. Basnight Brown supervised data collection. N. A. Coles and L. Gaertner analyzed and visualized the data. N. A. Coles drafted the manuscript. L. Gaertner, B. Frohlich, J. T. Larsen, and D. Basnight Brown provided critical feedback on the manuscript. All authors contributed to revisions and approved the final version of the manuscript for submission. Acknowledgments Thank you to Åse Innes-Ker, Nicholas James, Tiago Lubiana, Melissa Kline, Ingrid Aulike, Pradnya Bulusu, and Jonathan Williams for their earlier feedback through the Red Team Challenge: a financially incentivized error-detection challenge (Coles et al., 2020). Thank you to Leo Tiokhin, Daniël Lakens, and Ruben Arslan for comments on an earlier version of this manuscript.

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Supplemental Materials Study 1 Visual aid Experimenters in Study 1 were permitted to use the visual aids below to explain to participants the expected effects in the positive and null expectation conditions.

Self-reported emotion Although we were primarily interested in examining the Pose by Demand interaction in Study 1, results from the full model are displayed in Tables S1 and S2. Pairwise comparisons for the decomposition of Pose by Demand interactions are displayed in Table S3. For both self- reported happiness and anger, we fit a linear mixed effects model with Pose, Block, and Demand entered as effects-coded factors, all higher-order interactions, compound symmetry covariance, random-intercepts for each participant, and Type III Sums of Squares. We used model-estimated marginal means for all pairwise comparison. FACIAL FEEDBACK AND DEMAND CHARACTERISTICS 34

Table S1. Full Study 1 results from a linear mixed effects model of happiness self-reports. F-values are from ANOVAs with

Satterthwaite method degrees of freedom. Main effects were decomposed via pairwise difference tests. Interactions were decomposed by examining the effect of the first factor within each level of the second factor via contrasts.

Omnibus test Decomposition Result Pose - F(2, 1235) = 246.03, p < .001

Happy – Neutral t(1235) = 18.16, p < .001, Mdiff 95% CI [0.74, 0.92]

Happy – Angry t(1235) = 20.11, p < .001, Mdiff 95% CI [0.83, 1.01]

Anger – Neutral t(1235) = -1.96, p = .05, Mdiff 95% CI [-0.18, 0.00] Demand - F(2, 247) = 7.34, p < .001

Positive – Control t(247) = .94, p = .35, Mdiff 95% CI [-0.09, 0.27]

Positive – Null t(247) = 3.70, p < .001, Mdiff CI [0.15, 0.50]

Control – Null t(247) = 2.66, p = .008, Mdiff 95% CI [0.06, 0.42] Block - F(1, 1235) = 19.38, p < .001

Block 1 – Block 2 t(1235) = 4.40, p < .001, Mdiff 95% CI [0.09, 0.24] Pose by Demand - See main text See Table S3 See Table S3 Pose by Block - F(2, 1235) = 4.24, p = .01 Block 1 Pose Effect F(2, 1235) = 157.04, p < .001 Block 2 Pose Effect F(2, 1235) = 93.23, p < .001 Demand by Block - F(2, 1235) = 0.40, p = .67 Pose by Demand by Block - F(4, 1235) = 0.15, p = .96 FACIAL FEEDBACK AND DEMAND CHARACTERISTICS 35

Table S2. Full Study 1 results from a linear mixed effects model of anger self-reports. F-values are from ANOVAs with Satterthwaite method degrees of freedom. Main effects were decomposed via pairwise difference tests. Interactions were decomposed by examining the effect of the first factor within each level of the second factor via contrasts. Omnibus test Decomposition Result Pose - F(2, 1235) = 146.33, p < .001

Happy – Neutral t(1235) = -0.67, p = .51, Mdiff 95% CI [-0.09, 0.04]

Happy – Angry t(1235) = -15.14, p < .001, Mdiff 95% CI [-0.57, -0.44]

Anger – Neutral t(1235) = 14.47, p < .001, Mdiff 95% CI [0.42, 0.55] Demand - F(2, 247) = 9.50, p < .001

Positive – Control t(247) = 1.94, p = .05, Mdiff 95% CI [0.00, 0.28]

Positive – Null t(247) = 4.35, p < .001, Mdiff 95% CI [0.17, 0.44]

Control – Null t(247) = 2.29, p = .02, Mdiff 95% CI [0.02, 0.30] Block - F(1, 1235) = 8.30, p = .004

Block 1 – Block 2 t(1235) = -2.88, p = .004, Mdiff 95% CI [-0.13, -0.03] Pose by Demand - See main text See Table S3 See Table S3 Pose by Block - F(2, 1235) = 1.13, p = .32 Demand by Block - F(2, 1235) = 0.48, p = .62 Pose by Demand by Block - F(4, 1235) = 1.28, p = .28

FACIAL FEEDBACK AND DEMAND CHARACTERISTICS 36

Table S3. Study 1 mean differences in self-reported happiness and anger after posing happy vs. neutral vs. angry facial expressions in the positive expectation, control, and null expectation conditions. Positive expectation Control Null expectation

Emotion Pose contrast t Mdiff 95% CI t Mdiff 95% CI t Mdiff 95% CI Happiness Happy – Neutral 16.33*** [1.12, 1.43] 10.82*** [0.73, 1.05] 4.25*** [0.18, 0.48] Happy – Angry 17.50*** [1.21, 1.52] 11.77*** [0.81, 1.13] 5.54*** [0.28, 0.58] Anger – Neutral -1.17 [-0.24, 0.06] -0.95 [-0.24, 0.08] -1.29 [-0.25, 0.05]

Anger Happy – Neutral -2.58** [-0.26, -0.04] 0.25 [-0.10, 0.13] 1.15 [-0.05, 0.18] Happy – Angry -14.34*** [-0.93, -0.71] -8.91*** [-0.66, -0.42] -2.94** [-0.28, -0.06] Anger – Neutral 11.76*** [0.56, 0.78] 9.16*** [0.43, 0.67] 4.09*** [0.12, 0.34] Note: All pairwise comparison tests had 1,235 degrees of freedom. Boxes with grey fill represent pairwise comparisons that the demand characteristics account predicts will be null. Boxes with dotted outline represent comparisons where emotional embodiment theorists do not make clear predictions. p-values are not corrected for multiple comparisons. **p < .01; ***p < .001 FACIAL FEEDBACK AND DEMAND CHARACTERISTICS

37

Study 2 Self-reported emotion Although we were primarily interested in examining the Pose by Demand interaction in Study 2, results from the full model are displayed in Tables S4 and S5. Pairwise comparisons for the decomposition of Pose by Demand interactions are displayed in Table S6. For both self- reported happiness and anger, we fit a linear mixed effects model with Pose, Block, and Demand entered as effects-coded factors, all higher-order interactions, compound symmetry covariance, random-intercepts for each participant, and Type III Sums of Squares. We used model-estimated marginal means for all pairwise comparison. FACIAL FEEDBACK AND DEMAND CHARACTERISTICS 38

Table S4. Full Study 2 results from a linear mixed effects model of happiness self-reports. F-values are from ANOVAs with Satterthwaite method degrees of freedom. Main effects were decomposed via pairwise difference tests. Interactions were decomposed by examining the effect of the first factor within each level of the second factor via contrasts. Omnibus test Decomposition Result Pose - F(2, 945) = 179.42, p < .001

Happy – Neutral t(945) = 14.28, p < .001, Mdiff 95% CI [0.77, 1.02]

Happy – Angry t(945) = 17.92, p < .001, Mdiff 95% CI [1.00, 1.25]

Anger – Neutral t(945) = -3.65, p < .001, Mdiff 95% CI [-0.35, -0.11] Demand - F(2, 189) = 5.04, p = .007

Positive – Control t(189) = 1.44, p = .15, Mdiff 95% CI [-0.05, 0.35]

Positive – Null t(189) = 3.18, p = .002, Mdiff CI [0.13, 0.54]

Control – Null t(189) = 1.75, p = .08, Mdiff 95% CI [-0.02, 0.39] Block - F(1, 945) = 10.80, p = .001

Block 1 – Block 2 t(945) = 3.29, p = .001, Mdiff 95% CI [0.07, 0.27] Pose by Demand - See main text See main text See main text Pose by Block - F(2, 945) = 1.26, p = .28 Demand by Block - F(2, 945) = 0.15, p = .86 Pose by Demand by Block - F(4, 945) = 0.16, p = .96

FACIAL FEEDBACK AND DEMAND CHARACTERISTICS 39

Table S5. Full Study 2 results from a linear mixed effects model of anger self-reports. F-values are from ANOVAs with Satterthwaite method degrees of freedom. Main effects were decomposed via pairwise difference tests. Interactions were decomposed by examining the effect of the first factor within each level of the second factor via contrasts. Omnibus test Decomposition Result Pose - F(2, 945) = 108.74, p < .001

Happy – Neutral t(945) = -0.75, p = .45, Mdiff 95% CI [-0.16, 0.07]

Happy – Angry t(945) = -13.13, p < .001, Mdiff 95% CI [-0.88, -0.65]

Anger – Neutral t(945) = 12.38, p < .001, Mdiff 95% CI [0.61, 0.84] Demand - F(2, 189) = 8.75, p < .001

Positive – Control t(189) = -3.14, p = .002, Mdiff 95% CI [-0.58, -0.13]

Positive – Null t(189) = 0.90, p = .37, Mdiff 95% CI [-0.12, 0.33]

Control – Null t(189) = 3.95, p < .001, Mdiff 95% CI [0.23, 0.69] Block - F(1, 945) = 0.28, p = .60 Pose by Demand - See main text See main text See main text Pose by Block - F(2, 945) = 0.47, p = .63 Demand by Block - F(2, 945) = 0.18, p = .83 Pose by Demand by Block - F(4, 945) = 0.37, p = .83

FACIAL FEEDBACK AND DEMAND CHARACTERISTICS 40

Table S6. Study 2 mean differences in self-reported happiness and anger after posing happy vs. neutral vs. angry facial expressions in the positive expectation, control, and null expectation conditions. Positive expectation Control Null expectation

Emotion Pose contrast t Mdiff 95% CI t Mdiff 95% CI t Mdiff 95% CI Happiness Happy – Neutral 10.36*** [0.89, 1.31] 7.62*** [0.61, 1.03] 6.84*** [0.55, 0.99] Happy – Angry 13.23*** [1.19, 1.61] 10.14*** [0.88, 1.30] 7.83*** [0.66, 1.10] Anger – Neutral -2.86** [-0.51, -0.10] -2.52* [-0.48, -0.06] -0.99 [-0.33, 0.11]

Anger Happy – Neutral -0.88 [-0.28, 0.11] -0.05 [-0.20, 0.19] -0.37 [-0.24, 0.17] Happy – Angry -9.09*** [-1.09, -0.70] -8.13*** [-1.01, -0.62] -5.64*** [-0.79, -0.38] Anger – Neutral 8.20*** [0.62, 1.00] 8.07*** [0.61, 1.01] 5.27*** [0.35, 0.76] Note: All pairwise comparison tests had 945 degrees of freedom. Boxes with grey fill represent contrasts that the demand characteristics account predicts will be null Boxes with dotted outline represent comparisons where emotional embodiment theorists do not make clear predictions. p-values are not corrected for multiple comparisons. * p < .05; **p < .01; ***p < .001 FACIAL FEEDBACK AND DEMAND CHARACTERISTICS

41

Study 3 Self-reported emotion Although we were primarily interested in examining the Pose by Demand interaction in Study 3, results from the full model are displayed in Tables S7 and S9. Pairwise comparisons for the decomposition of Pose by Demand interactions are displayed in Table S9. For both self- reported happiness and anger, we fit a linear mixed effects model with Pose, Block, and Demand entered as effects-coded factors, all higher-order interactions, compound symmetry covariance, random-intercepts for each participant, and Type III Sums of Squares, FACIAL FEEDBACK AND DEMAND CHARACTERISTICS 42

Table S7. Full Study 3 results from a linear mixed effects model of happiness self-reports. F-values are from ANOVAs with Satterthwaite method degrees of freedom. Main effects were decomposed via pairwise difference tests. Interactions were decomposed by examining the effect of the first factor within each level of the second factor via contrasts. Omnibus test Decomposition Result Pose - F(2, 645) = 81.87, p < .001

Happy – Neutral t(645) = 9.20, p < .001, Mdiff 95% CI [0.62, 0.95]

Happy – Angry t(645) = 12.30, p < .001, Mdiff 95% CI [0.88, 1.22]

Anger – Neutral t(645) = -3.11, p = .002, Mdiff 95% CI [-0.43, -0.10] Demand - F(1, 129) = 1.42, p = .23 Block - F(1, 645) = 10.14, p = .002

Block 1 – Block 2 t(645) = 3.18, p = .002, Mdiff 95% CI [0.08, 0.36] Pose by Demand - See main text See main text See main text Pose by Block - F(2, 645) = 1.38, p = .25 Demand by Block - F(1, 645) = 0.06, p = .81 Pose by Demand by Block - F(2, 645) = 0.41, p = .67

FACIAL FEEDBACK AND DEMAND CHARACTERISTICS 43

Table S8. Full Study 3 results from a linear mixed effects model of anger self-reports. F-values are from ANOVAs with Satterthwaite method degrees of freedom. Main effects were decomposed via pairwise difference tests. Interactions were decomposed by examining the effect of the first factor within each level of the second factor via contrasts. Omnibus test Decomposition Result Pose - F(2, 645) = 104.35, p < .001

Happy – Neutral t(645) = 0.90, p = .37, Mdiff 95% CI [-0.09, 0.24]

Happy – Angry t(645) = -12.04, p < .001, Mdiff 95% CI [-1.15, -0.83]

Anger – Neutral t(645) = 12.94, p < .001, Mdiff 95% CI [0.90, 1.22] Demand - F(1, 129) = 1.77, p = .19 Block - F(1, 645) = 0.00, p = .95 Pose by Demand - See main text See main text See main text Pose by Block - F(2, 645) = 1.18, p = .31 Demand by Block - F(1, 645) = 2.06, p = .15 Pose by Demand by Block - F(2, 645) = 0.81, p = .45

FACIAL FEEDBACK AND DEMAND CHARACTERISTICS 44

Table S9. Study 3 mean differences in self-reported happiness and anger after posing happy vs. neutral vs. angry facial expressions in the positive expectation and null expectation conditions. Positive expectation Null expectation

Emotion Pose contrast t Mdiff 95% CI t Mdiff 95% CI Happiness Happy – Neutral 8.37*** [0.79, 1.28] 4.53*** [0.30, 0.76] Happy – Angry 10.55*** [1.06, 1.55] 6.76*** [0.56, 1.02] Anger – Neutral -2.17* [-0.51, -0.03] -2.23* [-0.49, -0.03] Anger Happy – Neutral 0.18 [-0.21, 0.26] 1.11 [-0.10, 0.35] Happy – Angry -11.22*** [-1.57, -1.10] -5.67** [-0.86, -0.42] Anger – Neutral 11.40*** [1.12, 1.59] 6.78*** [0.54, 0.99] Note: All pairwise comparison tests had 645 degrees of freedom. Boxes with grey fill represent pairwise comparisons that the demand characteristics account predicts will be null. Boxes with dotted outline represent comparisons where emotional embodiment theorists do not make clear predictions. p-values are not corrected for multiple comparisons. * p < .05; **p < .01; ***p < .001 FACIAL FEEDBACK AND DEMAND CHARACTERISTICS 45

The Role of Participant Belief Although Studies 1-3 were designed to test our primary interest in demand characteristics, we collected additional data on the degree to which participants believed posing facial expressions would impact their emotions (0 = “strongly believe that posing facial expressions can not influence their emotions” to 4 “strongly believe that posing facial expressions can influence emotions”). In Study 1, experimenters rated participants’ beliefs based on funnel debriefing. In Studies 2 and 3, participants self-reported their beliefs. Conceptually, demand characteristics are separable from participants’ beliefs about an effect. It is generally assumed that demand characteristics influence behavior because participants want to help confirm the study hypothesis (Orne, 1962) or appease the experimenter (Rosenberg et al., 1969)—not because they believe the experimenter’s hypothesis. Thus, our key manipulation check was the observed effects of the demand characteristics manipulations on participants’ awareness of our interest in testing facial feedback effects. Nevertheless, we reasoned that some participants may believe the experimenter’s stated hypothesis—as frequently documented in research on placebo effects (Crum & Phillips, 2015). Consequently, in all three studies, we pre-registered our expectation that demand characteristics would impact participants’ beliefs about facial feedback effects. We examined this hypothesis first with data from each study separately and then with data pooled from the three studies. The effects of demand characteristic manipulations on participants’ beliefs In Study 1, results from a one-way ANOVA indicated that ratings of participants’ belief about facial feedback effects differed across the three Demand conditions, F(2, 231) = 27.35, p < .001. Follow-up pairwise comparisons indicated that participants were judged to be more convinced that facial feedback influences emotion in the positive expectation (M = 2.84, SD =

1.22) vs. control (M = 2.31, SD = 1.39), t(231) = 2.37, p = .02, Mdiff 95% CI [0.09, 0.96], and

null expectation condition (M = 1.34, SD = 1.39), t(231) = 7.31, p < .001, Mdiff 95% CI [1.09, 1.90]. Participants were also judged to be more convinced that facial feedback influences

emotion in the control vs. null expectation condition, t(231) = 4.40, p < .001, Mdiff 95% CI [0.54, 1.41]. These results provided initial evidence that demand characteristics can impact participants’ beliefs about facial feedback effects. These results were partially replicated in Study 2. Results from a one-way ANOVA indicated that participants’ belief in facial feedback effects differed across the three Demand FACIAL FEEDBACK AND DEMAND CHARACTERISTICS 46

conditions, F(2, 182) = 4.49, p = .01. Consistent with Study 1, follow-up pairwise comparisons indicated that participants were less convinced that facial feedback influences emotion in the null expectation (M = 2.28, SD = 1.28) vs. positive expectation (M = 2.80, SD = 1.09), t(182) = -2.50,

p = .01, Mdiff 95% CI [-0.92, -0.11], and control condition (M = 2.84, SD = 1.04), t(192) = -2.73,

p = .007, Mdiff 95% CI [-0.97, -0.16]. Contrary to Study 1, though, results did not indicate that participants were more convinced that facial feedback influences emotion in the positive

expectation vs. control conditions, t(182) = -0.23, p = .82, Mdiff 95% CI [-0.44, 0.35]. Study 3 did not provide significant evidence that demand characteristics impacted participants’ beliefs about facial feedback effects. Although participants tended to be more convinced that facial feedback influences emotion in the positive expectation (M = 2.77 , SD = 1.24) vs. null expectation condition (M = 2.47 , SD = 1.37), this difference was not statistically significant, t(125) = 1.29, p = .20, Mdiff 95% CI [-0.16, 0.76]. Studies 1-3 each had high power to detect medium-sized effects of demand characteristics on participants’ beliefs about facial feedback effects. Although the three studies yielded similar patterns of results, inconsistent significance levels suggest that this effect—if real—is small. To generate a more precise estimate of this effect, we post-hoc re-examined the hypothesis using data pooled from the three studies (Cooper & Patall, 2009). The pooled analysis yielded similar results as the study-by-study analyses. A pooled one-way ANOVA confirmed that participants’ belief in facial feedback effects varied across the three Demand conditions, F(2, 543) = 23.16, p < .001. Most critically, participants were less convinced that facial feedback influences emotion in the null expectation (M = 1.96, SD = 1.45) vs. positive expectation condition (M = 2.80, SD = 1.18), t(543) = -6.64, p < .001, Mdiff 95% CI [-1.10, -0.60]. Participants were also less convinced that facial feedback influences emotion in the null expectation vs. control condition (M = 2.58, SD = 1.25), t(543) = -4.24, p < .001, Mdiff 95% CI [- 0.91, -0.33]. Results, however, provided only marginally significant evidence that participants were more convinced that facial feedback influences emotion in the positive expectation vs.

control condition, t(543) = 1.55, p = .12, Mdiff 95% CI [-0.06. 0.51] (one-sided p = .06). In other words, across the three studies, we observed that demand characteristics can weaken beliefs in facial feedback effects, but we did not observe clear evidence that demand characteristics can strengthen beliefs in facial feedback effects. FACIAL FEEDBACK AND DEMAND CHARACTERISTICS 47

Taken together, these results indicate that demand characteristics and participants’ beliefs are related but distinct. This unexpected observation raises the possibility that a different study artifact drives facial feedback effects: participants’ beliefs. The moderating role of participants’ beliefs To test the moderating role of participants’ beliefs post-hoc, we modeled self-reported happiness and anger using linear mixed-effects regression with Pose entered as an effects-coded factors, Belief scores entered mean-centered, a Pose by Belief interaction, compound symmetry covariance structures, random-intercepts for each participant, and Type III Sums of Squares. Similar results were obtained when Block, Study, and related higher-order interactions were included in the model, but we report results from the simpler model below. For both self-reported happiness and anger, results indicated that there were significant Pose by Belief interactions: happiness F(1, 2726) = 121.10, p < .001; anger F(1, 2726) = 66.23, p < .001. To decompose this interaction, we coded whether participants indicated that they believed (n = 344; 63% of sample), did not believe (n = 154; 28% of sample), or were unsure if posing facial expressions can influence emotions (n = 48; 9% of sample). To directly compare facial feedback effects amongst believers vs. nonbelievers, we removed participants who were unsure if posing facial expressions can influence emotions. As shown in Figure S2, Pose effects were largest amongst participants who believed in facial feedback effects (happiness F(2, 2486) = 515.72, p < .001; anger F(2, 2486) = 326.96, p < .001) and smallest among participants who did not believe in facial feedback effects (happiness F(2, 2486) = 15.00, p < .001; anger F(2, 2486) = 23.55, p < .001). Follow-up pairwise comparisons indicated that, regardless of whether they believed in facial feedback effects, participants (a) reported more happiness after posing happy vs. neutral and angry expressions, and (b) reported more anger after posing angry vs. neutral and happy expressions (see Figure S2). These results provide preliminary evidence that facial feedback effects are not merely an artifact of participants’ beliefs. FACIAL FEEDBACK AND DEMAND CHARACTERISTICS 48

Fig. S2. Self-reported happiness and anger for participants who posed happy, neutral, and angry expressions and either believed or did not believe in facial feedback effects. Bar heights represent estimated marginal means and error bars represent one standard error. Emotion reports were measured on a 7-point scale, but the y-axis is truncated for ease of visualization. Full brackets represent hypothesis-relevant directional pairwise comparisons and dotted brackets represent non-directional pairwise comparisons that facial feedback theorists do not make clear predictions about. All pairwise comparison tests had 2486 degrees of freedom. *p < .05; **p < .01; ***p < .001