Social perspective-taking as a catalyst to building common ground between opposing parties

Social perspective-taking as a catalyst to building common ground between opposing parties | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Social perspective-taking as a catalyst to building common ground between opposing parties Li Li, Lindi Shepard, Lisa Nehring, Nan Mu, Katherine Cornwall, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8234296/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Democracies thrive on constructive conversations between stakeholders with divergent opinions, yet building common ground between these stakeholders to facilitate such conversations appears more difficult than ever. How can opposing parties establish common ground when preexisting shared experiences, traits, or values are not easily uncovered? Drawing on an online sample of U.S. adults ( N = 558), this preregistered randomized controlled experiment tested social perspective-taking (SPT) as a promising strategy to create common ground between people who hold opposing opinions on a polarizing policy. Specifically, in two treatment conditions, a fictional expert took the perspective of participants by either affirming their (a) reasonable logic or (b) good intentions. The two interventions created a sense of common ground and improved multiple relationship-related outcomes, including perceived perspective-taking, perceived similarity, perceived fairness, and anticipated positive relationship with the opposing expert. Estimated effect sizes further suggest that affirming the other party’s logic was about twice as effective as affirming their intentions. In the present historical moment when society faces acute polarization threats, our study provides a recipe for effectively communicating SPT attempts that can catalyze common ground where none existed previously. Social science/Psychology/Human behaviour Scientific community and society/Social sciences/Communication Scientific community and society/Social sciences/Climate change Scientific community and society/Social sciences/Psychology/Human behaviour Social science/Education education controversies environmental issues social perspective-taking political polarization Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction In the current politically polarized times, engaging in the constructive conversations that democracy requires has become acutely challenging. Dozens of issues—immigration, affordable health care, climate crisis, etc.— require opposing parties to come together to reach collective solutions. However, productive conversations to reach these collective solutions seem especially rare (Falkenberg et al., 2024 ). To address these divides, numerous advocacy groups have emerged to attempt establishing common ground between parties with opposing points of view—Search for Common Ground, Braver Angels, Common Ground USA, More in Common, and many others. These organizations’ implicit logic is that identifying shared experiences or traits—the recent loss of a loved one, a fear of flying, valuing family over work, etc.—may facilitate conversation on a topic where they disagree because they have already established a common bond—even if that connection lies in an entirely different domain. Yet, identifying common ground may be impractical in many situations. Opposing parties may be brought to a negotiating table before they can establish a prior relationship. Searches to identify meaningful similarities may end unsuccessfully. Situational factors may preclude the opportunity to seek common ground—for example, a town hall setting with multiple factions present might prevent key actors from being vulnerable enough to reveal meaningful similarities. These challenges raise the question: Can common ground be actively constructed during the conversation rather than identified through pre-existing similarities? Social Perspective-Taking and Its Relational Benefits In cases where identifying shared experiences or traits elides the opposing parties, they may still establish some semblance of common ground by finding merit in some portion of the other party’s perspective. The last author drew the inspiration for this study from a conversation with colleagues from the Environmental Defense Fund. The colleagues led town hall meetings in the heart of coal-mining regions in the United States. Their goal was to convince local residents—many of whom were coal miners—to embrace cleaner sources of energy. Astonished that such a meeting did not result in the colleagues being chased out of town, he asked what their strategy was. They responded that the first 20 minutes of their presentation described the amazing historical benefits of coal—revolutionizing transportation, providing home heating for millions, eradicating hunger, etc. In other words, without meeting the town hall participants ahead of time, they put effort into finding merit in the other party’s perspective by anticipating and affirming arguments that the residents were likely to embrace. Notably, the mere act of trying hard to take the coal miners’ perspectives did not ensure accurate inferences about their point of view. Nevertheless, the efforts to affirm the other side appeared to have been appreciated. According to Gehlbach and Mu ( 2023 ), social perspective-taking (SPT), the process through which a perceiver figures out others’ thoughts, feelings, and motivations, encompasses an ability component (some individuals may be better at “reading” people than others) and a motivational component (some individuals may try harder or more frequently to understand where others are coming from). Because this latter component of SPT is under individuals’ control, it may be a promising mechanism through which common ground might be established with the hope that relational benefits ensue. A modest but growing set of studies supports this idea. For example, Goldstein et al. ( 2014 ) found that participants who felt their perspectives were taken by a stranger showed greater liking, engaged in more helping behaviors, and perceived greater similarity with the perspective-taker. In a virtual reality simulation, Gehlbach et al. ( 2015 ) found similarly strong connections between participants’ perceptions that someone else was trying hard to take their perspective and their relationship with that person. If the relational benefits of effortful SPT persist in important contexts where parties hold opposing positions but need to reach an agreement or compromise, then it would offer a pathway towards enabling these pivotal conversations. Different Approaches to Demonstrating SPT Attempts Although these studies recognize SPT as a promising mechanism for creating common ground, they provide little insight into exactly how that attempt might be most wisely deployed. When anticipating the specifics of another party’s mental state, perceivers may need to choose the domain they want to discern. Should a perceiver try to identify merits in the arguments the other side is likely to make? Or would the perceiver be better off trying to affirm the underlying intentions of the other party? Most prior studies show the promise of the former approach. For example, Stanley et al. ( 2020 ) found that when people learn more about their political opponents’ arguments, their impressions of the opponents improved. Xu and Petty ( 2022 ) also found that, after delivering a counter-attitudinal message, adding a message that respectfully acknowledges the other side’s arguments fosters increased openness to opposing ideas. Additionally, thanks to people’s innate motivation to show up as a good person (Steele, 1988 ; Sherman & Cohen, 2006 ), it is reasonable to infer that acknowledging others’ intentions could have benefits as well. The Current Study The current study compares whether demonstrating SPT by affirming the logic of an opposing party’s argument or their underlying intentions was more effective in creating a sense of common ground and promoting relationship-related outcomes. We examine these approaches–logic-affirming SPT and intention-affirming SPT –to establishing common ground in the context of simulated opinion exchanges about a climate change education policy between participants and an “expert” who holds the opposite point of view. We first hypothesized that: H1: Participants being exposed to either SPT condition would perceive that the expert did a better job of taking their perspective. To evaluate differences in perceptions of common ground, we assessed participants’ perceived similarity with the expert (Montoya et al., 2008 ) and perceived fairness. We hypothesized that: H2: Participants being exposed to either SPT condition would perceive greater similarity with the expert, H3: and that they would perceive the expert and the information presented as fairer. Given the robust association between perceived similarity and liking (Montoya et al., 2008 ; Tidwell et al., 2013 ; Sprecher, 2014 ), we further hypothesized that: H4 Participants being exposed to either SPT condition would anticipate a more positive relationship with the expert. We also assumed that participants who perceived that the expert was actively taking their perspective might reciprocate (Gouldner, 1960 ). Therefore, we hypothesized that: H5: Participants in either SPT condition would report putting forth more effort into taking the expert’s perspective, and H6: they would exhibit greater opinion change in the direction that the expert advocated. Because people tend to favor information that confirms their pre-existing beliefs (Nickerson, 1998 ) and supports positions that align with their political affiliations (Van Boven et al., 2018 ), we controlled for the strength of participants’ pre-existing beliefs and political orientation. Methods In line with open science recommendations (Gehlbach & Robinson, 2018 ; 2021 ), our study’s hypotheses were preregistered on the Open Science Framework (). The procedures, measures, and intervention materials are also available via the same link; data and Stata code will be posted after publication. The study was approved by the university's institutional review board before data collection began. Participants We recruited an initial sample of 579 adult participants currently residing in the United States from Prolific ’s survey platform. In line with our preregistration, we excluded participants who engaged in “straight-line” responding (Vriesema & Gehlbach, 2021 ), that is, who responded to 12 consecutive questions with identical responses ( n = 21). In the final sample ( N = 558), about 60.0% of the participants identified as female and 37.5% as male. The majority of participants self-identified as White/Caucasian (61.6%), followed by Black or African American (12.0%) and Asian/Pacific Islander (11.5%). Participants’ average age was around 39. An average participant tended to have liberal views ( M = 3.21 on a scale of 1 = “very liberal” to 7 = “very conservative” with 4 = “moderate”). About 68.6% of the participants reported having learned about environmental science in a formal school setting. The median education level of participants was holding a four-year college degree. The average socioeconomic status of the participants was 4.67 on a self-reported scale of one (highest) to nine (lowest). Table 1 presents the descriptive statistics of participants’ demographic characteristics. Table 1 Characteristics of Participants by Treatment Groups (N = 558) Control Logic-Affirming SPT Treatment Intention-Affirming SPT Treatment Full Sample n (%) n (%) n (%) n (%) N 185 (33.2%) 189 (33.9%) 184 (33.0%) 558 (100.0%) Gender (n = 558) Male 68 (36.8%) 66 (34.9%) 75 (40.8%) 209 (37.5%) Female 112 (60.5%) 118 (62.4%) 105 (57.1%) 335 (60.0%) Nonbinary, other, or prefer not to answer 5 (2.7%) 5 (2.6%) 4 (2.2%) 14 (2.5%) Race (n = 557) American Indian or Alaskan native 1 (0.5%) 1 (0.5%) 1 (0.5%) 3 (0.5%) Asian/Pacific Islander 18 (9.7%) 24 (12.7%) 22 (12.0%) 64 (11.5%) Black or African American 19 (10.3%) 22 (11.6%) 26 (14.2%) 67 (12.0%) Latino/a or Hispanic American 6 (3.2%) 15 (7.9%) 3 (1.6%) 24 (4.3%) Middle Eastern 1 (0.5%) 0 (0.0%) 0 (0.0%) 1 (0.2%) White/Caucasian 119 (64.3%) 109 (57.7%) 115 (62.8%) 343 (61.6%) Mixed Races or other 21 (11.4%) 18 (9.5%) 16 (8.7%) 55 (9.9%) mean (sd) mean (sd) mean (sd) mean (sd) Environmental Education (yes = 1, no = 0; n = 558) 0.74 (0.44) 0.67 (0.47) 0.66 (0.48) 0.69 (0.46) Age (n = 551) 37.88(12.12) 38.31(12.91) 40.81(13.72) 38.99(12.98) Political Orientation (n = 557) 3.11 (1.62) 3.20 (1.82) 3.31 (1.83) 3.21 (1.76) Educational Level (n = 558) 15.00 (2.02) 14.98 (1.88) 15.27 (1.93) 15.08 (1.95) Socioeconomic Status (n = 551) 4.56 (1.80) 4.65 (1.61) 4.80 (1.60) 4.67 (1.67) Procedure This study utilized a 1 x 3, between-subjects randomized controlled design. Participants completed the study through a Qualtrics survey. Participants first read a brief introduction to New Jersey’s climate change education mandate (New Jersey Department of Education, 2024 ). They then indicated how strongly they supported or opposed a potential national mandate to require the teaching of climate change education in all K-12 public schools. After stating their initial opinion, participants in each group (supporting or opposing) were randomly assigned to one of the two treatment groups or the control group through Qualtrics. All participants read a mock Facebook post from the same expert teacher, “Alex,” who presented arguments countering their initial opinions (i.e., those who initially opposed the mandate received pro-national mandate arguments for climate change education; those who initially favored the mandate read anti-national mandate arguments). Participants in the logic-affirming SPT group first had the validity of their arguments affirmed before seeing the counter-attitudinal arguments. For example, initial supporters in the logic treatment group first read that “Learning about the impact of climate change is directly relevant to students’ future.” Those in the intention-affirming SPT group were first shown text that assumed and affirmed the good intentions behind their initial opinion. For both initial supporters and opposers, their text included intentions such as “You probably want what’s best for the children in our communities.” Participants in the control condition saw only counter-attitudinal arguments. Following the intervention, participants were asked to re-evaluate their opinion on the issue, complete outcome measures, and provide demographic information. The overall flow of the procedure and the Facebook posts can be found in the Supplementary Materials (Figures A.1-A.4; attached at the end of this document to facilitate the review process). Measures Our primary outcome measures of interest consisted of scales assessing participants’ perceptions of the speaker’s social perspective-taking, participants’ evaluations of the speaker’s similarity and fairness, and potential for a positive relationship; participants’ self-evaluation of their own SPT effort; and a single measure of the magnitude of opinion change towards the other side of the issue. Scores for composite scales were computed by taking the average of all items on each scale. Kurtosis and the skewness of covariates and outcome variables fell within conventional thresholds (+/- 2.0 for skewness and +/- 7.0 for kurtosis; West et al., 1995 ). We present descriptive statistics and correlations of variables in Table 2 . A complete list of individual items for each composite scale can be found in the preregistered codebook (and Table A.1 of our Supplementary Materials—appended at the end of this manuscript to facilitate the review process). Table 2 Psychometric Properties and Pairwise Correlations for Covariates and Outcome variables (N = 558) Variables N Mean SD Min Max Skewness Kurtosis α 1 2 3 4 5 6 7 8 1. Political Orientation 557 3.21 1.76 1 7 0.44 2.23 - - 2. Initial Opinion 558 7.54 2.77 1 10 -1.10 2.97 - -0.57* - 3. Perceived SPT 558 2.80 0.99 1 5 -0.02 2.16 .86 -0.14* 0.17* - 4. Perceived Similarity 558 2.74 1.07 1 5 0.09 2.10 .89 -0.08 0.12* 0.78* - 5. Perceived Fairness 558 2.50 0.87 1 5 0.32 2.57 .91 -0.12* 0.16* 0.85* 0.71* - 6. Anticipated Relationship 558 3.18 0.87 1 5 -0.25 2.60 .88 -0.07 0.10* 0.76* 0.76* 0.71* - 7. Personal SPT Effort 558 3.28 0.80 1 5 -0.14 3.06 .86 -0.04 0.12* 0.25* 0.19* 0.22* 0.26* - 8. Opinion Change 558 0.80 1.76 -4 9 1.94 6.79 - -0.01 0.13* 0.36* 0.32* 0.35* 0.34* 0.14* - * p < .05 Perceived SPT (ɑ = .86): Adapted from Gehlbach et al. ( 2015 ), this four-item, five-point response scale assessed the extent to which participants perceived the speaker to take their perspective. Participants rated how much effort, motivation, clarity, and accuracy they thought the speaker had to understand their perspectives. One sample item is “In writing the post, how much effort do you think the teacher put into taking the perspective of people like you on this issue?” Perceived similarity (ɑ = .89): This four-item, five-point response scale assessed how similar participants perceived the speaker to be to them, in terms of overall similarity, similarity in personality, opinions, and attitudes on the same issues. Adapted from Gehlbach et al. ( 2012 ), this scale includes items such as “Overall, how much would you guess that you have in common with the teacher?” Perceived fairness (ɑ = .91): This four-item, five-point response scale measured the extent to which participants perceived the speaker and the information they gave to be fair. We also asked how similar the quality of information was on both sides of the issue and how balanced the information seemed to be. An example item is “Overall, how fair was the teacher's treatment of each side of the issue?” Anticipated relationship (ɑ = .88): Adapted from the teacher-student relationship positivity scale used in Gehlbach et al. ( 2012 ), this four-item, five-point response scale measured how positive of a relationship they would imagine having with the expert. We asked participants to rate the extent to which they would enjoy a conversation, as well as how friendly, respectful, and caring they believed the expert would be, e.g., “How much do you think you would enjoy having a conversation with the teacher?” Personal SPT effort (ɑ = .86): Adapted from Gehlbach et al. ( 2015 ), this five-item, five-point response scale measured participants’ self-perception of how much effort they exerted in trying to understand the opposing viewpoints, as well as understanding the motivations, goals, feelings, and thoughts of people supporting the other side. The scale includes items such as “How hard did you try to understand the merits of the opposing points of view on this issue?” Opinion change The scores on this continuous variable measured the amount that participants’ opinions shifted towards the opposing side, with negative values indicating that participants’ initial opinions became more deeply entrenched. We first obtained participants’ scores for initial opinion and final opinion measures, which ranged from 1 = “strongly oppose” to 10 = “strongly support” based on how strongly they opposed or supported such a national mandate, with no neutral point. To construct the opinion change measure, for initial opposers, we subtracted their initial opinion from the final opinion ; for initial supporters, we took the additive inverse of the final-minus-initial scores, so that positive values always indicated opinion change in the direction of the expert. Beyond these focal outcome measures, we asked participants, “Generally speaking, how would you classify your political orientation?” The scores ranged from 1 = “very liberal” to 7 = “very conservative”, with 4 = “moderate”. We used the initial opinion and political orientation as covariates. Considering the moderately high correlation between these two covariates ( r = − .57, p < 0.1 ) , we calculated the variance inflation factor (VIF; Allison, 2012 ) for each of the two variables and ruled out multicollinearity (VIF for both variables = 1.47). The study also included three manipulation checks. Participants indicated on a slider scale the percentage of information from the speaker that favored the climate change education mandate ( manipulation1) , opposed the mandate ( manipulation2 ), or acknowledged the underlying intentions of participants ( manipulation3 ). Analytic Approach Data analyses were conducted using Stata/SE 18.0. First, to see whether our randomization had created balanced treatment and control groups, we used our demographic variables—race/ethnicity, gender, age, educational level, environmental education experience, and socioeconomic status—to fit a multinomial logistic regression model predicting assignment to each treatment condition and the control group. Next, we evaluated whether the interventions worked as intended for the two treatment groups by comparing mean differences in our manipulation checks. We then tested each of our six hypotheses outlined in the preregistration through fitting the following OLS regression model: $$\:{Y}_{i}=\:{\beta\:}_{0}+\:{\beta\:}_{1}Conditio{n}_{1}+\:{\beta\:}_{2}Conditio{n}_{2}+\:\mu\:{X}_{i}+\:{ϵ}_{i}$$ where Y is the measured outcome for participant i ( perceived SPT , perceived similarity , perceived fairness , anticipated relationship , personal SPT effort , and opinion change ); Condition is an indicator for assignment to conditions (1 = Logic; 2 = Intention); \(\:{X}_{i}\) is a vector of covariates that included political orientation and initial opinion ; and \(\:{ϵ}_{i}\) is the error term. Following White ( 1980 ) and Wooldridge ( 2020 ), we calculated robust standard errors for all regressions using the robust function in Stata. Following Cumming’s ( 2014 ) guidance, we used 95% confidence intervals and effect sizes, rather than p values, to evaluate each hypothesis. Finally, considering opinion change is a function of initial and final opinions, we also fit the model to predict opinion change without the initial opinion covariate. This step was added after the preregistration as a sensitivity check to examine whether the preregistered model to examine opinion change might be biased. Results Preliminary Analyses Our multinomial logistic regression results (see Table 3) showed that the demographic variables of race/ethnicity, gender, age, educational level, environmental education experience, and socioeconomic status did not significantly predict assignment to different conditions. Therefore, in line with our preregistration, we did not include any of these variables as additional covariates in our model. Table 3 Multinomial Logistic Regression Results (n = 558) Treatment Groups Coefficient SE t p [95% CI] Logic-affirming SPT Gender .024 .199 0.12 .903 − .365 .414 Race/Ethnicity − .106 .068 -1.57 .116 − .239 .026 Environmental Education − .329 .245 -1.34 .180 − .808 .151 Age 0 .009 0.05 .961 − .017 .018 Educational Level − .016 .059 -0.27 .788 − .130 .099 Socioeconomic status .029 .071 0.40 .687 − .111 .168 Constant .876 .956 0.92 .360 − .998 2.749 Intention-affirming SPT Gender − .201 .202 -0.99 .321 − .597 .196 Race/Ethnicity − .086 .069 -1.24 .213 − .222 .050 Environmental Education − .352 .246 -1.43 .152 − .833 .129 Age .014 .009 1.57 .116 − .003 .031 Educational Level .032 .062 0.51 .610 − .090 .153 Socioeconomic status .059 .071 0.82 .410 − .081 .199 Constant − .482 .963 -0.50 .616 -2.37 1.405 Mean dependent variables 0.998 SD dependent variables 0.814 Pseudo r-squared 0.012 Number of observations 543 Chi-square 14.222 Prob > chi2 0.287 Akaike crit. (AIC) 1206.799 Bayesian crit. (BIC) 1266.958 Note. *** p < .01, ** p < .05, * p < .1. The baseline group is the control group. SE = standard error In addition, t -tests of the manipulation check items (see Table A.2 in the Supplementary Materials) showed that participants in the logic-affirming SPT groups perceived the post they read to contain significantly higher percentage of information that was on their side, both for initial supporters ( M logic = 26.99, M control = 8.40, t (282) = -9.26, p < .001, 95% CI [-22.54, -14.64]) and initial opposers ( M logic = 30.40, M control = 3.84, t (88) = -6.24, p < .001, 95% CI: [-35.03, -18.10]). Those in the intention-affirming SPT group perceived the message to contain more information that acknowledged the positive intentions behind their point of view than the control group, both for initial supporters ( M intention = 41.58, M control = 26, t (279) = -5.28, p < .001, 95% CI [-21.39, -9.77]) and initial opposers ( M intention = 35.80, M control = 17.12, t (86) = -3.11, p = 0.0025, 95% CI: [-30.62, -6.75]). Therefore, we concluded that manipulations of both interventions were functioning as intended. Pre-specified Hypotheses Next, to test our pre-specified hypotheses, we fit regression models predicting the effect of treatment on the outcomes while controlling for initial opinion and political orientation. Table 4 presents the unstandardized regression output and the effect sizes of the treatments (see Table A.3 in supplementary materials for more detailed regression output of the models). Figures 1–6 present the mean score comparisons of treatment effects on focal outcomes. Table 4 Unstandardized Regression Output for Preregistered Hypothesis Testing (N = 557) Outcome Variables Treatment Condition b (robust SE) t p-value 95% CI Cohen’s d adj. R 2 Perceived SPT Logic-affirming SPT 0.99 (0.09) 11.04 < .001 [0.82, 1.17] 1.12 .20 Intention-affirming SPT 0.40 (0.09) 4.30 < .001 [0.22, 0.58] 0.45 Perceived similarity Logic-affirming SPT 0.55 (0.09) 6.49 < .001 [0.39, 0.72] 0.66 .08 Intention-affirming SPT 0.27 (0.09) 3.17 .002 [0.10, 0.44] 0.33 Perceived fairness Logic-affirming SPT 1.27 (0.09) 13.73 < .001 [1.09, 1.45] 1.37 .26 Intention-affirming SPT 0.51 (0.10) 5.33 < .001 [0.32, 0.70] 0.56 Anticipated relationship Logic-affirming SPT 0.57 (0.09) 6.58 < .001 [0.40, 0.74] 0.69 .08 Intention-affirming SPT 0.30 (0.09) 3.43 .001 [0.13, 0.47] 0.36 Personal SPT effort Logic-affirming SPT 0.20 (0.08) 2.40 0.017 [0.03, 0.36] 0.25 .03 Intention-affirming SPT 0.10 (0.08) 1.23 0.220 [-0.06, 0.26] 0.13 Opinion change a Logic-affirming SPT 0.23 (0.18) 1.30 0.195 [-0.12, 0.58] 0.13 .02 Intention-affirming SPT 0.09 (0.18) 0.52 0.602 [-0.25, 0.44] 0.05 Opinion change b Logic-affirming SPT 0.23 (0.18) 1.31 0.192 [-0.12, 0.59] 0.13 .00 Intention-affirming SPT 0.10 (0.18) 0.58 0.564 [-0.25, 0.45] 0.06 Note. SE = standard error. a Regression output for the preregistered model with initial opinion covariate. b Regression output for the adjusted model without initial opinion covariate. As predicted, results showed that participants in both the logic-affirming and intention-affirming SPT groups perceived that the expert did a better job taking their perspectives ( b logic = 0.99, 95% CI = [0.82,1.17]; b intention = 0.40, 95% CI = [0.22, 0.58], adjusted R 2 = 0.20). Participants in both treatment groups also perceived greater similarity with the speaker ( b logic = 0.55, 95% CI = [0.39, 0.72]; b intention = 0.27, 95% CI = [0.10, 0.44], adjusted R 2 = 0.08). They perceived the expert and the information presented to be fairer ( b logic = 1.27, 95% CI = [1.09, 1.45]; b intention = 0.51, 95% CI = [0.32, 0.70]; adjusted \(\:{R}^{2}\) = 0.26), and they anticipated a more positive relationship with the expert ( b logic = 0.57, 95% CI = [0.40, 0.74]; b intention = 0.30, 95% CI = [0.13, 0.47], adjusted R 2 = 0.08). As shown in Table 4, the logic-affirming SPT treatment produced effects that were approximately twice those of the intention-affirming SPT treatment on the outcomes. Furthermore, we tested the hypothesis of whether participants in both treatments reciprocated the expert’s SPT efforts by putting forth more personal SPT effort toward the expert. The logic treatment condition was effective ( b = 0.20, 95% CI = [0.03, 0.36], adjusted R 2 = 0.03), while the effect of intention treatment was small, positive, but not significant ( b = 0.10, 95% CI = [-0.06,0.26], adjusted R 2 = 0.03). Finally, we tested whether treatment participants might exhibit a greater shift in their attitudes towards the position they initially opposed. The analysis of both the preregistered model with the initial opinion covariate and the additional model without the covariate produced similarly small differences between our treatment groups and the control participants, with confidence intervals that included zero. Discussion Results showed that both approaches to SPT—affirming the assumed logic of the other’s position or validating their underlying intentions—were effective. Not only did treatment participants perceive that the expert took their perspective to a greater degree, but the interventions also allowed for a sense of common ground to be created. In essence, treatment participants perceived the expert as more similar to themselves, both in personality and in attitudes. In response, these participants recognized the merits in the arguments the expert made as well, perceiving the speaker to have fairly presented and understood the issue. In addition to building a sense of common ground, the interventions led to additional relationship-related benefits— e.g., anticipating a more positive future relationship with the expert. The logic-affirming SPT intervention further encouraged participants to put forth more effort to take the speakers’ perspectives. In sum, these results suggest participants’ improved perceptions of the speaker. For most outcomes, effect sizes indicated that the logic-affirming SPT treatment had twice the benefit of the intention-affirming SPT treatment. The results highlight two findings that are of theoretical importance to the SPT literature. First, prior evidence suggests that SPT can foster a better relational climate with better liking, more perceived similarity, and pro-social behaviors (e.g., Goldstein et al., 2014 ; Gehlbach et al., 2015 ). Our study’s results reinforce and extend this prior literature. Specifically, it seems that creating a sense of common ground, where both parties agree on certain merits of one party’s point of view, is also possible via SPT. Notably, the results suggest that the benefits of these SPT attempts may generalize to a social context where people might most need to establish common ground and positive relationships—in a disagreement over controversial and divisive topics. Second, the study adds a key nuance to the SPT literature by focusing on the kinds of SPT attempts that might matter more for relationship-related outcomes. We found SPT attempts to be more effective when they emphasized the merits of the other side’s logic, rather than their positive intentions. All approaches to SPT may not be equal. We know that perceivers vary in their use of SPT strategies (Gehlbach & Brinkworth, 2012 ). This study raises the possibility that the substantive focus of what perceivers try to infer may also be an important influence on the outcomes of SPT attempts. Future research that tests other foci of SPT attempts, such as inferring a target’s future expectations, their preferences between choices, or their emotions, might be particularly valuable. The advantage of logic-affirming over intention-affirming SPT was unexpected; our research team had tentatively expected the opposite—that validating someone else’s deeper intentions would more deeply resonate with the other party’s core identity (Steele, 1988 ; Sherman & Cohen, 2006 ). A possible explanation might be participants’ preferences in social judgement. The stereotype content model (Cuddy et al., 2008 ; Fiske, 2018 ) suggests there are two dimensions of social perceptions: warmth and competence. “Warm” people embody traits such as trustworthiness, friendliness, honesty, and likability; “competent” individuals are considered intelligent and capable (Fiske, 2018 ). While warmth may matter more in the evaluation of strangers, competence matters more in self-evaluation (Cuddy et al., 2011 ). In our case, the SPT treatments might have communicated the participant’s warmth through affirmation of their intentions, and competence by affirming their logic. If competence were more important for our participants’ self-evaluations, this implicit feedback message could have felt better to participants, thereby explaining why the logic-affirming SPT condition produced a more powerful sense of liking and connection. An alternative explanation involves the different levels of abstraction that might be present in the two treatment conditions. When the expert communicated a specific understanding of the concrete reasons the participants might have in mind, it might make treatment participants feel “seen” in a psychologically meaningful way. This interpretation is consistent with the prior evidence (e.g., Stanley et al., 2020 ; Xu & Petty, 2022 ), which emphasizes the importance of thoughtful messages that show nuanced appreciation of the other side’s opinion. The intention-affirming SPT intervention, on the other hand, acknowledged what the participant would probably want, which may have seemed like a higher level of abstraction (construal level theory, Trope & Liberman, 2010 ). The expert’s attempt to validate participants’ intentions may therefore have felt like more of a token effort that landed on them with more psychological distance and less psychological impact. The advantage of logic-affirming SPT might also be tied to research on social cognitive conflict resolution. According to Butera et al. ( 2019 ), when people entertain conflicting ideas about the same issue, trying to find the answer to the issue itself rather than focusing on social opposition would lead to positive learning and relational outcomes. Therefore, when the expert affirmed potential arguments participants might have for the opposite side, participants may have perceived this as an effort to achieve a mutual epistemic learning of the topic. On the other hand, when the speaker affirms that the participants have good intentions, which may often be considered a way of indicating that they are a good person, the effort may be seen as trying to solve interpersonal opposition, which inadvertently shifts the nature of the disagreement from substance to interpersonal. A greater unease for potential interpersonal conflict might be triggered as a result. Practical Implications Important, but potentially polarizing conversations happen everywhere, from private interactions to public discussions, from classrooms to workplaces, from offline to virtual spaces. These conversations do not always offer opportunities to discover a pre-existing piece of common ground. Sometimes, the people standing on the opposite sides of an issue may share so few things in common that seeking them out might be a lengthy task. Having a recipe for creating common ground in these conversations where it cannot readily be discovered would be a boon for those hoping to engender productive conversations that might lead to collective actions. Our study found that the small gesture of attempting to take someone else’s perspective can produce powerful effects helping to bridge those with polarized opinions. Thus, the two approaches tested in this study – logic-affirming SPT and intention-affirming SPT – will be useful for advocacy groups that aim to build common ground among stakeholders. When hosting conversations among these stakeholders, facilitators can encourage opposing parties to try to demonstrate SPT attempts as a means of improving the relational climate and quality of discussions. Classroom teachers at K-12 schools and faculty members in higher education institutions who are trying to promote civic dialogues among students may also embed the two approaches in their classroom activities. Authors in science communications may find the two approaches beneficial in their writing to bridge the distance with their potential audience when writing about the scientific facts that might be contentious. Limitations While the study offers important insights, a few limitations highlight areas where future research is needed. First, conceptually, there has yet to be consensus on what might qualify as a sense of common ground: Does it connotate a particular level of similarity in personality or attitudes, or an alignment of behavioral tendencies? In our study, we used a perceived similarity scale as a proxy of the former, and perceived fairness and personal SPT effort to assess the latter. Future studies could explore the construct of a sense of common ground, examining its definition and measurement, particularly in the context of disagreement. Second, the intercorrelations of our outcome variables – specifically perceived SPT, perceived similarity, perceived fairness, and anticipated relationship – were substantial ( r = .71 to .85). Although these constructs are theoretically distinct and have reasonable internal consistency (Cronbach’s ɑ = .86 to .91), a broader range of distinct outcomes might benefit future studies. Third, the perceived SPT scale did not distinguish the perceived SPT accuracy items from the items that emphasized perceived SPT effort . It is possible for a person to perceive that the other is putting in effort to take their perspective, while simultaneously judging that the other is inaccurately understanding their mind. Perceived SPT accuracy might also serve as a mediator between SPT effort and relational outcomes. Perhaps a perceiver who demonstrates SPT effort only improves relationships with the participant when the perceiver correctly guesses the participant’s mind. Therefore, future research could benefit from developing two separate sub-scales – perceived SPT effort, and perceived SPT accuracy – to examine the distinct and interaction effects of SPT effort and accuracy. Finally, the experimental context of this study likely generalizes to only a subset of disagreements that opposing parties might have. Unlike the original town hall setting that inspired this study, our participants read a social media post made by an expert teacher. Participants’ interactions with the teacher were text-based, impersonal, and unidirectional, potentially making the stakes for taking the perspective of the teacher fairly low. As Gehlbach and Mu ( 2023 ) posit, whether someone will be sufficiently motivated to take another person’s perspective depends on various factors. Factors such as low-stakes situations would likely inhibit SPT motivation. Though these study characteristics frequently exist in social media contexts, future research that increases the interpersonal consequences of the interaction and/or varies the nature of the interactions (e.g., making interactions synchronous and in-person) would be especially illuminating. Conclusion In conclusion, by simulating an online interaction between people holding opposite opinions on a polarizing climate change-related educational policy issue, our study found that both affirming the logical reasoning aspect and the intention aspect of the other side’s perspective are effective in improving people’s attitudes towards the perspective-taker, with the former tactic being more effective. In the present historical moment when society faces an acute threat from intense political polarization, our findings offer a pathway to create common ground among people from opposing sides. With intentional acts of perspective-taking, difficult conversations can evolve into opportunities for relationship building even in the absence of agreement or prior common ground. Declarations Competing Interests The authors declare no competing interests. Author Contributions All authors jointly conceived and designed the study. Author 1 analyzed data and wrote the manuscript. Authors 2 and 3 contributed equally by giving technical support, discussing the results and implications, and revising the manuscript. Author 4 provided technical guidance on the statistical analyses and commented on previous versions of the manuscript. Author 5 commented on previous versions of the manuscript. Author 6 supervised the project and revised the manuscript substantially. Data and Code Availability The dataset generated and analyzed during the current study, and the Stata codes used to clean and analyze data, are available from the corresponding author upon request during the review process. Data and code will be posted on Open Science Framework upon publication. References Allison, P. (2012, September 10). When can you safely ignore multicollinearity? Statistical Horizons Blog . https://statisticalhorizons.com/multicollinearity/ Butera, F., Sommet, N., & Darnon, C. (2019). Sociocognitive conflict regulation: How to make sense of diverging ideas. 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A Heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica , 48 (4), 817. https://doi.org/10.2307/1912934 Wooldridge, J. M. (2020). Introductory econometrics: A modern approach (7th ed.). Cengage Learning. ISBN 9780357693223 Xu, M., & Petty, R. E. (2022). Two-sided messages promote openness for morally based attitudes. Personality and Social Psychology Bulletin , 48 (8), 1151–1166. https://doi.org/10.1177/0146167220988371 Footnotes The authors distinguish the affirmation conditions in this study from the self-affirmation concept (e.g. Steele, 1988 ). The logic- or intention-affirming SPT in this study refers to the attempts to acknowledge the merits in the other people’s points of views. Additional Declarations There is NO Competing Interest. Supplementary Files Socialperspectivetakingasacatalystsupplementarymaterials.docx Social perspective-taking as a catalyst_supplementary materials Cite Share Download PDF Status: Under Review Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8234296","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":555169050,"identity":"d3003050-5bae-443f-807d-0064f1a1f869","order_by":0,"name":"Li 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15:33:08","extension":"xml","order_by":29,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":187371,"visible":true,"origin":"","legend":"","description":"","filename":"COMMSPSYCHOL2508690structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8234296/v1/f546c125424d1f28c28c114a.xml"},{"id":97719891,"identity":"0eb2e024-ee01-4ebb-b489-d1a5f08d8256","added_by":"auto","created_at":"2025-12-08 15:33:09","extension":"html","order_by":30,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":206639,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8234296/v1/715272938c6a2d4faf103a72.html"},{"id":97894228,"identity":"bd0a5c46-da08-44c5-9d07-5837ece71a40","added_by":"auto","created_at":"2025-12-10 15:32:04","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":61797,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eTreatment Effects on Perceived SPT\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote\u003c/em\u003e. Error bars represent 95% Confidence Intervals\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8234296/v1/9e4a65c872d741682f28fac5.png"},{"id":97895736,"identity":"a43750f9-9ef6-4e2b-ae4b-57fa3f2c3053","added_by":"auto","created_at":"2025-12-10 15:34:50","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":64456,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eTreatment Effects on Perceived Similarity\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote\u003c/em\u003e. Error bars represent 95% Confidence Intervals.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8234296/v1/0ed3153a24a2e2f9a97e3ede.png"},{"id":97719862,"identity":"a92340bc-cc22-4060-9520-e60587cd7e92","added_by":"auto","created_at":"2025-12-08 15:33:08","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":60636,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eTreatment Effects on Perceived Fairness\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote\u003c/em\u003e. Error bars represent 95% Confidence Intervals.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8234296/v1/a48a84f7e95c80bd773aff84.png"},{"id":97719865,"identity":"2c7b5f77-7710-4543-8ef9-ded983445f35","added_by":"auto","created_at":"2025-12-08 15:33:08","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":65646,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eTreatment \u0026nbsp;\u0026nbsp;Effects on Anticipated Relationship\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote\u003c/em\u003e. \u0026nbsp;\u0026nbsp;Error bars represent 95% Confidence Intervals.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8234296/v1/42cfbe619cdd993e472d28ab.png"},{"id":97719868,"identity":"c1eff592-bb69-4415-b51b-fd3c3deba85f","added_by":"auto","created_at":"2025-12-08 15:33:08","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":64147,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eTreatment Effects on Personal SPT Effort\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote\u003c/em\u003e. Error bars represent 95% Confidence Intervals\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8234296/v1/860103e07d21937d5bd66b3f.png"},{"id":97719872,"identity":"0722ca72-0bc0-400d-bf4a-f3925532e938","added_by":"auto","created_at":"2025-12-08 15:33:08","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":71980,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eTreatment Effects on Hypothesized Opinion Change\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote\u003c/em\u003e. Error bars represent 95% Confidence Intervals.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8234296/v1/f63f7ac1709ee0fa163b93c4.png"},{"id":98622014,"identity":"59e8dfda-2e5c-4add-a7e3-eeaa0433e5d9","added_by":"auto","created_at":"2025-12-19 16:41:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1447310,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8234296/v1/7b4e3947-360e-49c9-89f6-19e9df5782ce.pdf"},{"id":97719864,"identity":"d212da0a-b182-4e27-b6ee-c3ad5b45f943","added_by":"auto","created_at":"2025-12-08 15:33:08","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1771284,"visible":true,"origin":"","legend":"Social perspective-taking as a catalyst_supplementary materials","description":"","filename":"Socialperspectivetakingasacatalystsupplementarymaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-8234296/v1/24017605bdd5eb6f8bd3c5cb.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Social perspective-taking as a catalyst to building common ground between opposing parties","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn the current politically polarized times, engaging in the constructive conversations that democracy requires has become acutely challenging. Dozens of issues\u0026mdash;immigration, affordable health care, climate crisis, etc.\u0026mdash; require opposing parties to come together to reach collective solutions. However, productive conversations to reach these collective solutions seem especially rare (Falkenberg et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). To address these divides, numerous advocacy groups have emerged to attempt establishing common ground between parties with opposing points of view\u0026mdash;Search for Common Ground, Braver Angels, Common Ground USA, More in Common, and many others. These organizations\u0026rsquo; implicit logic is that identifying shared experiences or traits\u0026mdash;the recent loss of a loved one, a fear of flying, valuing family over work, etc.\u0026mdash;may facilitate conversation on a topic where they disagree because they have already established a common bond\u0026mdash;even if that connection lies in an entirely different domain.\u003c/p\u003e\u003cp\u003eYet, identifying common ground may be impractical in many situations. Opposing parties may be brought to a negotiating table before they can establish a prior relationship. Searches to identify meaningful similarities may end unsuccessfully. Situational factors may preclude the opportunity to seek common ground\u0026mdash;for example, a town hall setting with multiple factions present might prevent key actors from being vulnerable enough to reveal meaningful similarities. These challenges raise the question: Can common ground be actively constructed during the conversation rather than identified through pre-existing similarities?\u003c/p\u003e\n\u003ch3\u003eSocial Perspective-Taking and Its Relational Benefits\u003c/h3\u003e\n\u003cp\u003eIn cases where identifying shared experiences or traits elides the opposing parties, they may still establish some semblance of common ground by finding merit in some portion of the other party\u0026rsquo;s perspective. The last author drew the inspiration for this study from a conversation with colleagues from the Environmental Defense Fund. The colleagues led town hall meetings in the heart of coal-mining regions in the United States. Their goal was to convince local residents\u0026mdash;many of whom were coal miners\u0026mdash;to embrace cleaner sources of energy. Astonished that such a meeting did not result in the colleagues being chased out of town, he asked what their strategy was. They responded that the first 20 minutes of their presentation described the amazing historical benefits of coal\u0026mdash;revolutionizing transportation, providing home heating for millions, eradicating hunger, etc. In other words, without meeting the town hall participants ahead of time, they put effort into finding merit in the other party\u0026rsquo;s perspective by anticipating and affirming arguments that the residents were likely to embrace. Notably, the mere act of trying hard to take the coal miners\u0026rsquo; perspectives did not ensure accurate inferences about their point of view. Nevertheless, the efforts to affirm the other side appeared to have been appreciated.\u003c/p\u003e\u003cp\u003eAccording to Gehlbach and Mu (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), social perspective-taking (SPT), the process through which a perceiver figures out others\u0026rsquo; thoughts, feelings, and motivations, encompasses an ability component (some individuals may be better at \u0026ldquo;reading\u0026rdquo; people than others) and a motivational component (some individuals may try harder or more frequently to understand where others are coming from). Because this latter component of SPT is under individuals\u0026rsquo; control, it may be a promising mechanism through which common ground might be established with the hope that relational benefits ensue. A modest but growing set of studies supports this idea. For example, Goldstein et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) found that participants who felt their perspectives were taken by a stranger showed greater liking, engaged in more helping behaviors, and perceived greater similarity with the perspective-taker. In a virtual reality simulation, Gehlbach et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) found similarly strong connections between participants\u0026rsquo; perceptions that someone else was trying hard to take their perspective and their relationship with that person. If the relational benefits of effortful SPT persist in important contexts where parties hold opposing positions but need to reach an agreement or compromise, then it would offer a pathway towards enabling these pivotal conversations.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eDifferent Approaches to Demonstrating SPT Attempts\u003c/h2\u003e\u003cp\u003eAlthough these studies recognize SPT as a promising mechanism for creating common ground, they provide little insight into exactly how that attempt might be most wisely deployed. When anticipating the specifics of another party\u0026rsquo;s mental state, perceivers may need to choose the domain they want to discern. Should a perceiver try to identify merits in the arguments the other side is likely to make? Or would the perceiver be better off trying to affirm the underlying intentions of the other party? Most prior studies show the promise of the former approach. For example, Stanley et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) found that when people learn more about their political opponents\u0026rsquo; arguments, their impressions of the opponents improved. Xu and Petty (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) also found that, after delivering a counter-attitudinal message, adding a message that respectfully acknowledges the other side\u0026rsquo;s arguments fosters increased openness to opposing ideas. Additionally, thanks to people\u0026rsquo;s innate motivation to show up as a good person (Steele, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1988\u003c/span\u003e; Sherman \u0026amp; Cohen, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), it is reasonable to infer that acknowledging others\u0026rsquo; intentions could have benefits as well.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eThe Current Study\u003c/h3\u003e\n\u003cp\u003eThe current study compares whether demonstrating SPT by affirming the logic of an opposing party\u0026rsquo;s argument or their underlying intentions was more effective in creating a sense of common ground and promoting relationship-related outcomes. We examine these approaches\u0026ndash;logic-affirming SPT and intention-affirming SPT\u003ca class=\"FNLink\" href=\"#Fn1\" id=\"#FNLinkFn1\"\u003e\u003c/a\u003e\u0026ndash;to establishing common ground in the context of simulated opinion exchanges about a climate change education policy between participants and an \u0026ldquo;expert\u0026rdquo; who holds the opposite point of view. We first hypothesized that:\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eH1: Participants being exposed to either SPT condition would perceive that the expert did a better job of taking their perspective.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTo evaluate differences in perceptions of common ground, we assessed participants\u0026rsquo; perceived similarity with the expert (Montoya et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) and perceived fairness. We hypothesized that:\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eH2: Participants being exposed to either SPT condition would perceive greater similarity with the expert,\u003c/p\u003e\u003cp\u003eH3: and that they would perceive the expert and the information presented as fairer.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eGiven the robust association between perceived similarity and liking (Montoya et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Tidwell et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Sprecher, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), we further hypothesized that:\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eH4\u003c/strong\u003e\u003cp\u003eParticipants being exposed to either SPT condition would anticipate a more positive relationship with the expert.\u003c/p\u003e\u003c/p\u003e\u003cp\u003eWe also assumed that participants who perceived that the expert was actively taking their perspective might reciprocate (Gouldner, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1960\u003c/span\u003e). Therefore, we hypothesized that:\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eH5: Participants in either SPT condition would report putting forth more effort into taking the expert\u0026rsquo;s perspective, and\u003c/p\u003e\u003cp\u003eH6: they would exhibit greater opinion change in the direction that the expert advocated.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eBecause people tend to favor information that confirms their pre-existing beliefs (Nickerson, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1998\u003c/span\u003e) and supports positions that align with their political affiliations (Van Boven et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), we controlled for the strength of participants\u0026rsquo; pre-existing beliefs and political orientation.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eIn line with open science recommendations (Gehlbach \u0026amp; Robinson, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), our study\u0026rsquo;s hypotheses were preregistered on the Open Science Framework (\u0026lt;\u0026thinsp;preregistration link blinded for review\u0026gt;). The procedures, measures, and intervention materials are also available via the same link; data and Stata code will be posted after publication. The study was approved by the university's institutional review board before data collection began.\u003c/p\u003e\n\u003ch3\u003eParticipants\u003c/h3\u003e\n\u003cp\u003eWe recruited an initial sample of 579 adult participants currently residing in the United States from \u003cem\u003eProlific\u003c/em\u003e\u0026rsquo;s survey platform. In line with our preregistration, we excluded participants who engaged in \u0026ldquo;straight-line\u0026rdquo; responding (Vriesema \u0026amp; Gehlbach, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), that is, who responded to 12 consecutive questions with identical responses (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;21). In the final sample (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;558), about 60.0% of the participants identified as female and 37.5% as male. The majority of participants self-identified as White/Caucasian (61.6%), followed by Black or African American (12.0%) and Asian/Pacific Islander (11.5%). Participants\u0026rsquo; average age was around 39. An average participant tended to have liberal views (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.21 on a scale of 1 = \u0026ldquo;very liberal\u0026rdquo; to 7 = \u0026ldquo;very conservative\u0026rdquo; with 4 = \u0026ldquo;moderate\u0026rdquo;). About 68.6% of the participants reported having learned about environmental science in a formal school setting. The median education level of participants was holding a four-year college degree. The average socioeconomic status of the participants was 4.67 on a self-reported scale of one (highest) to nine (lowest). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the descriptive statistics of participants\u0026rsquo; demographic characteristics.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cem\u003eCharacteristics of Participants by Treatment Groups (N\u0026thinsp;=\u0026thinsp;558)\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eControl\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLogic-Affirming SPT Treatment\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIntention-Affirming SPT Treatment\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eFull Sample\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003en (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003en (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003en (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003en (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eN\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e185 (33.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e189 (33.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e184 (33.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e558 (100.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGender\u003c/b\u003e (n\u0026thinsp;=\u0026thinsp;558)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e68 (36.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e66 (34.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e75 (40.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e209 (37.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e112 (60.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e118 (62.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e105 (57.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e335 (60.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNonbinary, other, or prefer not to answer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5 (2.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 (2.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4 (2.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e14 (2.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRace\u003c/b\u003e (n\u0026thinsp;=\u0026thinsp;557)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAmerican Indian or Alaskan native\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (0.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (0.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (0.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3 (0.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAsian/Pacific Islander\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18 (9.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24 (12.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22 (12.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e64 (11.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlack or African American\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19 (10.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22 (11.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26 (14.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e67 (12.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLatino/a or Hispanic American\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6 (3.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15 (7.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3 (1.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e24 (4.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMiddle Eastern\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (0.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1 (0.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWhite/Caucasian\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e119 (64.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e109 (57.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e115 (62.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e343 (61.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMixed Races or other\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21 (11.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18 (9.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16 (8.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e55 (9.9%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003emean (sd)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003emean (sd)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003emean (sd)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003emean (sd)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEnvironmental Education\u003c/b\u003e\u003c/p\u003e\u003cp\u003e(yes\u0026thinsp;=\u0026thinsp;1, no\u0026thinsp;=\u0026thinsp;0; n\u0026thinsp;=\u0026thinsp;558)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.74 (0.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.67 (0.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.66 (0.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.69 (0.46)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge\u003c/b\u003e (n\u0026thinsp;=\u0026thinsp;551)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e37.88(12.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e38.31(12.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e40.81(13.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e38.99(12.98)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePolitical Orientation\u003c/b\u003e (n\u0026thinsp;=\u0026thinsp;557)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.11 (1.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.20 (1.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.31 (1.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.21 (1.76)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEducational Level\u003c/b\u003e (n\u0026thinsp;=\u0026thinsp;558)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15.00 (2.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14.98 (1.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15.27 (1.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15.08 (1.95)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSocioeconomic Status\u003c/b\u003e (n\u0026thinsp;=\u0026thinsp;551)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.56 (1.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.65 (1.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.80 (1.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.67 (1.67)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003eProcedure\u003c/h3\u003e\n\u003cp\u003eThis study utilized a 1 x 3, between-subjects randomized controlled design. Participants completed the study through a Qualtrics survey. Participants first read a brief introduction to New Jersey\u0026rsquo;s climate change education mandate (New Jersey Department of Education, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). They then indicated how strongly they supported or opposed a potential \u003cem\u003enational\u003c/em\u003e mandate to require the teaching of climate change education in all K-12 public schools. After stating their initial opinion, participants in each group (supporting or opposing) were randomly assigned to one of the two treatment groups or the control group through Qualtrics. All participants read a mock Facebook post from the same expert teacher, \u0026ldquo;Alex,\u0026rdquo; who presented arguments countering their initial opinions (i.e., those who initially opposed the mandate received pro-national mandate arguments for climate change education; those who initially favored the mandate read anti-national mandate arguments).\u003c/p\u003e\u003cp\u003eParticipants in the logic-affirming SPT group first had the validity of their arguments affirmed before seeing the counter-attitudinal arguments. For example, initial supporters in the logic treatment group first read that \u0026ldquo;Learning about the impact of climate change is directly relevant to students\u0026rsquo; future.\u0026rdquo; Those in the intention-affirming SPT group were first shown text that assumed and affirmed the good intentions behind their initial opinion. For both initial supporters and opposers, their text included intentions such as \u0026ldquo;You probably want what\u0026rsquo;s best for the children in our communities.\u0026rdquo; Participants in the control condition saw only counter-attitudinal arguments. Following the intervention, participants were asked to re-evaluate their opinion on the issue, complete outcome measures, and provide demographic information. The overall flow of the procedure and the Facebook posts can be found in the Supplementary Materials (Figures A.1-A.4; attached at the end of this document to facilitate the review process).\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eMeasures\u003c/h2\u003e\u003cp\u003e Our primary outcome measures of interest consisted of scales assessing participants\u0026rsquo; perceptions of the speaker\u0026rsquo;s social perspective-taking, participants\u0026rsquo; evaluations of the speaker\u0026rsquo;s similarity and fairness, and potential for a positive relationship; participants\u0026rsquo; self-evaluation of their own SPT effort; and a single measure of the magnitude of opinion change towards the other side of the issue. Scores for composite scales were computed by taking the average of all items on each scale. Kurtosis and the skewness of covariates and outcome variables fell within conventional thresholds (+/- 2.0 for skewness and +/- 7.0 for kurtosis; West et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). We present descriptive statistics and correlations of variables in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. A complete list of individual items for each composite scale can be found in the preregistered codebook (and Table \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003eA.1\u003c/span\u003e of our Supplementary Materials\u0026mdash;appended at the end of this manuscript to facilitate the review process).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cem\u003ePsychometric Properties and Pairwise Correlations for Covariates and Outcome variables (N\u0026thinsp;=\u0026thinsp;558)\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"17\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMin\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMax\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSkewness\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eKurtosis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eα\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c13\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c14\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c15\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c16\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c17\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1. Political Orientation\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e557\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2. Initial Opinion\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e558\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-1.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.57*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3. Perceived SPT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e558\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.14*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.17*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4. Perceived Similarity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e558\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.08\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.12*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.78*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5. Perceived Fairness\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e558\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.12*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.16*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.85*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.71*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6. Anticipated Relationship\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e558\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.10*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.76*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.76*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e0.71*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7. Personal SPT Effort\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e558\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e3.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.12*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.25*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.19*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e0.22*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e0.26*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8. Opinion Change\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e558\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e6.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.13*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.36*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.32*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e0.35*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e0.34*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e0.14*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c17\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"17\"\u003e\u003cem\u003e* p\u0026thinsp;\u0026lt;\u0026thinsp;.05\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003ePerceived SPT\u003c/em\u003e (ɑ = .86): Adapted from Gehlbach et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), this four-item, five-point response scale assessed the extent to which participants perceived the speaker to take their perspective. Participants rated how much effort, motivation, clarity, and accuracy they thought the speaker had to understand their perspectives. One sample item is \u0026ldquo;In writing the post, how much effort do you think the teacher put into taking the perspective of people like you on this issue?\u0026rdquo;\u003c/p\u003e\u003cp\u003e\u003cem\u003ePerceived similarity\u003c/em\u003e (ɑ = .89): This four-item, five-point response scale assessed how similar participants perceived the speaker to be to them, in terms of overall similarity, similarity in personality, opinions, and attitudes on the same issues. Adapted from Gehlbach et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), this scale includes items such as \u0026ldquo;Overall, how much would you guess that you have in common with the teacher?\u0026rdquo;\u003c/p\u003e\u003cp\u003e\u003cem\u003ePerceived fairness\u003c/em\u003e (ɑ = .91): This four-item, five-point response scale measured the extent to which participants perceived the speaker and the information they gave to be fair. We also asked how similar the quality of information was on both sides of the issue and how balanced the information seemed to be. An example item is \u0026ldquo;Overall, how fair was the teacher's treatment of each side of the issue?\u0026rdquo;\u003c/p\u003e\u003cp\u003e\u003cem\u003eAnticipated relationship\u003c/em\u003e (ɑ = .88): Adapted from the teacher-student relationship positivity scale used in Gehlbach et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), this four-item, five-point response scale measured how positive of a relationship they would imagine having with the expert. We asked participants to rate the extent to which they would enjoy a conversation, as well as how friendly, respectful, and caring they believed the expert would be, e.g., \u0026ldquo;How much do you think you would enjoy having a conversation with the teacher?\u0026rdquo;\u003c/p\u003e\u003cp\u003e\u003cem\u003ePersonal SPT effort\u003c/em\u003e (ɑ = .86): Adapted from Gehlbach et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), this five-item, five-point response scale measured participants\u0026rsquo; self-perception of how much effort they exerted in trying to understand the opposing viewpoints, as well as understanding the motivations, goals, feelings, and thoughts of people supporting the other side. The scale includes items such as \u0026ldquo;How hard did you try to understand the merits of the opposing points of view on this issue?\u0026rdquo;\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eOpinion change\u003c/strong\u003e\u003cp\u003eThe scores on this continuous variable measured the amount that participants\u0026rsquo; opinions shifted towards the opposing side, with negative values indicating that participants\u0026rsquo; initial opinions became more deeply entrenched. We first obtained participants\u0026rsquo; scores for \u003cem\u003einitial opinion\u003c/em\u003e and \u003cem\u003efinal opinion\u003c/em\u003e measures, which ranged from 1 = \u0026ldquo;strongly oppose\u0026rdquo; to 10 = \u0026ldquo;strongly support\u0026rdquo; based on how strongly they opposed or supported such a national mandate, with no neutral point. To construct the \u003cem\u003eopinion change\u003c/em\u003e measure, for initial opposers, we subtracted their \u003cem\u003einitial opinion\u003c/em\u003e from the \u003cem\u003efinal opinion\u003c/em\u003e; for initial supporters, we took the additive inverse of the final-minus-initial scores, so that positive values always indicated opinion change in the direction of the expert.\u003c/p\u003e\u003c/p\u003e\u003cp\u003eBeyond these focal outcome measures, we asked participants, \u0026ldquo;Generally speaking, how would you classify your political orientation?\u0026rdquo; The scores ranged from 1 = \u0026ldquo;very liberal\u0026rdquo; to 7 = \u0026ldquo;very conservative\u0026rdquo;, with 4 = \u0026ldquo;moderate\u0026rdquo;. We used the \u003cem\u003einitial opinion\u003c/em\u003e and \u003cem\u003epolitical orientation\u003c/em\u003e as covariates. Considering the moderately high correlation between these two covariates (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.57, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003cem\u003e)\u003c/em\u003e, we calculated the variance inflation factor (VIF; Allison, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) for each of the two variables and ruled out multicollinearity (VIF for both variables\u0026thinsp;=\u0026thinsp;1.47).\u003c/p\u003e\u003cp\u003eThe study also included three manipulation checks. Participants indicated on a slider scale the percentage of information from the speaker that favored the climate change education mandate (\u003cem\u003emanipulation1)\u003c/em\u003e, opposed the mandate (\u003cem\u003emanipulation2\u003c/em\u003e), or acknowledged the underlying intentions of participants (\u003cem\u003emanipulation3\u003c/em\u003e).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eAnalytic Approach\u003c/h3\u003e\n\u003cp\u003eData analyses were conducted using Stata/SE 18.0. First, to see whether our randomization had created balanced treatment and control groups, we used our demographic variables\u0026mdash;race/ethnicity, gender, age, educational level, environmental education experience, and socioeconomic status\u0026mdash;to fit a multinomial logistic regression model predicting assignment to each treatment condition and the control group. Next, we evaluated whether the interventions worked as intended for the two treatment groups by comparing mean differences in our manipulation checks. We then tested each of our six hypotheses outlined in the preregistration through fitting the following OLS regression model:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{Y}_{i}=\\:{\\beta\\:}_{0}+\\:{\\beta\\:}_{1}Conditio{n}_{1}+\\:{\\beta\\:}_{2}Conditio{n}_{2}+\\:\\mu\\:{X}_{i}+\\:{ϵ}_{i}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere \u003cem\u003eY\u003c/em\u003e is the measured outcome for participant \u003cem\u003ei\u003c/em\u003e (\u003cem\u003eperceived SPT\u003c/em\u003e, \u003cem\u003eperceived similarity\u003c/em\u003e, \u003cem\u003eperceived fairness\u003c/em\u003e, \u003cem\u003eanticipated relationship\u003c/em\u003e, \u003cem\u003epersonal SPT effort\u003c/em\u003e, and \u003cem\u003eopinion change\u003c/em\u003e); \u003cem\u003eCondition\u003c/em\u003e is an indicator for assignment to conditions (1\u0026thinsp;=\u0026thinsp;Logic; 2\u0026thinsp;=\u0026thinsp;Intention); \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{X}_{i}\\)\u003c/span\u003e\u003c/span\u003e is a vector of covariates that included \u003cem\u003epolitical orientation\u003c/em\u003e and \u003cem\u003einitial opinion\u003c/em\u003e; and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{ϵ}_{i}\\)\u003c/span\u003e\u003c/span\u003e is the error term. Following White (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1980\u003c/span\u003e) and Wooldridge (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), we calculated robust standard errors for all regressions using the robust function in Stata. Following Cumming\u0026rsquo;s (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) guidance, we used 95% confidence intervals and effect sizes, rather than \u003cem\u003ep\u003c/em\u003e values, to evaluate each hypothesis.\u003c/p\u003e\u003cp\u003eFinally, considering \u003cem\u003eopinion change\u003c/em\u003e is a function of initial and final opinions, we also fit the model to predict \u003cem\u003eopinion change\u003c/em\u003e without the \u003cem\u003einitial opinion\u003c/em\u003e covariate. This step was added after the preregistration as a sensitivity check to examine whether the preregistered model to examine \u003cem\u003eopinion change\u003c/em\u003e might be biased.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\"\u003e\n \u003ch2\u003ePreliminary Analyses\u003c/h2\u003e\n \u003cp\u003eOur multinomial logistic regression results (see Table\u0026nbsp;3) showed that the demographic variables of race/ethnicity, gender, age, educational level, environmental education experience, and socioeconomic status did not significantly predict assignment to different conditions. Therefore, in line with our preregistration, we did not include any of these variables as additional covariates in our model.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003e\u003cem\u003eMultinomial Logistic Regression Results (n\u0026thinsp;=\u0026thinsp;558)\u003c/em\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" style=\"width: 15.1977%;\"\u003e\n \u003cp\u003eTreatment Groups\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 7.0748%;\"\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 5.6773%;\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 3.319%;\"\u003e\n \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 6.2887%;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\" style=\"width: 14.499%;\"\u003e\n \u003cp\u003e[95% CI]\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"7\" style=\"width: 51.9692%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLogic-affirming SPT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 15.1977%;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.0748%;\"\u003e\n \u003cp\u003e.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.6773%;\"\u003e\n \u003cp\u003e.199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 3.319%;\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.2887%;\"\u003e\n \u003cp\u003e.903\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.7343%;\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.6773%;\"\u003e\n \u003cp\u003e.414\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 15.1977%;\"\u003e\n \u003cp\u003eRace/Ethnicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.0748%;\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.6773%;\"\u003e\n \u003cp\u003e.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 3.319%;\"\u003e\n \u003cp\u003e-1.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.2887%;\"\u003e\n \u003cp\u003e.116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.7343%;\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.6773%;\"\u003e\n \u003cp\u003e.026\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 15.1977%;\"\u003e\n \u003cp\u003eEnvironmental Education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.0748%;\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.329\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.6773%;\"\u003e\n \u003cp\u003e.245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 3.319%;\"\u003e\n \u003cp\u003e-1.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.2887%;\"\u003e\n \u003cp\u003e.180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.7343%;\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.808\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.6773%;\"\u003e\n \u003cp\u003e.151\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 15.1977%;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.0748%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.6773%;\"\u003e\n \u003cp\u003e.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 3.319%;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.2887%;\"\u003e\n \u003cp\u003e.961\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.7343%;\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.6773%;\"\u003e\n \u003cp\u003e.018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 15.1977%;\"\u003e\n \u003cp\u003eEducational Level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.0748%;\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.6773%;\"\u003e\n \u003cp\u003e.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 3.319%;\"\u003e\n \u003cp\u003e-0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.2887%;\"\u003e\n \u003cp\u003e.788\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.7343%;\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.6773%;\"\u003e\n \u003cp\u003e.099\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 15.1977%;\"\u003e\n \u003cp\u003eSocioeconomic status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.0748%;\"\u003e\n \u003cp\u003e.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.6773%;\"\u003e\n \u003cp\u003e.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 3.319%;\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.2887%;\"\u003e\n \u003cp\u003e.687\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.7343%;\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.6773%;\"\u003e\n \u003cp\u003e.168\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 15.1977%;\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.0748%;\"\u003e\n \u003cp\u003e.876\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.6773%;\"\u003e\n \u003cp\u003e.956\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 3.319%;\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.2887%;\"\u003e\n \u003cp\u003e.360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.7343%;\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.6773%;\"\u003e\n \u003cp\u003e2.749\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"7\" style=\"width: 51.9692%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIntention-affirming SPT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 15.1977%;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.0748%;\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.6773%;\"\u003e\n \u003cp\u003e.202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 3.319%;\"\u003e\n \u003cp\u003e-0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.2887%;\"\u003e\n \u003cp\u003e.321\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.7343%;\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.597\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.6773%;\"\u003e\n \u003cp\u003e.196\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 15.1977%;\"\u003e\n \u003cp\u003eRace/Ethnicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.0748%;\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.6773%;\"\u003e\n \u003cp\u003e.069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 3.319%;\"\u003e\n \u003cp\u003e-1.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.2887%;\"\u003e\n \u003cp\u003e.213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.7343%;\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.6773%;\"\u003e\n \u003cp\u003e.050\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 15.1977%;\"\u003e\n \u003cp\u003eEnvironmental Education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.0748%;\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.352\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.6773%;\"\u003e\n \u003cp\u003e.246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 3.319%;\"\u003e\n \u003cp\u003e-1.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.2887%;\"\u003e\n \u003cp\u003e.152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.7343%;\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.833\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.6773%;\"\u003e\n \u003cp\u003e.129\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 15.1977%;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.0748%;\"\u003e\n \u003cp\u003e.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.6773%;\"\u003e\n \u003cp\u003e.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 3.319%;\"\u003e\n \u003cp\u003e1.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.2887%;\"\u003e\n \u003cp\u003e.116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.7343%;\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.6773%;\"\u003e\n \u003cp\u003e.031\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 15.1977%;\"\u003e\n \u003cp\u003eEducational Level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.0748%;\"\u003e\n \u003cp\u003e.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.6773%;\"\u003e\n \u003cp\u003e.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 3.319%;\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.2887%;\"\u003e\n \u003cp\u003e.610\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.7343%;\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.6773%;\"\u003e\n \u003cp\u003e.153\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 15.1977%;\"\u003e\n \u003cp\u003eSocioeconomic status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.0748%;\"\u003e\n \u003cp\u003e.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.6773%;\"\u003e\n \u003cp\u003e.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 3.319%;\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.2887%;\"\u003e\n \u003cp\u003e.410\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.7343%;\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.6773%;\"\u003e\n \u003cp\u003e.199\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 15.1977%;\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.0748%;\"\u003e\n \u003cp\u003e\u0026minus;\u0026thinsp;.482\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.6773%;\"\u003e\n \u003cp\u003e.963\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 3.319%;\"\u003e\n \u003cp\u003e-0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 6.2887%;\"\u003e\n \u003cp\u003e.616\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 8.7343%;\"\u003e\n \u003cp\u003e-2.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.6773%;\"\u003e\n \u003cp\u003e1.405\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 22.1852%;\"\u003e\n \u003cp\u003eMean dependent variables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.6773%;\"\u003e\n \u003cp\u003e0.998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 3.319%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 15.023%;\"\u003e\n \u003cp\u003eSD dependent variables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.6773%;\"\u003e\n \u003cp\u003e0.814\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 15.1977%;\"\u003e\n \u003cp\u003ePseudo r-squared\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.0748%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.6773%;\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 3.319%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 15.023%;\"\u003e\n \u003cp\u003eNumber of observations \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.6773%;\"\u003e\n \u003cp\u003e543\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 15.1977%;\"\u003e\n \u003cp\u003eChi-square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.0748%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.6773%;\"\u003e\n \u003cp\u003e14.222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 3.319%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 15.023%;\"\u003e\n \u003cp\u003eProb\u0026thinsp;\u0026gt;\u0026thinsp;chi2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.6773%;\"\u003e\n \u003cp\u003e0.287\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 15.1977%;\"\u003e\n \u003cp\u003eAkaike crit. (AIC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 7.0748%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.6773%;\"\u003e\n \u003cp\u003e1206.799\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 3.319%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 15.023%;\"\u003e\n \u003cp\u003eBayesian crit. (BIC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.6773%;\"\u003e\n \u003cp\u003e1266.958\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e\u003cem\u003eNote. *** p\u0026thinsp;\u0026lt;\u0026thinsp;.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;.1.\u003c/em\u003e The baseline group is the control group. SE\u0026thinsp;=\u0026thinsp;standard error\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eIn addition, \u003cem\u003et\u003c/em\u003e-tests of the manipulation check items (see Table A.2 in the Supplementary Materials) showed that participants in the logic-affirming SPT groups perceived the post they read to contain significantly higher percentage of information that was on their side, both for initial supporters (\u003cem\u003eM\u003c/em\u003e\u003csub\u003elogic\u003c/sub\u003e = 26.99, \u003cem\u003eM\u003c/em\u003e\u003csub\u003econtrol\u003c/sub\u003e = 8.40, \u003cem\u003et\u003c/em\u003e (282) = -9.26, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, 95% CI [-22.54, -14.64]) and initial opposers ( \u003cem\u003eM\u003c/em\u003e\u003csub\u003elogic\u003c/sub\u003e = 30.40, \u003cem\u003eM\u003c/em\u003e\u003csub\u003econtrol\u003c/sub\u003e = 3.84, \u003cem\u003et\u003c/em\u003e (88) = -6.24, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, 95% CI: [-35.03, -18.10]). Those in the intention-affirming SPT group perceived the message to contain more information that acknowledged the positive intentions behind their point of view than the control group, both for initial supporters (\u003cem\u003eM\u003c/em\u003e\u003csub\u003eintention\u003c/sub\u003e = 41.58, \u003cem\u003eM\u003c/em\u003e\u003csub\u003econtrol\u003c/sub\u003e = 26, \u003cem\u003et\u003c/em\u003e (279) = -5.28, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, 95% CI [-21.39, -9.77]) and initial opposers ( \u003cem\u003eM\u003c/em\u003e\u003csub\u003eintention\u003c/sub\u003e = 35.80, \u003cem\u003eM\u003c/em\u003e\u003csub\u003econtrol\u003c/sub\u003e = 17.12, \u003cem\u003et\u003c/em\u003e (86) = -3.11, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0025, 95% CI: [-30.62, -6.75]). Therefore, we concluded that manipulations of both interventions were functioning as intended.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\"\u003e\n \u003ch2\u003ePre-specified Hypotheses\u003c/h2\u003e\n \u003cp\u003eNext, to test our pre-specified hypotheses, we fit regression models predicting the effect of treatment on the outcomes while controlling for initial opinion and political orientation. Table 4 presents the unstandardized regression output and the effect sizes of the treatments (see Table A.3 in supplementary materials for more detailed regression output of the models). Figures 1\u0026ndash;6 present the mean score comparisons of treatment effects on focal outcomes.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab7\" border=\"1\" class=\"fr-table-selection-hover\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 4\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003e\u003cem\u003eUnstandardized Regression Output for Preregistered Hypothesis Testing (N\u0026thinsp;=\u0026thinsp;557)\u003c/em\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOutcome Variables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTreatment Condition\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eb\u003c/em\u003e (robust SE)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eadj. \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePerceived SPT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLogic-affirming SPT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99 (0.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.82, 1.17]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntention-affirming SPT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.40 (0.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.22, 0.58]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePerceived similarity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLogic-affirming SPT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.55 (0.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.39, 0.72]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntention-affirming SPT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.27 (0.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.10, 0.44]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePerceived fairness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLogic-affirming SPT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.27 (0.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[1.09, 1.45]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntention-affirming SPT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.51 (0.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.32, 0.70]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eAnticipated relationship\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLogic-affirming SPT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.57 (0.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.40, 0.74]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntention-affirming SPT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.30 (0.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.13, 0.47]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePersonal SPT effort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLogic-affirming SPT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.20 (0.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.03, 0.36]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntention-affirming SPT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.10 (0.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[-0.06, 0.26]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eOpinion change\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLogic-affirming SPT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.23 (0.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[-0.12, 0.58]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntention-affirming SPT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.09 (0.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.602\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[-0.25, 0.44]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eOpinion change\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLogic-affirming SPT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.23 (0.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[-0.12, 0.59]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntention-affirming SPT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.10 (0.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.564\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[-0.25, 0.45]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003e\u003cem\u003eNote.\u003c/em\u003e SE\u0026thinsp;=\u0026thinsp;standard error.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003e\u003csup\u003ea\u003c/sup\u003eRegression output for the preregistered model with \u003cem\u003einitial opinion\u003c/em\u003e covariate.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003e\u003csup\u003eb\u003c/sup\u003eRegression output for the adjusted model without \u003cem\u003einitial opinion\u003c/em\u003e covariate.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eAs predicted, results showed that participants in both the logic-affirming and intention-affirming SPT groups perceived that the expert did a better job taking their perspectives (\u003cem\u003eb\u003c/em\u003e\u003csub\u003elogic\u003c/sub\u003e = 0.99, 95% CI = [0.82,1.17]; \u003cem\u003eb\u003c/em\u003e\u003csub\u003eintention\u003c/sub\u003e = 0.40, 95% CI = [0.22, 0.58], adjusted \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.20). Participants in both treatment groups also perceived greater similarity with the speaker (\u003cem\u003eb\u003c/em\u003e\u003csub\u003elogic\u003c/sub\u003e = 0.55, 95% CI = [0.39, 0.72]; \u003cem\u003eb\u003c/em\u003e\u003csub\u003eintention\u003c/sub\u003e = 0.27, 95% CI = [0.10, 0.44], adjusted \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.08). They perceived the expert and the information presented to be fairer (\u003cem\u003eb\u003c/em\u003e\u003csub\u003elogic\u003c/sub\u003e = 1.27, 95% CI = [1.09, 1.45]; \u003cem\u003eb\u003c/em\u003e\u003csub\u003eintention\u003c/sub\u003e = 0.51, 95% CI = [0.32, 0.70]; adjusted \\(\\:{R}^{2}\\) = 0.26), and they anticipated a more positive relationship with the expert (\u003cem\u003eb\u003c/em\u003e\u003csub\u003elogic\u003c/sub\u003e = 0.57, 95% CI = [0.40, 0.74]; \u003cem\u003eb\u003c/em\u003e\u003csub\u003eintention\u003c/sub\u003e = 0.30, 95% CI = [0.13, 0.47], adjusted \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e = 0.08). As shown in Table 4, the logic-affirming SPT treatment produced effects that were approximately twice those of the intention-affirming SPT treatment on the outcomes.\u003c/p\u003e\n \u003cp\u003eFurthermore, we tested the hypothesis of whether participants in both treatments reciprocated the expert\u0026rsquo;s SPT efforts by putting forth more personal SPT effort toward the expert. The logic treatment condition was effective (\u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.20, 95% CI = [0.03, 0.36], adjusted \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.03), while the effect of intention treatment was small, positive, but not significant (\u003cem\u003eb\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.10, 95% CI = [-0.06,0.26], adjusted \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.03).\u003c/p\u003e\n \u003cp\u003eFinally, we tested whether treatment participants might exhibit a greater shift in their attitudes towards the position they initially opposed. The analysis of both the preregistered model with the \u003cem\u003einitial opinion\u003c/em\u003e covariate and the additional model without the covariate produced similarly small differences between our treatment groups and the control participants, with confidence intervals that included zero.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eResults showed that both approaches to SPT\u0026mdash;affirming the assumed logic of the other\u0026rsquo;s position or validating their underlying intentions\u0026mdash;were effective. Not only did treatment participants perceive that the expert took their perspective to a greater degree, but the interventions also allowed for a sense of common ground to be created. In essence, treatment participants perceived the expert as more similar to themselves, both in personality and in attitudes. In response, these participants recognized the merits in the arguments the expert made as well, perceiving the speaker to have fairly presented and understood the issue. In addition to building a sense of common ground, the interventions led to additional relationship-related benefits\u0026mdash; e.g., anticipating a more positive future relationship with the expert. The logic-affirming SPT intervention further encouraged participants to put forth more effort to take the speakers\u0026rsquo; perspectives. In sum, these results suggest participants\u0026rsquo; improved perceptions of the speaker. For most outcomes, effect sizes indicated that the logic-affirming SPT treatment had twice the benefit of the intention-affirming SPT treatment.\u003c/p\u003e\u003cp\u003eThe results highlight two findings that are of theoretical importance to the SPT literature. First, prior evidence suggests that SPT can foster a better relational climate with better liking, more perceived similarity, and pro-social behaviors (e.g., Goldstein et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Gehlbach et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Our study\u0026rsquo;s results reinforce and extend this prior literature. Specifically, it seems that creating a sense of common ground, where both parties agree on certain merits of one party\u0026rsquo;s point of view, is also possible via SPT. Notably, the results suggest that the benefits of these SPT attempts may generalize to a social context where people might most need to establish common ground and positive relationships\u0026mdash;in a disagreement over controversial and divisive topics.\u003c/p\u003e\u003cp\u003eSecond, the study adds a key nuance to the SPT literature by focusing on the kinds of SPT attempts that might matter more for relationship-related outcomes. We found SPT attempts to be more effective when they emphasized the merits of the other side\u0026rsquo;s logic, rather than their positive intentions. All approaches to SPT may not be equal. We know that perceivers vary in their use of SPT strategies (Gehlbach \u0026amp; Brinkworth, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). This study raises the possibility that the substantive focus of what perceivers try to infer may also be an important influence on the outcomes of SPT attempts. Future research that tests other foci of SPT attempts, such as inferring a target\u0026rsquo;s future expectations, their preferences between choices, or their emotions, might be particularly valuable.\u003c/p\u003e\u003cp\u003eThe advantage of logic-affirming over intention-affirming SPT was unexpected; our research team had tentatively expected the opposite\u0026mdash;that validating someone else\u0026rsquo;s deeper intentions would more deeply resonate with the other party\u0026rsquo;s core identity (Steele, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1988\u003c/span\u003e; Sherman \u0026amp; Cohen, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). A possible explanation might be participants\u0026rsquo; preferences in social judgement. The stereotype content model (Cuddy et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Fiske, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) suggests there are two dimensions of social perceptions: warmth and competence. \u0026ldquo;Warm\u0026rdquo; people embody traits such as trustworthiness, friendliness, honesty, and likability; \u0026ldquo;competent\u0026rdquo; individuals are considered intelligent and capable (Fiske, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). While warmth may matter more in the evaluation of strangers, competence matters more in self-evaluation (Cuddy et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). In our case, the SPT treatments might have communicated the participant\u0026rsquo;s warmth through affirmation of their intentions, and competence by affirming their logic. If competence were more important for our participants\u0026rsquo; self-evaluations, this implicit feedback message could have felt better to participants, thereby explaining why the logic-affirming SPT condition produced a more powerful sense of liking and connection.\u003c/p\u003e\u003cp\u003eAn alternative explanation involves the different levels of abstraction that might be present in the two treatment conditions. When the expert communicated a specific understanding of the concrete reasons the participants might have in mind, it might make treatment participants feel \u0026ldquo;seen\u0026rdquo; in a psychologically meaningful way. This interpretation is consistent with the prior evidence (e.g., Stanley et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Xu \u0026amp; Petty, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), which emphasizes the importance of thoughtful messages that show nuanced appreciation of the other side\u0026rsquo;s opinion. The intention-affirming SPT intervention, on the other hand, acknowledged what the participant would probably want, which may have seemed like a higher level of abstraction (construal level theory, Trope \u0026amp; Liberman, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The expert\u0026rsquo;s attempt to validate participants\u0026rsquo; intentions may therefore have felt like more of a token effort that landed on them with more psychological distance and less psychological impact.\u003c/p\u003e\u003cp\u003eThe advantage of logic-affirming SPT might also be tied to research on social cognitive conflict resolution. According to Butera et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), when people entertain conflicting ideas about the same issue, trying to find the answer to the issue itself rather than focusing on social opposition would lead to positive learning and relational outcomes. Therefore, when the expert affirmed potential arguments participants might have for the opposite side, participants may have perceived this as an effort to achieve a mutual epistemic learning of the topic. On the other hand, when the speaker affirms that the participants have good intentions, which may often be considered a way of indicating that they are a good person, the effort may be seen as trying to solve interpersonal opposition, which inadvertently shifts the nature of the disagreement from substance to interpersonal. A greater unease for potential interpersonal conflict might be triggered as a result.\u003c/p\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003ePractical Implications\u003c/h2\u003e\u003cp\u003eImportant, but potentially polarizing conversations happen everywhere, from private interactions to public discussions, from classrooms to workplaces, from offline to virtual spaces. These conversations do not always offer opportunities to discover a pre-existing piece of common ground. Sometimes, the people standing on the opposite sides of an issue may share so few things in common that seeking them out might be a lengthy task. Having a recipe for creating common ground in these conversations where it cannot readily be discovered would be a boon for those hoping to engender productive conversations that might lead to collective actions.\u003c/p\u003e\u003cp\u003eOur study found that the small gesture of attempting to take someone else\u0026rsquo;s perspective can produce powerful effects helping to bridge those with polarized opinions. Thus, the two approaches tested in this study \u0026ndash; logic-affirming SPT and intention-affirming SPT \u0026ndash; will be useful for advocacy groups that aim to build common ground among stakeholders. When hosting conversations among these stakeholders, facilitators can encourage opposing parties to try to demonstrate SPT attempts as a means of improving the relational climate and quality of discussions. Classroom teachers at K-12 schools and faculty members in higher education institutions who are trying to promote civic dialogues among students may also embed the two approaches in their classroom activities. Authors in science communications may find the two approaches beneficial in their writing to bridge the distance with their potential audience when writing about the scientific facts that might be contentious.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eLimitations\u003c/h2\u003e\u003cp\u003eWhile the study offers important insights, a few limitations highlight areas where future research is needed. First, conceptually, there has yet to be consensus on what might qualify as a sense of common ground: Does it connotate a particular level of similarity in personality or attitudes, or an alignment of behavioral tendencies? In our study, we used a perceived similarity scale as a proxy of the former, and perceived fairness and personal SPT effort to assess the latter. Future studies could explore the construct of a sense of common ground, examining its definition and measurement, particularly in the context of disagreement.\u003c/p\u003e\u003cp\u003eSecond, the intercorrelations of our outcome variables \u0026ndash; specifically perceived SPT, perceived similarity, perceived fairness, and anticipated relationship \u0026ndash; were substantial (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.71 to .85). Although these constructs are theoretically distinct and have reasonable internal consistency (Cronbach\u0026rsquo;s ɑ = .86 to .91), a broader range of distinct outcomes might benefit future studies.\u003c/p\u003e\u003cp\u003eThird, the perceived SPT scale did not distinguish the perceived SPT \u003cem\u003eaccuracy\u003c/em\u003e items from the items that emphasized perceived SPT \u003cem\u003eeffort\u003c/em\u003e. It is possible for a person to perceive that the other is putting in effort to take their perspective, while simultaneously judging that the other is inaccurately understanding their mind. Perceived SPT accuracy might also serve as a mediator between SPT effort and relational outcomes. Perhaps a perceiver who demonstrates SPT effort only improves relationships with the participant when the perceiver correctly guesses the participant\u0026rsquo;s mind. Therefore, future research could benefit from developing two separate sub-scales \u0026ndash; perceived SPT effort, and perceived SPT accuracy \u0026ndash; to examine the distinct and interaction effects of SPT effort and accuracy.\u003c/p\u003e\u003cp\u003eFinally, the experimental context of this study likely generalizes to only a subset of disagreements that opposing parties might have. Unlike the original town hall setting that inspired this study, our participants read a social media post made by an expert teacher. Participants\u0026rsquo; interactions with the teacher were text-based, impersonal, and unidirectional, potentially making the stakes for taking the perspective of the teacher fairly low. As Gehlbach and Mu (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) posit, whether someone will be sufficiently motivated to take another person\u0026rsquo;s perspective depends on various factors. Factors such as low-stakes situations would likely inhibit SPT motivation. Though these study characteristics frequently exist in social media contexts, future research that increases the interpersonal consequences of the interaction and/or varies the nature of the interactions (e.g., making interactions synchronous and in-person) would be especially illuminating.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, by simulating an online interaction between people holding opposite opinions on a polarizing climate change-related educational policy issue, our study found that both affirming the logical reasoning aspect and the intention aspect of the other side\u0026rsquo;s perspective are effective in improving people\u0026rsquo;s attitudes towards the perspective-taker, with the former tactic being more effective. In the present historical moment when society faces an acute threat from intense political polarization, our findings offer a pathway to create common ground among people from opposing sides. With intentional acts of perspective-taking, difficult conversations can evolve into opportunities for relationship building even in the absence of agreement or prior common ground.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eCompeting Interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003ch2\u003eAuthor Contributions\u003c/h2\u003e\n\u003cp\u003eAll authors jointly conceived and designed the study. Author 1 analyzed data and wrote the manuscript. Authors 2 and 3 contributed equally by giving technical support, discussing the results and implications, and revising the manuscript. Author 4 provided technical guidance on the statistical analyses and commented on previous versions of the manuscript. Author 5 commented on previous versions of the manuscript. Author 6 supervised the project and revised the manuscript substantially.\u003c/p\u003e\n\u003ch2\u003eData and Code Availability\u003c/h2\u003e\n\u003cp\u003eThe dataset generated and analyzed during the current study, and the Stata codes used to clean and analyze data, are available from the corresponding author upon request during the review process. Data and code will be posted on Open Science Framework upon publication.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAllison, P. (2012, September 10). 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Two-sided messages promote openness for morally based attitudes. \u003cem\u003ePersonality and Social Psychology Bulletin\u003c/em\u003e, \u003cem\u003e48\u003c/em\u003e(8), 1151\u0026ndash;1166. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/0146167220988371\u003c/span\u003e\u003cspan address=\"10.1177/0146167220988371\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e The authors distinguish the affirmation conditions in this study from the self-affirmation concept (e.g. Steele, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1988\u003c/span\u003e). The logic- or intention-affirming SPT in this study refers to the attempts to acknowledge the merits in the other people\u0026rsquo;s points of views.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"education controversies, environmental issues, social perspective-taking, political polarization","lastPublishedDoi":"10.21203/rs.3.rs-8234296/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8234296/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDemocracies thrive on constructive conversations between stakeholders with divergent opinions, yet building common ground between these stakeholders to facilitate such conversations appears more difficult than ever. How can opposing parties establish common ground when preexisting shared experiences, traits, or values are not easily uncovered? Drawing on an online sample of U.S. adults (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;558), this preregistered randomized controlled experiment tested social perspective-taking (SPT) as a promising strategy to create common ground between people who hold opposing opinions on a polarizing policy. Specifically, in two treatment conditions, a fictional expert took the perspective of participants by either affirming their (a) reasonable logic or (b) good intentions. The two interventions created a sense of common ground and improved multiple relationship-related outcomes, including perceived perspective-taking, perceived similarity, perceived fairness, and anticipated positive relationship with the opposing expert. Estimated effect sizes further suggest that affirming the other party\u0026rsquo;s logic was about twice as effective as affirming their intentions. In the present historical moment when society faces acute polarization threats, our study provides a recipe for effectively communicating SPT attempts that can catalyze common ground where none existed previously.\u003c/p\u003e","manuscriptTitle":"Social perspective-taking as a catalyst to building common ground between opposing parties","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-08 15:33:03","doi":"10.21203/rs.3.rs-8234296/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"communications-psychology","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"commspsychol","sideBox":"Learn more about [Communications Psychology](http://www.nature.com/commspsychol/)","snPcode":"44271","submissionUrl":"https://mts-commspsychol.nature.com/cgi-bin/main.plex","title":"Communications Psychology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Communications Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9de9fb24-7191-4e06-8f6a-e1c7edca8e04","owner":[],"postedDate":"December 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":59082158,"name":"Social science/Psychology/Human behaviour"},{"id":59082159,"name":"Scientific community and society/Social sciences/Communication"},{"id":59082160,"name":"Scientific community and society/Social sciences/Climate change"},{"id":59082161,"name":"Scientific community and society/Social sciences/Psychology/Human behaviour"},{"id":59082162,"name":"Social science/Education"}],"tags":[],"updatedAt":"2026-04-09T09:58:39+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-08 15:33:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8234296","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8234296","identity":"rs-8234296","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}