Personality-based assortment in helping intentions: Evidence for partner choice using trait cues

Personality-based assortment in helping intentions: Evidence for partner choice using trait cues | 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 Research Article Personality-based assortment in helping intentions: Evidence for partner choice using trait cues Ryo Oda This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9529601/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Cooperation among non-kin requires mechanisms that allow individuals to preferentially direct costly help toward reliable partners. From an evolutionary perspective, positive assortment can be achieved through partner choice based on cues that signal cooperative value. This study tests whether personality traits function as such cues, guiding helping decisions under uncertainty. Building on theories of competitive altruism and biological markets, we focus on agreeableness and conscientiousness as signals of cooperative intent and reliability. A sample of 120 Japanese adults evaluated 40 experimentally constructed personality profiles varying in Big Five traits (excluding neuroticism) and rated their willingness to help and their liking for each target. Linear mixed-effects models showed that openness and extraversion did not predict helping. In contrast, agreeableness and conscientiousness significantly increased helping toward targets, and interaction effects revealed assortative helping based on trait similarity. Mediation analyses indicated that these effects were fully explained by liking, suggesting that trait-based partner choice operates through evolved evaluative mechanisms. These findings support the view that personality traits serve as adaptive social signals that facilitate partner choice and promote the evolutionary stability of cooperation. partner choice positive assortment biological markets competitive altruism agreeableness conscientiousness Introduction Cooperation among non-kin poses a fundamental challenge in evolutionary theory, as individuals must incur immediate costs to benefit others under conditions of uncertainty. A key mechanism underlying the evolution of cooperation is positive assortment—the nonrandom aggregation of individuals with similar trait values (Bowles & Gintis, 2011 ). From the perspective of multilevel selection theory, the evolution of altruism via positive assortment can also extend to non-kin if individuals with altruistic tendencies are able to form networks. Competitive altruism has been proposed as one such mechanism, whereby individuals benefit from signaling their generosity in a “biological market” in which partners can be chosen and prosocial behavior escalates through reputation-based partner selection (Barclay, 2011 ; Hardy & Van Vugt, 2006 ). However, positive assortment requires that individuals identify suitable partners based on limited and often noisy information. In real-world social environments, future behavior cannot be directly observed; thus, decisions about whether to help others must rely on probabilistic inferences drawn from sparse cues. Under such conditions, evaluating isolated behaviors—often context-dependent and variable—is inefficient. Instead, individuals may rely on stable, trait-based representations that summarize behavioral tendencies and facilitate prediction. From this perspective, personality traits can be understood as compressed representations of social information that reduce cognitive load while preserving predictive utility in partner-choice contexts (Oda, 2026 ). This view suggests that personality traits may function as socially usable signals of cooperative reliability. Such expectations are supported by evidence that people can infer personality traits from minimal behavioral input and use these inferences to guide social decisions (e.g., Back et al., 2010 ; Borkenau & Liebler, 1992 ). Within this framework, agreeableness is of particular importance in cooperative contexts. Agreeableness reflects tendencies toward trust, empathy, and concern for others and may signal cooperative intent by structuring intrinsic cost asymmetries between altruistic and exploitative behavior. Individuals high in agreeableness may experience lower subjective costs of helping and higher costs of norm violation, rendering their cooperative behavior more stable and predictable. In addition, agreeableness has been associated with greater sensitivity to social learning and norm internalization, suggesting a role in the formation and maintenance of cooperative norms within social networks. Ehlert et al. ( 2020 ) conducted a longitudinal study examining whether social preferences cluster in real-world networks and how such clustering emerges. They found that cooperative tendencies clustered within networks and that these clusters arose more through social learning (i.e., peer influence) than through partner selection (i.e., actively choosing similar peers). Using the Big Five framework, Rawlings et al. ( 2022 ) examined whether personality traits predicted children’s choice of learning strategy—observing others demonstrate a solution versus attempting the task independently—among UK children aged 7–11 presented with novel puzzle boxes. Children higher in agreeableness were more likely to choose social demonstration. Moreover, among those who selected demonstration, more agreeable children were more likely to engage in “innovation by modification.” Agreeableness thus appears to characterize individuals who build on social information rather than merely imitate others, a tendency the authors describe as “social glue.” From this perspective, individuals may preferentially affiliate with and help others who exhibit similar levels of agreeableness. Such assortment could, in turn, reinforce altruism within social networks through social learning processes. From a partner-choice perspective, these properties make agreeableness a plausible cue for identifying desirable cooperative partners (Oda, 2026 ). Although agreeableness is closely associated with prosocial tendencies such as empathy, trust, and cooperativeness, it is conceptually distinct from altruistic behavior. Agreeableness refers to a stable personality disposition that shapes the likelihood of prosocial responses across situations, whereas altruism refers to specific behaviors that involve incurring costs to benefit others. Importantly, although agreeableness may increase the likelihood of altruistic action, not all agreeable individuals behave altruistically in every situation, and altruistic behavior may also arise from strategic or situational factors independent of stable personality traits. Distinguishing between trait-level dispositions and behavioral outcomes is therefore critical for understanding how individuals make decisions about helping others. In contrast, conscientiousness may contribute to partner selection through a distinct mechanism. Whereas agreeableness signals prosocial motivation, conscientiousness may signal reliability, self-control, and behavioral consistency. These characteristics are critical for sustaining cooperation over repeated interactions, as they enhance the predictability of future behavior. Thus, both agreeableness and conscientiousness may function as cues of partner value, albeit via distinct pathways. If personality traits serve as cues for partner selection, individuals may preferentially direct altruistic behavior toward others who exhibit desirable trait profiles. Moreover, because individuals vary in their own trait levels, they may exhibit assortative tendencies, preferentially helping others who are similar to themselves in traits relevant to cooperation. Such assortative helping may contribute to the formation of cooperative clusters in social networks, complementing mechanisms such as social learning. The present study experimentally examines whether the personality traits of both helpers and targets predict helping intentions toward strangers and whether interactions between these traits produce assortative patterns. Participants evaluated experimentally constructed profiles that varied in the Big Five traits (excluding neuroticism) and indicated their willingness to help and their liking for each target. Neuroticism was excluded because it is the only negatively valenced dimension among the five major personality traits. Although emotional stability may be considered its conceptual counterpart, previous research with Japanese participants suggests that emotional stability is unsuitable as an experimental stimulus. Matsuki and Matsumoto ( 2021 ) developed descriptions portraying individuals characterized by a single prominent Big Five trait. Thirty-five Japanese undergraduates rated each stimulus individual on a five-point scale indicating the extent to which the individual fit each item of the Big Five scale. For all stimulus individuals except the emotional stability type, the intended trait received the highest ratings. However, for the emotional stability type, no significant difference was observed between emotional stability and agreeableness scores. Based on the theoretical framework outlined above, we examine whether traits relevant to cooperative reliability—particularly agreeableness and conscientiousness—serve as cues for partner choice. We propose the following hypotheses. First, traits that signal cooperative value (agreeableness and conscientiousness) will positively predict willingness to help. Second, similarity between participants and targets in these traits will lead to assortative helping. Third, traits less directly related to cooperation (e.g., openness and extraversion) will show weaker or no associations with helping decisions. Finally, similar patterns are expected for liking, reflecting partner-valuation processes in social interaction. Although liking and helping are often positively correlated, they serve partially distinct functions in social decision-making. Liking reflects a general evaluative response or an index of partner value, whereas helping involves a cost-incurring behavioral intention that depends on expectations about future interaction outcomes. From a partner-choice perspective, individuals may like others without being willing to incur costs to help them and, conversely, may provide help for strategic reasons—such as anticipated reciprocity or reputational benefits—even in the absence of strong liking. Distinguishing between these processes is therefore important for determining whether personality-based cues predict general evaluations or more specific cooperative decisions. The present study assesses both liking and helping intentions to examine the extent to which these outcomes exhibit convergent or distinct patterns. From a partner-choice perspective, decisions about whether to help others often rely on limited information and rapid inferences rather than on direct behavioral observation. Vignette-based paradigms therefore provide a useful approximation of these decision-making processes by presenting participants with controlled cues under conditions of uncertainty. Because respondents do not actually incur costs, their stated willingness to help is likely to be higher than it would be in real-life situations. However, because this study focuses on individual differences in helping intentions and on the interaction between helper and recipient personality traits, this bias is unlikely to substantially affect the results. Methods Stimulus For each of the four personality traits, we used a large language model (ChatGPT, OpenAI) to create profiles for 10 target individuals. Each profile consisted of one or two sentences in Japanese, approximately 50–70 characters in length. Five targets were designed to exhibit a high level of the focal trait, whereas the remaining five exhibited a low level. Artificial modifications were applied to the generated results to create the final profile. The targets’ gender was not identifiable, and vocabulary drawn from the personality scale used later to assess trait intensity was removed from the profiles. In a preliminary study, the 40 profiles were evaluated by 61 Japanese participants (30 men and 31 women; median age = 54 years, range = 25–68) who were recruited through Cross Marketing, Inc. (Tokyo, Japan). Participants rated the corresponding personality traits of each target. Personality was assessed using the short form of the Japanese Big Five Scale developed by Namikawa et al. (2012), which is based on the Big-Five Scale of Personality Trait Adjectives (Wada, 1996), a measure commonly used in Japan. The short form contains 29 items and has demonstrated adequate reliability and validity despite the reduced number of items. Following Matsuki and Matsumoto (2021), we employed the five items with the highest factor loadings for each dimension in the present survey. The mean rating across the 61 participants was used as the index of each target’s personality intensity. For each personality trait, ratings differed significantly between the five high-intensity profiles and the five low-intensity profiles (see Table 1). While it could be argued that this evaluation—which selected only the highest-scoring items from the short-form version—lacks validity, evaluating all 40 profiles would have placed a significant burden on the participants; therefore, We prioritized preventing careless responses that might result from such a heavy workload. Furthermore, since each personality trait follows a bimodal distribution, highly precise evaluations are not strictly necessary. In fact, as mentioned above, statistically significant differences were observed across all personality traits. The profiles and detailed personality intensity scores for the 40 targets are available at Open Science Framework (https://osf.io/gax6w/overview?view_only=5d32138326014c82a281c7603db02b24). Participants A total of 120 Japanese adults (60 women and 60 men) were recruited through Cross Marketing, Inc. (Tokyo, Japan). Participants were stratified by age to ensure an equal number of individuals in each age group (15 women and 15 men per group: 20–29, 30–39, 40–49, and 50–59 years). Procedure First, participants were asked to read the profile of one target and rate how much they would be willing to help that person in four situations that varied in cost: (1) being in financial trouble, (2) lying on a railroad crossing as a train approached, (3) struggling with heavy luggage, and (4) wanting someone to listen to them. These four items were averaged to create a composite “help” score. Participants also rated their liking for each target using two items (liking and friendliness), which were averaged to create a “liking” score. All items were rated on a 9-point scale (1 = not at all, 9 = extremely). Participants repeated this procedure for all 40 targets. The order of profile presentation was randomized for each participant. After completing these evaluations, participants completed the short form of the Japanese Big Five Scale developed by Namikawa et al. (2012). Ethical Approval: This study was approved by the Bioethics Review Committee of Nagoya Institute of Technology (No. 2025-11). Human Ethics and Consent to Participate declarations: Informed consent was obtained from all participants prior to participation. Participants were recruited through a survey panel provider (Cross Marketing Inc.), and all participants provided consent in accordance with the provider’s procedures. Funding: This work was supported by JSPS KAKENHI Grant Number 25K00866. Statistical Analysis All analyses were conducted using R (version 4.4.3; R Core Team, 2024). Linear mixed-effects models were fitted using the lme4 package (Bates et al., 2015). Predictor variables were mean-centered prior to analysis. Because each target profile was constructed to emphasize a single focal personality trait, analyses were conducted separately for each trait (openness, conscientiousness, extraversion, and agreeableness). For each trait, two dependent variables were analyzed: willingness to help and liking. For each personality trait, separate linear mixed-effects models were estimated. The dependent variable (help or liking) was predicted by target trait intensity, participant trait intensity, and their interaction. The model can be expressed as: Y ij = β 0 + β 1 (TargetTrait_j) + β 2 (ParticipantTrait_i) + β 3 (TargetTrait × ParticipantTrait) + u_ i+ v_ j+ ε_ ij where u_ i and v_ j represent random intercepts for participants and targets, respectively. Random intercepts for both participants and targets were included to account for the non-independence of observations arising from the repeated-measures design (i.e., multiple targets rated by each participant and multiple ratings obtained for each target). Random slopes were not included because preliminary models including maximal random-effects structures (Barr et al., 2013) resulted in convergence failures and singular fits, likely due to the limited number of observations per grouping factor. Each personality trait was analyzed in a separate model rather than being entered simultaneously. This approach was chosen because each target profile was designed to manipulate a single focal trait dimension. Accordingly, trait-specific models allowed direct tests of whether each trait served as a cue for helping and liking. However, this approach does not fully control for potential cross-trait associations. Therefore, the results should be interpreted as reflecting trait-specific effects under experimentally simplified conditions. Statistical significance was evaluated using a Bonferroni-corrected alpha level of .00625(.05/4 traits × 2 outcomes). Fixed effects were evaluated using t -values, with effects considered significant if they exceeded the corrected threshold. For significant interaction effects, simple slope analyses were conducted at ±1 SD of the moderator variable. These were computed based on the estimated model parameters. Data are available at the Open Science Framework (OSF): https://osf.io/gax6w/overview?view_only=5d32138326014c82a281c7603db02b24. Sample Size Rationale To estimate statistical power, 1,000 simulations of the interaction effects were conducted using the simr package in R, which is designed for power analysis of generalized linear mixed models. The significance level was set at α = .00625 using a Bonferroni correction. The regression coefficients ( β ) for the two main effects were assumed to be 0.30 (medium), and the coefficient for the interaction effect was assumed to be 0.10 (small). The results indicated that a sample size of N = 120 yielded an estimated statistical power of 89.3% (95% CI [87.22%, 91.15%]). Results For each of the 40 target profiles, Cronbach’s α was calculated separately for the four items measuring willingness to help. The resulting α coefficients ranged from .73 to .81, indicating adequate internal consistency despite differences in helping costs and the small number of items. The correlation coefficients between the helping and liking scores were .68 for openness, .69 for conscientiousness, .70 for extraversion, and .70 for agreeableness, t s(1198) = 32.0–34.2, p s < .001. The linear mixed-effects model analyses indicated that predictors of willingness to help varied across personality traits (see Table 2). Participants higher in openness tended to report greater willingness to help; however, this effect did not reach significance under the conservative criterion adopted in this study. Neither target openness nor the interaction between participant and target openness significantly predicted willingness to help. For extraversion, neither the main effects nor the interaction effect significantly predicted willingness to help. Although participants’ conscientiousness did not significantly predict willingness to help, target conscientiousness and the interaction between participant and target conscientiousness were significant predictors. To probe the interaction, simple slope analyses were conducted at ±1 SD of participants’ conscientiousness. The association between target conscientiousness and willingness to help was significant at both low (-1 SD ; β = 0.22, SE = 0.04, t = 6.10, p < .001) and high (+1 SD ; β = 0.39, SE = 0.04, t = 10.70, p < .001) levels of participant conscientiousness, with a stronger slope at higher levels. Participants higher in agreeableness were more willing to help, and targets characterized by higher agreeableness were more likely to receive help. Moreover, the interaction between participant and target agreeableness was significant. To further examine this interaction, simple slope analyses were conducted at ±1 SD of target agreeableness. Participant agreeableness significantly predicted willingness to help when targets were low in agreeableness (-1 SD ; β = 0.70, SE = 0.16, t = 4.30, p < .001) and when targets were high in agreeableness (+1 SD ; β = 0.88, SE = 0.16, t = 5.40, p < .001), with a stronger association for highly agreeable targets. The predictors of liking also varied across personality traits (see Table 3). Participants higher in openness tended to report greater liking for the targets. Although targets higher in openness were also evaluated more favorably, this effect did not reach significance under the conservative criterion adopted in this study. The interaction between participant and target openness was not significant. For extraversion, neither the main effects nor the interaction effect significantly predicted liking. Although participants’ conscientiousness did not significantly predict liking for the targets, target conscientiousness and the interaction between participant and target conscientiousness were significant predictors. The association between target conscientiousness and liking was significant at both low (-1 SD ; β = 0.47, SE = 0.08, t = 6.20, p < .001) and high (+1 SD ; β = 0.87, SE = 0.08, t = 11.60, p < .001) levels of participant conscientiousness, with a stronger slope at higher levels. As with willingness to help, participants higher in agreeableness tended to report greater liking for the targets, and targets higher in agreeableness were more likely to be liked. Because the interaction between participant and target agreeableness was significant, simple slope analyses were conducted at ±1 SD of target agreeableness. Participant agreeableness significantly predicted liking when targets were low in agreeableness (-1 SD ; β = 0.63, SE = 0.18, t = 3.50, p < .001) and when targets were high in agreeableness (+1 SD ; β = 0.95, SE = 0.18, t = 5.30, p < .001), with a stronger association for highly agreeable targets. To examine whether the interaction between participant and target conscientiousness and agreeableness predicted helping intentions via liking, mediation analyses was conducted using linear mixed-effects models. First, whether the interaction effect on helping intentions was mediated by liking was examined for conscientiousness. When liking was included as a predictor in the model, it was a strong positive predictor of helping intentions (Table 4). Importantly, the previously significant interaction effect on helping was no longer significant after controlling for liking. This pattern suggests that the effect of personality similarity on helping intentions was fully mediated by liking. In other words, participants were more willing to help targets who were similar to themselves in conscientiousness because they liked them more. Next, the same analysis was conducted for agreeableness. Liking was also a strong positive predictor of helping intentions, and after controlling for liking, the previously significant interaction effect on helping was no longer significant either (Table 4). Participants were more willing to help targets who were similar to themselves in agreeableness because they liked them more. In the mediation model for agreeableness, the variance associated with the random effect for targets was estimated as zero, indicating that target-level variability was largely accounted for by the fixed effects (i.e., personality traits and liking). This is consistent with the experimental design, in which target profiles were systematically constructed based on personality traits. Discussion The present study examined whether helping intentions toward strangers reflect personality-based positive assortment. Using experimentally manipulated target profiles and mixed-effects models, I identified trait-specific patterns. Openness and extraversion did not significantly predict helping intentions, nor did they interact with target traits. In contrast, conscientiousness and agreeableness exhibited clear assortative patterns. Participants’ conscientiousness did not significantly predict willingness to help in hypothetical situations. However, the present study revealed that targets characterized by higher conscientiousness were more likely to receive help, and this tendency was amplified among participants higher in conscientiousness. Conscientiousness is associated with responsibility, diligence, and norm adherence—qualities that may signal reliability in reciprocity. From this perspective, helping a conscientious individual may be perceived as a lower-risk cooperative investment. The significant interaction between target and participant conscientiousness suggests that individuals higher in conscientiousness are particularly sensitive to such cues, resulting in personality-based assortment in helping intentions. Agreeableness showed both dispositional and assortative effects. More agreeable participants reported a greater overall willingness to help. Importantly, they were especially likely to help agreeable targets, supporting this prediction. Agreeableness may function as a socially salient cue of warmth and cooperative intent, facilitating mutual preference and reinforcing prosocial clustering. However, the present study examined only the association between personality similarity and willingness to help in one-shot, one-on-one interactions with strangers. Future research should investigate how such helping decisions translate into longer-term reciprocal relationships and examine the role of agreeableness in the social learning of reciprocity within networks formed through positive assortment. Taken together, these findings indicate that different personality traits contribute to helping behavior through distinct interpersonal processes. Openness and extraversion appear to be indirectly associated with helping, likely by shaping individuals’ tendencies to seek social engagement and novel interactions rather than by functioning as direct cues in partner evaluation. Conscientiousness, in contrast, may operate as a signal of dependability and norm adherence. Individuals higher in conscientiousness may be perceived as reliable interaction partners, thereby eliciting greater willingness to help, particularly among similarly conscientious individuals. Agreeableness showed both a direct association with helping and an assortative pattern, whereby individuals higher in agreeableness were especially inclined to help similarly agreeable others. This pattern is consistent with research on personality homophily and suggests that interpersonal similarity in prosocial tendencies may facilitate mutually reinforcing helping dynamics. From a personality-process perspective, these results highlight how traits function not only as stable individual differences but also as socially interpretable signals that guide partner preferences, shape expectations of reciprocity, and structure patterns of affiliation within emerging social networks. Over time, such trait-based selection and reinforcement processes may contribute to the stability of cooperative relationships and the clustering of altruistic dispositions in naturalistic settings. The present findings suggest that assortative helping based on agreeableness is primarily driven by evaluative processes. Specifically, similarity in agreeableness increased liking, which in turn fully accounted for the observed effects on helping intentions. This pattern indicates that individuals do not directly use personality similarity as a rule for deciding whom to help; rather, similarity predicts helping indirectly through partner evaluation. From a partner-choice perspective, liking may function as an index of partner value, integrating multiple trait-based cues into a summary evaluation that guides cooperative decisions. Because future behavior cannot be directly observed, individuals may rely on such evaluative processes to estimate the likelihood of beneficial interactions. The present results are consistent with this account, suggesting that personality-based assortment in helping may emerge as a byproduct of evaluative mechanisms rather than a direct matching strategy. Limitations Several limitations warrant consideration. First, helping was assessed using hypothetical scenarios that varied in cost. Whether personality-based positive assortment in helping behavior manifests similarly in real-world interactions remains an open question. Examining these processes in naturalistic social networks would enhance ecological validity. Second, similarity was examined within individual traits rather than across multidimensional personality profiles. In everyday life, however, individuals possess multidimensional profiles that vary in their relative strengths. Interactions among traits may also shape attitudes and behavioral tendencies (e.g., Arora & Rangnekar, 2016). Stronger forms of positive assortment may emerge when multiple traits—such as conscientiousness and agreeableness—are considered simultaneously. Third, the sample consisted solely of Japanese adults; cultural norms emphasizing social harmony may influence the salience of conscientiousness and agreeableness as social signals. The strength and form of trait-based assortment in helping and liking may therefore vary across sociocultural contexts. Replication in more culturally diverse samples is needed to assess the robustness of the present findings. Conclusion These findings suggest that personality traits contribute to helping intentions through distinct interpersonal processes. Conscientiousness and agreeableness appear to function as socially interpretable signals that guide cooperative investment and affiliative preferences, thereby providing a psychological basis for positive assortment in altruism. Declarations Author Contribution R.O. contributed to the study conception and design, data collection and analysis, and wrote the main manuscript text. Data Availability All data have been made publicly available at the Open Science Foundation and can be accessed at https://osf.io/tex6c/overview?view_only=cc71167ee6bf4518868cbb625bdbab52 References Arora, R., & Rangnekar, S. (2016). The interactive effects of conscientiousness and agreeableness on career commitment. Journal of Employment Counseling , 53 , 14–29. https://doi.org/10.1002/joec.12025 Back, M. D., Stopfer, J. M., Vazire, S., Gaddis, S., Schmukle, S. C., Egloff, B., & Gosling, S. D. (2010). Facebook profiles reflect actual personality, not self-idealization. Psychological Science , 21 (3), 372–374. https://doi.org/10.1177/0956797609360756 Barclay, P. (2011). Competitive helping increases with the size of biological markets and invades defection. Journal of Theoretical Biology , 281 , 47–55. https://doi.org/10.1016/j.jtbi.2011.04.023 Barr, D. J., Levy, R., Scheepers, C., & Tily, H. J. (2013). Random effects structure for confirmatory hypothesis testing: Keep it maximal. Journal of Memory and Language , 68 (3), 255–278. https://doi.org/10.1016/j.jml.2012.11.001 Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015). Fitting linear mixed-effects models using lme4. Journal of Statistical Software , 67 (1), 1–48. https://doi.org/10.18637/jss.v067.i01 Borkenau, P., & Liebler, A. (1992). Trait inferences: Sources of validity at zero acquaintance. Journal of Personality and Social Psychology , 62 (4), 645–657. https://doi.org/10.1037/0022-3514.62.4.645 Bowles, S., & Gintis, H. (2011). A Cooperative Species: Human Reciprocity and Its Evolution . Princeton University Press. Ehlert, A., Kindschi, M., Algesheimer, R., & Rauhut, H. (2020). Human social preferences cluster and spread in the field. Proceedings of the National Academy of Sciences of the United States of America , 117 (37), 22787–22792. https://doi.org/10.1073/pnas.2000824117 Hardy, C. L., & Van Vugt, M. (2006). Nice guys finish first: The competitive altruism hypothesis. Personality and Social Psychology Bulletin , 32 (10), 1402–1413. https://doi.org/10.1177/0146167206291006 Matsuki, Y., & Matsumoto, Y. (2021). The effect of personality similarity on evaluations of interpersonal attraction: The role of the characteristic personality traits of the evaluated person. The Japanese Journal of Personality , 29 (3), 141–143. http://doi.org/10.2132/personality.29.3.3 Namikawa, T., Tani, I., Wakita, T., Kumagai, R., Nakane, A., & Noguchi, H. (2012). Development of a short form of the Japanese Big-Five Scale, and a test of its reliability and validity. The Japanese Journal of Psychology , 83 , 91–99. https://doi.org/10.4992/jjpsy.83.91 Oda, R. (2026). Personality as compressed social information in cooperative partner selection. Letters on Evolutionary Behavioral Sciences , 17 (1), 25–30. https://doi.org/10.5178/lebs.2026.134 Rawlings, B. S., Flynn, E. G., & Kendal, R. L. (2022). Personality predicts innovation and social learning in children: Implications for cultural evolution. Developmental Science , e13153. https://doi.org/10.1111/desc.13153 R Core Team (2024). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. https://www.R-project.org/ Wada, S. (1996). Construction of the Big Five Scales of personality trait terms and concurrent validity with NPI. The Japanese Journal of Psychology , 67 , 61–67. https://doi.org/10.4992/jjpsy.67.61 Tables Table 1 Score ranges for high- and low-intensity personality profiles. Personality Range t p Cohen’s d Five with higher score Five with lower score Openness 3.93-3.72 2.81-2.44 13.57 < .001 7.75 Conscientiousness 4.63-4.51 2.60-2.00 21.27 < .001 12.15 Extraversion 4.68-4.36 3.07-2.40 13.16 < .001 7.52 Agreeableness 4.58-4.34 3.39-2.43 9.85 < .001 5.63 df = 8 Table 2 Regression of participant and target personality intensity and their interaction on willingness to help. Fixed effect Openness Conscientiousness Extraversion Agreeableness β SE t p β SE t p β SE t p β SE t p Participant 0.37 0.16 2.31 .022 0.00 0.17 0.02 .986 0.10 0.14 0.77 .442 0.79 0.16 4.93 < .001 Target 0.11 0.08 1.37 .207 0.30 0.03 9.82 < .001 0.00 0.04 -0.04 .967 0.39 0.03 14.81 < .001 Interaction 0.04 0.04 0.97 .332 0.12 0.03 4.49 < .001 0.00 0.02 -0.13 .894 0.10 0.03 3.09 .002 Random effect Participant (var.) 1.92 1.91 1.98 1.65 Target (var.) 0.02 0.01 0.01 0.00 Residual 0.40 0.59 0.42 0.55 Table 3 Regression of participant and target personality intensity and their interaction on liking. Fixed effect Openness Conscientiousness Extraversion Agreeableness β SE t p β SE t p β SE t p β SE t p Participant 0.57 0.18 3.22 .001 -0.08 0.18 -0.47 .639 0.19 0.16 1.20 .233 0.79 0.17 4.58 < .001 Target 0.35 0.13 2.70 .027 0.67 0.07 9.56 < .001 0.00 0.08 -0.04 .970 0.88 0.07 13.67 < .001 Interaction 0.10 0.06 1.61 .107 0.29 0.04 7.22 < .001 0.06 0.03 1.88 .060 0.18 0.05 3.61 < .001 Random effect Participant (var.) 2.14 1.98 2.60 1.83 Target (var.) 0.05 0.06 0.05 0.02 Residual 0.93 1.33 0.82 1.27 Table 4 Regression of participant and target personality intensity and their interaction, as well as liking, on willingness to help. Fixed effect Conscientiousness Agreeableness β SE t p β SE t p Participant 0.02 0.13 0.19 .848 0.50 0.13 4.01 < .001 Target 0.07 0.02 3.45 .003 0.06 0.03 2.34 .019 Interaction 0.02 0.02 0.81 .417 0.04 0.03 1.25 .211 Liking 0.35 0.02 20.45 < .001 0.37 0.02 22.90 < .001 Random effect Participant (var.) 1.12 1.00 Target (var.) 0.00 0.00 Residual 0.45 0.39 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 01 May, 2026 Reviewers agreed at journal 30 Apr, 2026 Reviewers agreed at journal 30 Apr, 2026 Reviewers invited by journal 30 Apr, 2026 Editor assigned by journal 30 Apr, 2026 Submission checks completed at journal 30 Apr, 2026 First submitted to journal 26 Apr, 2026 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. 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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-9529601","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":633080145,"identity":"0190db00-56a4-46a5-87b5-d3e6597dd14c","order_by":0,"name":"Ryo Oda","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIiWNgGAWjYFCCBDApx8AMJBkbkIQIaTFmYGYmUUtiAwNCC37Az578gLmyzSZ9Ozv/wQc/dzDI8zcwPHuAT4tkzzMDxrNtabk7m5mZDXvPMBjOOMCQboBPi8GNBAPGxrbDuRsOM7NJM7YxMG5gYEiTwK8l/QNIS7rBYWb230At9kRoyQHbkgDUwsYM1JJIUItkz5uCgw3n0gyBDjOW7G2TSJ5xmIBf+NnTNz5sKLORNzh/8OGHn202tv3tPWkP8GkBgQOMbHA20EnMPGmEdADBHxQe+zEitIyCUTAKRsEIAgADzEZWIcliIgAAAABJRU5ErkJggg==","orcid":"","institution":"Nagoya Institute of Technology","correspondingAuthor":true,"prefix":"","firstName":"Ryo","middleName":"","lastName":"Oda","suffix":""}],"badges":[],"createdAt":"2026-04-26 05:53:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9529601/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9529601/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108806277,"identity":"ae0861af-7ac7-4e33-b79c-29b6987f93e4","added_by":"auto","created_at":"2026-05-08 15:28:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":462218,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9529601/v1/7951227f-eaae-43fe-94d9-0f73d1787b7b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Personality-based assortment in helping intentions: Evidence for partner choice using trait cues","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCooperation among non-kin poses a fundamental challenge in evolutionary theory, as individuals must incur immediate costs to benefit others under conditions of uncertainty. A key mechanism underlying the evolution of cooperation is positive assortment\u0026mdash;the nonrandom aggregation of individuals with similar trait values (Bowles \u0026amp; Gintis, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). From the perspective of multilevel selection theory, the evolution of altruism via positive assortment can also extend to non-kin if individuals with altruistic tendencies are able to form networks. Competitive altruism has been proposed as one such mechanism, whereby individuals benefit from signaling their generosity in a \u0026ldquo;biological market\u0026rdquo; in which partners can be chosen and prosocial behavior escalates through reputation-based partner selection (Barclay, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Hardy \u0026amp; Van Vugt, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). However, positive assortment requires that individuals identify suitable partners based on limited and often noisy information.\u003c/p\u003e \u003cp\u003eIn real-world social environments, future behavior cannot be directly observed; thus, decisions about whether to help others must rely on probabilistic inferences drawn from sparse cues. Under such conditions, evaluating isolated behaviors\u0026mdash;often context-dependent and variable\u0026mdash;is inefficient. Instead, individuals may rely on stable, trait-based representations that summarize behavioral tendencies and facilitate prediction. From this perspective, personality traits can be understood as compressed representations of social information that reduce cognitive load while preserving predictive utility in partner-choice contexts (Oda, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). This view suggests that personality traits may function as socially usable signals of cooperative reliability. Such expectations are supported by evidence that people can infer personality traits from minimal behavioral input and use these inferences to guide social decisions (e.g., Back et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Borkenau \u0026amp; Liebler, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1992\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWithin this framework, agreeableness is of particular importance in cooperative contexts. Agreeableness reflects tendencies toward trust, empathy, and concern for others and may signal cooperative intent by structuring intrinsic cost asymmetries between altruistic and exploitative behavior. Individuals high in agreeableness may experience lower subjective costs of helping and higher costs of norm violation, rendering their cooperative behavior more stable and predictable. In addition, agreeableness has been associated with greater sensitivity to social learning and norm internalization, suggesting a role in the formation and maintenance of cooperative norms within social networks.\u003c/p\u003e \u003cp\u003eEhlert et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) conducted a longitudinal study examining whether social preferences cluster in real-world networks and how such clustering emerges. They found that cooperative tendencies clustered within networks and that these clusters arose more through social learning (i.e., peer influence) than through partner selection (i.e., actively choosing similar peers). Using the Big Five framework, Rawlings et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) examined whether personality traits predicted children\u0026rsquo;s choice of learning strategy\u0026mdash;observing others demonstrate a solution versus attempting the task independently\u0026mdash;among UK children aged 7\u0026ndash;11 presented with novel puzzle boxes. Children higher in agreeableness were more likely to choose social demonstration. Moreover, among those who selected demonstration, more agreeable children were more likely to engage in \u0026ldquo;innovation by modification.\u0026rdquo; Agreeableness thus appears to characterize individuals who build on social information rather than merely imitate others, a tendency the authors describe as \u0026ldquo;social glue.\u0026rdquo; From this perspective, individuals may preferentially affiliate with and help others who exhibit similar levels of agreeableness. Such assortment could, in turn, reinforce altruism within social networks through social learning processes. From a partner-choice perspective, these properties make agreeableness a plausible cue for identifying desirable cooperative partners (Oda, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2026\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlthough agreeableness is closely associated with prosocial tendencies such as empathy, trust, and cooperativeness, it is conceptually distinct from altruistic behavior. Agreeableness refers to a stable personality disposition that shapes the likelihood of prosocial responses across situations, whereas altruism refers to specific behaviors that involve incurring costs to benefit others. Importantly, although agreeableness may increase the likelihood of altruistic action, not all agreeable individuals behave altruistically in every situation, and altruistic behavior may also arise from strategic or situational factors independent of stable personality traits. Distinguishing between trait-level dispositions and behavioral outcomes is therefore critical for understanding how individuals make decisions about helping others.\u003c/p\u003e \u003cp\u003eIn contrast, conscientiousness may contribute to partner selection through a distinct mechanism. Whereas agreeableness signals prosocial motivation, conscientiousness may signal reliability, self-control, and behavioral consistency. These characteristics are critical for sustaining cooperation over repeated interactions, as they enhance the predictability of future behavior. Thus, both agreeableness and conscientiousness may function as cues of partner value, albeit via distinct pathways.\u003c/p\u003e \u003cp\u003eIf personality traits serve as cues for partner selection, individuals may preferentially direct altruistic behavior toward others who exhibit desirable trait profiles. Moreover, because individuals vary in their own trait levels, they may exhibit assortative tendencies, preferentially helping others who are similar to themselves in traits relevant to cooperation. Such assortative helping may contribute to the formation of cooperative clusters in social networks, complementing mechanisms such as social learning.\u003c/p\u003e \u003cp\u003eThe present study experimentally examines whether the personality traits of both helpers and targets predict helping intentions toward strangers and whether interactions between these traits produce assortative patterns. Participants evaluated experimentally constructed profiles that varied in the Big Five traits (excluding neuroticism) and indicated their willingness to help and their liking for each target. Neuroticism was excluded because it is the only negatively valenced dimension among the five major personality traits. Although emotional stability may be considered its conceptual counterpart, previous research with Japanese participants suggests that emotional stability is unsuitable as an experimental stimulus. Matsuki and Matsumoto (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) developed descriptions portraying individuals characterized by a single prominent Big Five trait. Thirty-five Japanese undergraduates rated each stimulus individual on a five-point scale indicating the extent to which the individual fit each item of the Big Five scale. For all stimulus individuals except the emotional stability type, the intended trait received the highest ratings. However, for the emotional stability type, no significant difference was observed between emotional stability and agreeableness scores.\u003c/p\u003e \u003cp\u003eBased on the theoretical framework outlined above, we examine whether traits relevant to cooperative reliability\u0026mdash;particularly agreeableness and conscientiousness\u0026mdash;serve as cues for partner choice. We propose the following hypotheses. First, traits that signal cooperative value (agreeableness and conscientiousness) will positively predict willingness to help. Second, similarity between participants and targets in these traits will lead to assortative helping. Third, traits less directly related to cooperation (e.g., openness and extraversion) will show weaker or no associations with helping decisions. Finally, similar patterns are expected for liking, reflecting partner-valuation processes in social interaction.\u003c/p\u003e \u003cp\u003eAlthough liking and helping are often positively correlated, they serve partially distinct functions in social decision-making. Liking reflects a general evaluative response or an index of partner value, whereas helping involves a cost-incurring behavioral intention that depends on expectations about future interaction outcomes. From a partner-choice perspective, individuals may like others without being willing to incur costs to help them and, conversely, may provide help for strategic reasons\u0026mdash;such as anticipated reciprocity or reputational benefits\u0026mdash;even in the absence of strong liking. Distinguishing between these processes is therefore important for determining whether personality-based cues predict general evaluations or more specific cooperative decisions. The present study assesses both liking and helping intentions to examine the extent to which these outcomes exhibit convergent or distinct patterns.\u003c/p\u003e \u003cp\u003eFrom a partner-choice perspective, decisions about whether to help others often rely on limited information and rapid inferences rather than on direct behavioral observation. Vignette-based paradigms therefore provide a useful approximation of these decision-making processes by presenting participants with controlled cues under conditions of uncertainty. Because respondents do not actually incur costs, their stated willingness to help is likely to be higher than it would be in real-life situations. However, because this study focuses on individual differences in helping intentions and on the interaction between helper and recipient personality traits, this bias is unlikely to substantially affect the results.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eStimulus\u003c/p\u003e\n\u003cp\u003eFor each of the four personality traits, we used a large language model (ChatGPT, OpenAI) to create profiles for 10 target individuals. Each profile consisted of one or two sentences in Japanese, approximately 50–70 characters in length. Five targets were designed to exhibit a high level of the focal trait, whereas the remaining five exhibited a low level. Artificial modifications were applied to the generated results to create the final profile. The targets’ gender was not identifiable, and vocabulary drawn from the personality scale used later to assess trait intensity was removed from the profiles. In a preliminary study, the 40 profiles were evaluated by 61 Japanese participants (30 men and 31 women; median age = 54 years, range = 25–68) who were recruited through Cross Marketing, Inc. (Tokyo, Japan). Participants rated the corresponding personality traits of each target. Personality was assessed using the short form of the Japanese Big Five Scale developed by Namikawa et al. (2012), which is based on the Big-Five Scale of Personality Trait Adjectives (Wada, 1996), a measure commonly used in Japan. The short form contains 29 items and has demonstrated adequate reliability and validity despite the reduced number of items. Following Matsuki and Matsumoto (2021), we employed the five items with the highest factor loadings for each dimension in the present survey.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe mean rating across the 61 participants was used as the index of each target’s personality intensity. For each personality trait, ratings differed significantly between the five high-intensity profiles and the five low-intensity profiles (see Table 1). While it could be argued that this evaluation—which selected only the highest-scoring items from the short-form version—lacks validity, evaluating all 40 profiles would have placed a significant burden on the participants; therefore, We prioritized preventing careless responses that might result from such a heavy workload. Furthermore, since each personality trait follows a bimodal distribution, highly precise evaluations are not strictly necessary. In fact, as mentioned above, statistically significant differences were observed across all personality traits. The profiles and detailed personality intensity scores for the 40 targets are available at Open Science Framework (https://osf.io/gax6w/overview?view_only=5d32138326014c82a281c7603db02b24).\u003c/p\u003e\n\u003cp\u003eParticipants\u003c/p\u003e\n\u003cp\u003eA total of 120 Japanese adults (60 women and 60 men) were recruited through Cross Marketing, Inc. (Tokyo, Japan). Participants were stratified by age to ensure an equal number of individuals in each age group (15 women and 15 men per group: 20–29, 30–39, 40–49, and 50–59 years).\u003c/p\u003e\n\u003cp\u003eProcedure\u003c/p\u003e\n\u003cp\u003eFirst, participants were asked to read the profile of one target and rate how much they would be willing to help that person in four situations that varied in cost: (1) being in financial trouble, (2) lying on a railroad crossing as a train approached, (3) struggling with heavy luggage, and (4) wanting someone to listen to them. These four items were averaged to create a composite “help” score. Participants also rated their liking for each target using two items (liking and friendliness), which were averaged to create a “liking” score. All items were rated on a 9-point scale (1 = not at all, 9 = extremely). Participants repeated this procedure for all 40 targets. The order of profile presentation was randomized for each participant. After completing these evaluations, participants completed the short form of the Japanese Big Five Scale developed by Namikawa et al. (2012).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEthical Approval: This study was approved by the Bioethics Review Committee of Nagoya Institute of Technology (No. 2025-11).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHuman Ethics and Consent to Participate declarations: Informed consent was obtained from all participants prior to participation. Participants were recruited through a survey panel provider (Cross Marketing Inc.), and all participants provided consent in accordance with the provider’s procedures. Funding: This work was supported by JSPS KAKENHI Grant Number 25K00866.\u003c/p\u003e\n\u003cp\u003eStatistical Analysis\u003c/p\u003e\n\u003cp\u003eAll analyses were conducted using R (version 4.4.3; R Core Team, 2024). Linear mixed-effects models were fitted using the \u003cem\u003elme4\u003c/em\u003e package (Bates et al., 2015). Predictor variables were mean-centered prior to analysis.\u003c/p\u003e\n\u003cp\u003eBecause each target profile was constructed to emphasize a single focal personality trait, analyses were conducted separately for each trait (openness, conscientiousness, extraversion, and agreeableness). For each trait, two dependent variables were analyzed: willingness to help and liking. For each personality trait, separate linear mixed-effects models were estimated. The dependent variable (help or liking) was predicted by target trait intensity, participant trait intensity, and their interaction. The model can be expressed as:\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eY\u003csub\u003eij\u003c/sub\u003e\u003c/em\u003e =\u003cem\u003e\u0026nbsp;β\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e + \u003cem\u003eβ\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e(TargetTrait_j) + \u003cem\u003eβ\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e(ParticipantTrait_i) + \u003cem\u003eβ\u003c/em\u003e\u003csub\u003e3\u003c/sub\u003e(TargetTrait × ParticipantTrait) + \u003cem\u003eu_\u003c/em\u003ei+ \u003cem\u003ev_\u003c/em\u003ej+ \u003cem\u003eε_\u003c/em\u003eij\u003c/p\u003e\n\u003cp\u003ewhere \u003cem\u003eu_\u003c/em\u003ei and \u003cem\u003ev_\u003c/em\u003ej represent random intercepts for participants and targets, respectively.\u003c/p\u003e\n\u003cp\u003eRandom intercepts for both participants and targets were included to account for the non-independence of observations arising from the repeated-measures design (i.e., multiple targets rated by each participant and multiple ratings obtained for each target). Random slopes were not included because preliminary models including maximal random-effects structures (Barr et al., 2013) resulted in convergence failures and singular fits, likely due to the limited number of observations per grouping factor.\u003c/p\u003e\n\u003cp\u003eEach personality trait was analyzed in a separate model rather than being entered simultaneously. This approach was chosen because each target profile was designed to manipulate a single focal trait dimension. Accordingly, trait-specific models allowed direct tests of whether each trait served as a cue for helping and liking. However, this approach does not fully control for potential cross-trait associations. Therefore, the results should be interpreted as reflecting trait-specific effects under experimentally simplified conditions.\u003c/p\u003e\n\u003cp\u003eStatistical significance was evaluated using a Bonferroni-corrected alpha level of .00625(.05/4 traits × 2 outcomes). Fixed effects were evaluated using \u003cem\u003et\u003c/em\u003e-values, with effects considered significant if they exceeded the corrected threshold. For significant interaction effects, simple slope analyses were conducted at ±1 \u003cem\u003eSD\u003c/em\u003e of the moderator variable. These were computed based on the estimated model parameters. Data are available at the Open Science Framework (OSF): https://osf.io/gax6w/overview?view_only=5d32138326014c82a281c7603db02b24.\u003c/p\u003e\n\u003cp\u003eSample Size Rationale\u003c/p\u003e\n\u003cp\u003eTo estimate statistical power, 1,000 simulations of the interaction effects were conducted using the \u003cem\u003esimr\u003c/em\u003e package in R, which is designed for power analysis of generalized linear mixed models. The significance level was set at α = .00625 using a Bonferroni correction. The regression coefficients (\u003cem\u003eβ\u003c/em\u003e) for the two main effects were assumed to be 0.30 (medium), and the coefficient for the interaction effect was assumed to be 0.10 (small). The results indicated that a sample size of N = 120 yielded an estimated statistical power of 89.3% (95% CI [87.22%, 91.15%]).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eFor each of the 40 target profiles, Cronbach\u0026rsquo;s \u003cem\u003e\u0026alpha;\u003c/em\u003e was calculated separately for the four items measuring willingness to help. The resulting \u0026alpha; coefficients ranged from .73 to .81, indicating adequate internal consistency despite differences in helping costs and the small number of items. The correlation coefficients between the helping and liking scores were .68 for openness, .69 for conscientiousness, .70 for extraversion, and .70 for agreeableness, \u003cem\u003et\u003c/em\u003es(1198) = 32.0\u0026ndash;34.2, \u003cem\u003ep\u003c/em\u003es \u0026lt; .001.\u003c/p\u003e\n\u003cp\u003eThe linear mixed-effects model analyses indicated that predictors of willingness to help varied across personality traits (see Table 2). Participants higher in openness tended to report greater willingness to help; however, this effect did not reach significance under the conservative criterion adopted in this study. Neither target openness nor the interaction between participant and target openness significantly predicted willingness to help. For extraversion, neither the main effects nor the interaction effect significantly predicted willingness to help.\u003c/p\u003e\n\u003cp\u003eAlthough participants\u0026rsquo; conscientiousness did not significantly predict willingness to help, target conscientiousness and the interaction between participant and target conscientiousness were significant predictors. To probe the interaction, simple slope analyses were conducted at \u0026plusmn;1 \u003cem\u003eSD\u003c/em\u003e of participants\u0026rsquo; conscientiousness. The association between target conscientiousness and willingness to help was significant at both low (-1 \u003cem\u003eSD\u003c/em\u003e; \u003cem\u003e\u0026beta;\u003c/em\u003e = 0.22, \u003cem\u003eSE\u003c/em\u003e = 0.04, \u003cem\u003et\u003c/em\u003e = 6.10, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001) and high (+1 \u003cem\u003eSD\u003c/em\u003e; \u003cem\u003e\u0026beta;\u003c/em\u003e = 0.39, \u003cem\u003eSE\u003c/em\u003e = 0.04, \u003cem\u003et\u003c/em\u003e = 10.70, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001) levels of participant conscientiousness, with a stronger slope at higher levels. Participants higher in agreeableness were more willing to help, and targets characterized by higher agreeableness were more likely to receive help. Moreover, the interaction between participant and target agreeableness was significant. To further examine this interaction, simple slope analyses were conducted at \u0026plusmn;1 \u003cem\u003eSD\u003c/em\u003e of target agreeableness. Participant agreeableness significantly predicted willingness to help when targets were low in agreeableness (-1 \u003cem\u003eSD\u003c/em\u003e; \u003cem\u003e\u0026beta;\u0026nbsp;\u003c/em\u003e= 0.70, \u003cem\u003eSE\u003c/em\u003e = 0.16, \u003cem\u003et\u003c/em\u003e = 4.30, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001) and when targets were high in agreeableness (+1 \u003cem\u003eSD\u003c/em\u003e; \u003cem\u003e\u0026beta;\u003c/em\u003e = 0.88, \u003cem\u003eSE\u003c/em\u003e = 0.16, \u003cem\u003et\u0026nbsp;\u003c/em\u003e= 5.40,\u003cem\u003e\u0026nbsp;p\u003c/em\u003e \u0026lt; .001), with a stronger association for highly agreeable targets.\u003c/p\u003e\n\u003cp\u003eThe predictors of liking also varied across personality traits (see Table 3). Participants higher in openness tended to report greater liking for the targets. Although targets higher in openness were also evaluated more favorably, this effect did not reach significance under the conservative criterion adopted in this study. The interaction between participant and target openness was not significant. For extraversion, neither the main effects nor the interaction effect significantly predicted liking.\u003c/p\u003e\n\u003cp\u003eAlthough participants\u0026rsquo; conscientiousness did not significantly predict liking for the targets, target conscientiousness and the interaction between participant and target conscientiousness were significant predictors. The association between target conscientiousness and liking was significant at both low (-1 \u003cem\u003eSD\u003c/em\u003e; \u003cem\u003e\u0026beta;\u003c/em\u003e = 0.47, \u003cem\u003eSE\u003c/em\u003e = 0.08, \u003cem\u003et\u003c/em\u003e = 6.20, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; .001) and high (+1 \u003cem\u003eSD\u003c/em\u003e; \u003cem\u003e\u0026beta;\u003c/em\u003e = 0.87, \u003cem\u003eSE\u003c/em\u003e = 0.08, \u003cem\u003et\u003c/em\u003e = 11.60, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001) levels of participant conscientiousness, with a stronger slope at higher levels. As with willingness to help, participants higher in agreeableness tended to report greater liking for the targets, and targets higher in agreeableness were more likely to be liked. Because the interaction between participant and target agreeableness was significant, simple slope analyses were conducted at \u0026plusmn;1 \u003cem\u003eSD\u003c/em\u003e of target agreeableness. Participant agreeableness significantly predicted liking when targets were low in agreeableness (-1 \u003cem\u003eSD\u003c/em\u003e; \u003cem\u003e\u0026beta;\u003c/em\u003e = 0.63, \u003cem\u003eSE\u003c/em\u003e = 0.18, \u003cem\u003et\u003c/em\u003e = 3.50, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001) and when targets were high in agreeableness (+1 \u003cem\u003eSD\u003c/em\u003e; \u003cem\u003e\u0026beta;\u003c/em\u003e = 0.95,\u003cem\u003e\u0026nbsp;SE\u003c/em\u003e = 0.18,\u003cem\u003e\u0026nbsp;t\u0026nbsp;\u003c/em\u003e= 5.30, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001), with a stronger association for highly agreeable targets.\u003c/p\u003e\n\u003cp\u003eTo examine whether the interaction between participant and target conscientiousness and agreeableness predicted helping intentions via liking, mediation analyses was conducted using linear mixed-effects models. First, whether the interaction effect on helping intentions was mediated by liking was examined for conscientiousness. When liking was included as a predictor in the model, it was a strong positive predictor of helping intentions (Table 4). Importantly, the previously significant interaction effect on helping was no longer significant after controlling for liking. This pattern suggests that the effect of personality similarity on helping intentions was fully mediated by liking. In other words, participants were more willing to help targets who were similar to themselves in conscientiousness because they liked them more. Next, the same analysis was conducted for agreeableness. Liking was also a strong positive predictor of helping intentions, and after controlling for liking, the previously significant interaction effect on helping was no longer significant either (Table 4). Participants were more willing to help targets who were similar to themselves in agreeableness because they liked them more. In the mediation model for agreeableness, the variance associated with the random effect for targets was estimated as zero, indicating that target-level variability was largely accounted for by the fixed effects (i.e., personality traits and liking). This is consistent with the experimental design, in which target profiles were systematically constructed based on personality traits.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study examined whether helping intentions toward strangers reflect personality-based positive assortment. Using experimentally manipulated target profiles and mixed-effects models, I identified trait-specific patterns. Openness and extraversion did not significantly predict helping intentions, nor did they interact with target traits. In contrast, conscientiousness and agreeableness exhibited clear assortative patterns.\u003c/p\u003e\n\u003cp\u003eParticipants\u0026rsquo; conscientiousness did not significantly predict willingness to help in hypothetical situations. However, the present study revealed that targets characterized by higher conscientiousness were more likely to receive help, and this tendency was amplified among participants higher in conscientiousness. Conscientiousness is associated with responsibility, diligence, and norm adherence\u0026mdash;qualities that may signal reliability in reciprocity. From this perspective, helping a conscientious individual may be perceived as a lower-risk cooperative investment. The significant interaction between target and participant conscientiousness suggests that individuals higher in conscientiousness are particularly sensitive to such cues, resulting in personality-based assortment in helping intentions.\u003c/p\u003e\n\u003cp\u003eAgreeableness showed both dispositional and assortative effects. More agreeable participants reported a greater overall willingness to help. Importantly, they were especially likely to help agreeable targets, supporting this prediction. Agreeableness may function as a socially salient cue of warmth and cooperative intent, facilitating mutual preference and reinforcing prosocial clustering. However, the present study examined only the association between personality similarity and willingness to help in one-shot, one-on-one interactions with strangers. Future research should investigate how such helping decisions translate into longer-term reciprocal relationships and examine the role of agreeableness in the social learning of reciprocity within networks formed through positive assortment.\u003c/p\u003e\n\u003cp\u003eTaken together, these findings indicate that different personality traits contribute to helping behavior through distinct interpersonal processes. Openness and extraversion appear to be indirectly associated with helping, likely by shaping individuals\u0026rsquo; tendencies to seek social engagement and novel interactions rather than by functioning as direct cues in partner evaluation. Conscientiousness, in contrast, may operate as a signal of dependability and norm adherence. Individuals higher in conscientiousness may be perceived as reliable interaction partners, thereby eliciting greater willingness to help, particularly among similarly conscientious individuals. Agreeableness showed both a direct association with helping and an assortative pattern, whereby individuals higher in agreeableness were especially inclined to help similarly agreeable others. This pattern is consistent with research on personality homophily and suggests that interpersonal similarity in prosocial tendencies may facilitate mutually reinforcing helping dynamics. From a personality-process perspective, these results highlight how traits function not only as stable individual differences but also as socially interpretable signals that guide partner preferences, shape expectations of reciprocity, and structure patterns of affiliation within emerging social networks. Over time, such trait-based selection and reinforcement processes may contribute to the stability of cooperative relationships and the clustering of altruistic dispositions in naturalistic settings.\u003c/p\u003e\n\u003cp\u003eThe present findings suggest that assortative helping based on agreeableness is primarily driven by evaluative processes. Specifically, similarity in agreeableness increased liking, which in turn fully accounted for the observed effects on helping intentions. This pattern indicates that individuals do not directly use personality similarity as a rule for deciding whom to help; rather, similarity predicts helping indirectly through partner evaluation. From a partner-choice perspective, liking may function as an index of partner value, integrating multiple trait-based cues into a summary evaluation that guides cooperative decisions. Because future behavior cannot be directly observed, individuals may rely on such evaluative processes to estimate the likelihood of beneficial interactions. The present results are consistent with this account, suggesting that personality-based assortment in helping may emerge as a byproduct of evaluative mechanisms rather than a direct matching strategy.\u003c/p\u003e\n\u003cp\u003eLimitations\u003c/p\u003e\n\u003cp\u003eSeveral limitations warrant consideration. First, helping was assessed using hypothetical scenarios that varied in cost. Whether personality-based positive assortment in helping behavior manifests similarly in real-world interactions remains an open question. Examining these processes in naturalistic social networks would enhance ecological validity. Second, similarity was examined within individual traits rather than across multidimensional personality profiles. In everyday life, however, individuals possess multidimensional profiles that vary in their relative strengths. Interactions among traits may also shape attitudes and behavioral tendencies (e.g., Arora \u0026amp; Rangnekar, 2016). Stronger forms of positive assortment may emerge when multiple traits\u0026mdash;such as conscientiousness and agreeableness\u0026mdash;are considered simultaneously. Third, the sample consisted solely of Japanese adults; cultural norms emphasizing social harmony may influence the salience of conscientiousness and agreeableness as social signals. The strength and form of trait-based assortment in helping and liking may therefore vary across sociocultural contexts. Replication in more culturally diverse samples is needed to assess the robustness of the present findings.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThese findings suggest that personality traits contribute to helping intentions through distinct interpersonal processes. Conscientiousness and agreeableness appear to function as socially interpretable signals that guide cooperative investment and affiliative preferences, thereby providing a psychological basis for positive assortment in altruism.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eR.O. contributed to the study conception and design, data collection and analysis, and wrote the main manuscript text.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll data have been made publicly available at the Open Science Foundation and can be accessed at https://osf.io/tex6c/overview?view_only=cc71167ee6bf4518868cbb625bdbab52\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eArora, R., \u0026amp; Rangnekar, S. (2016). The interactive effects of conscientiousness and agreeableness on career commitment. \u003cem\u003eJournal of Employment Counseling\u003c/em\u003e, \u003cem\u003e53\u003c/em\u003e, 14\u0026ndash;29. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/joec.12025\u003c/span\u003e\u003cspan address=\"10.1002/joec.12025\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBack, M. D., Stopfer, J. M., Vazire, S., Gaddis, S., Schmukle, S. C., Egloff, B., \u0026amp; Gosling, S. D. (2010). Facebook profiles reflect actual personality, not self-idealization. \u003cem\u003ePsychological Science\u003c/em\u003e, \u003cem\u003e21\u003c/em\u003e(3), 372\u0026ndash;374. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/0956797609360756\u003c/span\u003e\u003cspan address=\"10.1177/0956797609360756\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarclay, P. (2011). Competitive helping increases with the size of biological markets and invades defection. \u003cem\u003eJournal of Theoretical Biology\u003c/em\u003e, \u003cem\u003e281\u003c/em\u003e, 47\u0026ndash;55. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jtbi.2011.04.023\u003c/span\u003e\u003cspan address=\"10.1016/j.jtbi.2011.04.023\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarr, D. J., Levy, R., Scheepers, C., \u0026amp; Tily, H. J. (2013). Random effects structure for confirmatory hypothesis testing: Keep it maximal. \u003cem\u003eJournal of Memory and Language\u003c/em\u003e, \u003cem\u003e68\u003c/em\u003e(3), 255\u0026ndash;278. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jml.2012.11.001\u003c/span\u003e\u003cspan address=\"10.1016/j.jml.2012.11.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBates, D., M\u0026auml;chler, M., Bolker, B., \u0026amp; Walker, S. (2015). Fitting linear mixed-effects models using lme4. \u003cem\u003eJournal of Statistical Software\u003c/em\u003e, \u003cem\u003e67\u003c/em\u003e(1), 1\u0026ndash;48. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.18637/jss.v067.i01\u003c/span\u003e\u003cspan address=\"10.18637/jss.v067.i01\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBorkenau, P., \u0026amp; Liebler, A. (1992). Trait inferences: Sources of validity at zero acquaintance. \u003cem\u003eJournal of Personality and Social Psychology\u003c/em\u003e, \u003cem\u003e62\u003c/em\u003e(4), 645\u0026ndash;657. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1037/0022-3514.62.4.645\u003c/span\u003e\u003cspan address=\"10.1037/0022-3514.62.4.645\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBowles, S., \u0026amp; Gintis, H. (2011). \u003cem\u003eA Cooperative Species: Human Reciprocity and Its Evolution\u003c/em\u003e. Princeton University Press.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEhlert, A., Kindschi, M., Algesheimer, R., \u0026amp; Rauhut, H. (2020). Human social preferences cluster and spread in the field. \u003cem\u003eProceedings of the National Academy of Sciences of the United States of America\u003c/em\u003e, \u003cem\u003e117\u003c/em\u003e(37), 22787\u0026ndash;22792. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1073/pnas.2000824117\u003c/span\u003e\u003cspan address=\"10.1073/pnas.2000824117\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHardy, C. L., \u0026amp; Van Vugt, M. (2006). Nice guys finish first: The competitive altruism hypothesis. \u003cem\u003ePersonality and Social Psychology Bulletin\u003c/em\u003e, \u003cem\u003e32\u003c/em\u003e(10), 1402\u0026ndash;1413. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/0146167206291006\u003c/span\u003e\u003cspan address=\"10.1177/0146167206291006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMatsuki, Y., \u0026amp; Matsumoto, Y. (2021). The effect of personality similarity on evaluations of interpersonal attraction: The role of the characteristic personality traits of the evaluated person. \u003cem\u003eThe Japanese Journal of Personality\u003c/em\u003e, \u003cem\u003e29\u003c/em\u003e(3), 141\u0026ndash;143. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://doi.org/10.2132/personality.29.3.3\u003c/span\u003e\u003cspan address=\"10.2132/personality.29.3.3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNamikawa, T., Tani, I., Wakita, T., Kumagai, R., Nakane, A., \u0026amp; Noguchi, H. (2012). Development of a short form of the Japanese Big-Five Scale, and a test of its reliability and validity. \u003cem\u003eThe Japanese Journal of Psychology\u003c/em\u003e, \u003cem\u003e83\u003c/em\u003e, 91\u0026ndash;99. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4992/jjpsy.83.91\u003c/span\u003e\u003cspan address=\"10.4992/jjpsy.83.91\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOda, R. (2026). Personality as compressed social information in cooperative partner selection. \u003cem\u003eLetters on Evolutionary Behavioral Sciences\u003c/em\u003e, \u003cem\u003e17\u003c/em\u003e(1), 25\u0026ndash;30. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5178/lebs.2026.134\u003c/span\u003e\u003cspan address=\"10.5178/lebs.2026.134\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRawlings, B. S., Flynn, E. G., \u0026amp; Kendal, R. L. (2022). Personality predicts innovation and social learning in children: Implications for cultural evolution. \u003cem\u003eDevelopmental Science\u003c/em\u003e, e13153. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/desc.13153\u003c/span\u003e\u003cspan address=\"10.1111/desc.13153\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eR Core Team (2024). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.R-project.org/\u003c/span\u003e\u003cspan address=\"https://www.R-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWada, S. (1996). Construction of the Big Five Scales of personality trait terms and concurrent validity with NPI. \u003cem\u003eThe Japanese Journal of Psychology\u003c/em\u003e, \u003cem\u003e67\u003c/em\u003e, 61\u0026ndash;67. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4992/jjpsy.67.61\u003c/span\u003e\u003cspan address=\"10.4992/jjpsy.67.61\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\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\u003eScore ranges for high- and low-intensity personality profiles.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026minus;\" 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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePersonality\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFive with higher score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFive with lower score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOpenness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e3.93-3.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e2.81-2.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConscientiousness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e4.63-4.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e2.60-2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExtraversion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e4.68-4.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e3.07-2.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgreeableness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e4.58-4.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e3.39-2.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003edf\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \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\u003eRegression of participant and target personality intensity and their interaction on willingness to help.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"20\"\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=\"left\" 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=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c18\" colnum=\"18\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c19\" colnum=\"19\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c20\" colnum=\"20\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFixed effect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eOpenness\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e \u003cp\u003eConscientiousness\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c15\" namest=\"c12\"\u003e \u003cp\u003eExtraversion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c20\" namest=\"c17\"\u003e \u003cp\u003eAgreeableness\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eβ\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eβ\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cem\u003eβ\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c17\"\u003e \u003cp\u003e\u003cem\u003eβ\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c18\"\u003e \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c19\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c20\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParticipant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e.442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e \u003cp\u003e4.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTarget\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e9.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e-0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e.967\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e \u003cp\u003e14.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInteraction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e-0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e.894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e \u003cp\u003e3.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRandom effect\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\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 \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParticipant (var.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.92\u003c/p\u003e \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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e1.98\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=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e1.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTarget (var.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.02\u003c/p\u003e \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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.01\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=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.40\u003c/p\u003e \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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.42\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=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRegression of participant and target personality intensity and their interaction on liking.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"20\"\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=\"left\" 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=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c18\" colnum=\"18\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c19\" colnum=\"19\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c20\" colnum=\"20\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFixed effect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eOpenness\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e \u003cp\u003eConscientiousness\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c15\" namest=\"c12\"\u003e \u003cp\u003eExtraversion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c20\" namest=\"c17\"\u003e \u003cp\u003eAgreeableness\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eβ\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eβ\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cem\u003eβ\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c17\"\u003e \u003cp\u003e\u003cem\u003eβ\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c18\"\u003e \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c19\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c20\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParticipant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.639\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e.233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e \u003cp\u003e4.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTarget\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e9.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e-0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e.970\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e \u003cp\u003e13.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInteraction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e7.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e1.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e \u003cp\u003e3.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRandom effect\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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\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 \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParticipant (var.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.14\u003c/p\u003e \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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e2.60\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=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e1.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTarget (var.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.05\u003c/p\u003e \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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.05\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=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.93\u003c/p\u003e \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 \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.82\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=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c20\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRegression of participant and target personality intensity and their interaction, as well as liking, on willingness to help.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\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=\"left\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFixed effect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eConscientiousness\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e \u003cp\u003eAgreeableness\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eβ\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eβ\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParticipant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTarget\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInteraction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.211\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.45\u003c/p\u003e \u003c/td\u003e \u003ctd 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Science","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"partner choice, positive assortment, biological markets, competitive altruism, agreeableness, conscientiousness","lastPublishedDoi":"10.21203/rs.3.rs-9529601/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9529601/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCooperation among non-kin requires mechanisms that allow individuals to preferentially direct costly help toward reliable partners. From an evolutionary perspective, positive assortment can be achieved through partner choice based on cues that signal cooperative value. This study tests whether personality traits function as such cues, guiding helping decisions under uncertainty. Building on theories of competitive altruism and biological markets, we focus on agreeableness and conscientiousness as signals of cooperative intent and reliability. A sample of 120 Japanese adults evaluated 40 experimentally constructed personality profiles varying in Big Five traits (excluding neuroticism) and rated their willingness to help and their liking for each target. Linear mixed-effects models showed that openness and extraversion did not predict helping. In contrast, agreeableness and conscientiousness significantly increased helping toward targets, and interaction effects revealed assortative helping based on trait similarity. Mediation analyses indicated that these effects were fully explained by liking, suggesting that trait-based partner choice operates through evolved evaluative mechanisms. These findings support the view that personality traits serve as adaptive social signals that facilitate partner choice and promote the evolutionary stability of cooperation.\u003c/p\u003e","manuscriptTitle":"Personality-based assortment in helping intentions: Evidence for partner choice using trait cues","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-08 08:22:19","doi":"10.21203/rs.3.rs-9529601/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"153634039462152858468782725492298021485","date":"2026-05-01T17:56:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"256231287214985543220394079141372761772","date":"2026-04-30T13:56:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"235992850041007716174734318856460857005","date":"2026-04-30T10:45:55+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-30T10:12:55+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-30T07:09:55+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-30T07:09:22+00:00","index":"","fulltext":""},{"type":"submitted","content":"Evolutionary Psychological Science","date":"2026-04-26T05:48:53+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"evolutionary-psychological-science","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"evop","sideBox":"Learn more about [Evolutionary Psychological Science](http://link.springer.com/journal/40806)","snPcode":"40806","submissionUrl":"https://submission.springernature.com/new-submission/40806/3","title":"Evolutionary Psychological Science","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"5c64a6a2-28b3-46f7-91af-7d5259f7e1e4","owner":[],"postedDate":"May 8th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"153634039462152858468782725492298021485","date":"2026-05-01T17:56:48+00:00","index":16,"fulltext":""},{"type":"reviewerAgreed","content":"256231287214985543220394079141372761772","date":"2026-04-30T13:56:17+00:00","index":14,"fulltext":""},{"type":"reviewerAgreed","content":"235992850041007716174734318856460857005","date":"2026-04-30T10:45:55+00:00","index":12,"fulltext":""},{"type":"reviewersInvited","content":"6","date":"2026-04-30T10:12:55+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-30T07:09:55+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-30T07:09:22+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-08T08:22:19+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-08 08:22:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9529601","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9529601","identity":"rs-9529601","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}