Not All Risks Are Equal: Female Preferences for Male Risk-Taking as a Function of Domain and Social Norms

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Grueter, Bradley Walker, David Coall, Nicolas Fay This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7919701/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Male risk-taking is often considered an attractive trait in the context of short-term mating, as it may signal underlying qualities such as strength, confidence, or genetic fitness. However, little is known about how the domain of risk-taking (e.g., health vs. financial risk-taking) affects perceived attractiveness, or whether conformity to social norms mediates this perception. Using an online questionnaire, we collected attractiveness ratings from 1,000 heterosexual female participants for various male behaviors representing different domains of risk-taking (ethical, financial, health, recreational, and social). Results show that health-related risk-taking was rated as the most attractive in short-term relationship contexts, whereas financial risk-taking was consistently rated the least attractive. Importantly, risk-taking that conformed to social norms was viewed more positively across all domains and both relationship contexts, suggesting that norm adherence plays a key role in shaping female preferences. These findings highlight that the appeal of risk-taking is not uniform; rather, it is contingent on both the domain of risk and the normative context in which the behavior is embedded. Risk-taking behavior mate preferences social norms domain-specific risk attractiveness sexual selection Figures Figure 1 Introduction Risk-taking is a sexually dimorphic trait, with consistent evidence indicating that males are more predisposed to engage in risky behavior than females (Byrnes, Miller, & Schafer, 1999 ; Charness & Gneezy, 2012 ). This sex difference has been documented across diverse domains, including financial decisions (e.g., gambling), and high-adrenaline activities (e.g., extreme sports and reckless driving) (Greitemeyer, Kastenmüller, & Fischer, 2013 ). Males tend to take more risks because their reproductive success is both more variable and more dependent on outcompeting rivals (Trivers, 1972 ; Wilson & Daly, 1985 ). In contrast, female reproductive success hinges on selecting high-quality mates. From a sexual selection perspective, non-lethal risk-taking may function as a signal of genetic or intrinsic quality to potential partners (Pawlowski, Atwal, & Dunbar, 2008 ; Wilke, Hutchinson, Todd, & Kruger, 2006 ; Wilson & Daly, 1985 ). Females therefore find risk-takers desirable as mates (Wilke et al., 2006 ), but mainly for short-term liaisons (Apalkova et al., 2018 ; Bassett & Moss, 2004 ; Grueter, Goodman, Fay, Walker, & Coall, 2023 ; Kelly & Dunbar, 2001 ; Sylwester & Pawłowski, 2011 ). In support of its sexual signalling function, males are more likely to display risk-taking behavior if a female is present (McAlvanah, 2009 ; Pawlowski et al., 2008 ; but see Goodman, Grueter, & Coall, 2024 ), and even more so if she is romantically available (Baker Jr & Maner, 2009 ), perhaps because the odds of gaining reproductive benefits will be greater. By contrast, risk-averse males are considered more attractive as long-term partners (Sylwester & Pawłowski, 2011 ). In long-term relationships, where both partners share reproductive goals and invest heavily in raising offspring, risk-taking tends to be unattractive to both men and women. Supporting this, research has found that individuals who frequently engage in risk-taking behaviors report a lower desire to marry (Willoughby & Dworkin, 2009 ). Overall, general risk-taking attitudes, as seen in males today, appear to be a manifestation of female mate choice. While there is a consensus that risk-taking is a sexually selected trait that enhances male attractiveness, empirical findings reveal important discrepancies. A generalized view of risk-taking has been increasingly challenged, with critics emphasizing the need to distinguish between different types or domains of risk (Hanoch, Johnson, & Wilke, 2006 ; Weber, Blais, & Betz, 2002 ). Given that women tend to find risk-taking males more attractive, particularly in short-term mating contexts, one might expect a consistent pattern of androcentrism in risk-taking, especially among males in their reproductive prime, for whom the potential reproductive payoffs of increased mate access are greatest (Wilson & Daly, 1985 ). However, Weber et al. ( 2002 ) demonstrated that this sex difference in risk-taking behavior holds across only four of five examined domains (ethical, health, financial, and recreational, but not social), which highlights the domain-specific nature of risk-taking (see also Kruger, Wang, & Wilke, 2007 ; Wilke et al., 2014 ). Attractiveness judgments also vary by domain: risk-taking tends to enhance male attractiveness in physical and social domains, but not in financial contexts (Sylwester & Pawłowski, 2011 ; Wilke et al., 2006 ). Physical risk-taking, in particular, may serve as an honest signal of traits related to physical condition and mate value, such as strength, athleticism, and vitality (Farthing, 2005 ), and can be perceived as indicative of genetic quality (Kelly & Dunbar, 2001 ; Sylwester & Pawłowski, 2011 ). Supporting this interpretation, Fessler, Tiokhin, Holbrook, Gervais, & Snyder ( 2014 ) found that men who voluntarily engaged in physical risks were perceived as more formidable than those who were risk-averse, which underscores the social and evolutionary relevance of risk behavior in specific domains. An important but understudied dimension of risk-taking in relation to attractiveness is whether the behavior conforms to or violates prevailing social norms . Social norms, i.e., culturally transmitted rules and expectations, play a powerful role in shaping human behavior and have likely exerted significant evolutionary pressure on our psychology (Cialdini & Goldstein, 2004 ). According to Chudek & Henrich ( 2011 ), humans possess a "norm psychology," described as a suite of psychological adaptations for detecting, internalizing, adhering to, and enforcing the shared behavioral standards of one’s community. Individuals who violate these norms are perceived less favorably, especially when the norm in question is salient (Wenegrat, Abrams, Castillo-Yee, & Romine, 1996 ). Given the importance of social cohesion and reputation in human social life, it stands to reason that norm adherence may also influence perceptions of interpersonal attractiveness. Specifically, risk-taking behaviors that violate social norms may be seen as socially undesirable and thus reduce perceived attractiveness. Supporting this, Petraitis, Lampman, Boeckmann, & Falconer ( 2014 ) found that contemporary norm-violating risk behaviors such as media piracy or not wearing a seatbelt were rated as unattractive by males and females. In contrast, risk-taking associated with evolutionarily relevant challenges, such as handling fire or confronting dangerous animals, was rated as particularly attractive when performed by men. These findings suggest the attractiveness of risk-taking is context-dependent and moderated by both domain relevance and normative conformity. However, to our knowledge, no previous study has systematically examined the attractiveness of norm-conforming versus norm-violating risk-taking across multiple domains. Taken together, prior work on domain-specific risk-taking and the role of normative conformity suggests that different forms of risk-taking should vary systematically in their perceived attractiveness across relationship contexts. Based on this work, we expected financial risk-taking to be rated as relatively unattractive, particularly in long-term contexts, whereas physical and health-related risk-taking were expected to be more attractive in short-term contexts. For other domains, including ethical and social risk-taking, we adopted a more exploratory approach given mixed theoretical predictions. Accordingly, the current study tests three main predictions. First, female participants are expected to rate male risk-taking as more attractive in short-term than in long-term relationship contexts. Second, risk-taking behaviors that violate social norms will, on average, be rated as less attractive than norm-conforming behaviors within the same domain and relationship context. Third, the magnitude and direction of these effects are expected to vary across risk domains, reflecting domain-specific theoretical expectations. The study hypotheses were not preregistered. Methods Vignettes To assess the domain specificity of risk-taking attractiveness, we used the DOSPERT framework (Weber et al., 2002 ) as a conceptual guide to generate domain-labeled descriptions of male risk-taking across five domains (ethical, financial, health, recreational, and social). The vignettes were not intended to replicate DOSPERT items directly, but to represent broader classes of risk-related outcomes within each domain. Each domain was further divided into norm-conforming and norm-violating variants, resulting in ten distinct vignettes depicting different types of male risk-taking behavior (Table 1 ). Importantly, norm adherence (conforming vs. violating) was manipulated orthogonally and systematically across all domains, allowing us to examine the independent and interactive effects of risk domain and norm violation on perceived attractiveness. Norm-violating behavior was defined as any act that infringes on formal or informal social norms (Van Kleef, Homan, Finkenauer, Gündemir, & Stamkou, 2011 ); in this study, norm adherence was operationalized through legality, with norm-violating behaviors involving illegal or antisocial actions (e.g., not paying for a meal or driving over the speed limit). We excluded heroic acts from the design, as they contain an altruistic component (e.g., saving someone from a burning building), which could confound interpretations by making it unclear whether attractiveness is driven by the risk-taking itself or by altruism. Table 1 Ten vignettes depicting male risk-taking across five domains (ethical, financial, health, recreational, social), each presented in a norm-conforming and norm-violating variant to assess domain-specific attractiveness. Risk-taking domain Norm-conforming Norm-violating Ethical Adrian reveals that he works as a lawyer and sometimes has to defend people whom he knows are guilty. Adrian suggests that you both run away after your meal without paying since you both did not fancy the taste much. Financial During dinner, you both start talking about investing in bitcoin when Adrian reveals he loves investing in risky stock as it feels extremely rewarding when it does well. Adrian reveals that he often gives money to a friend to invest in a sport betting scheme, but he asks you not to tell anyone as it is not necessarily legal. Health In conversation, you discover that Adrian has had a headache for a few days but he does not think he needs to get it checked as it will pass. After dinner, Adrian drives you to dinner and notice him over speeding even whilst turning. Recreational When discussing weekend plans, Adrian tells you he loves trekking and is going to a high-risk 28 km trek down south this weekend. When discussing guilty pleasures, Adrian reveals he often goes to underground fights as its more rewarding than boxing in the gym – until the police raids it! Social Talking about social lives, Adrian reveals how he does not mind letting go of friends if the connection is not deep enough. He would rather be alone than have a large group of superficial friends as a safety net. Talking about public nudity, Adrian says “I still don’t understand how public nudity makes people so upset. We live in a free country!” Focus groups All vignettes (Table 1 ) intended for the questionnaire were evaluated for content validity through an iterative focus-group piloting process to ensure they accurately captured the intended risk-taking behaviors and their subtypes (i.e., domain and norm adherence). In total, 25 participants from the general public were involved across multiple small focus-group sessions conducted during vignette development. Participants were recruited via personal and professional networks and had no formal affiliation with the behavioral sciences. No detailed demographic data beyond general background were collected for focus-group participants, as the purpose of this stage was qualitative refinement rather than hypothesis testing. Participants were provided with definitions of each behavior type to ensure a shared baseline understanding. For each vignette, participants identified the behavioral trait represented with respect to both risk domain and norm-adherence status, and rated its clarity on a 5-point Likert scale (1 = very unclear, 5 = very clear). This procedure allowed us to verify that domain classification and norm adherence were reliably perceived as distinct dimensions. Vignettes with a mean clarity score below 4, or those misidentified by three or more participants, were revised and reassessed in subsequent focus-group sessions. In addition to quantitative ratings, participants provided qualitative feedback via open-ended response forms, which informed minor wording adjustments. The vignette development process was not preregistered and did not involve formal coding rules beyond these predefined clarity and classification criteria. Questionnaire layout The final questionnaire was developed in Qualtrics and consisted of two sections. Section I collected demographic information, including age, country of residence, and sexual orientation, as well as an attention check repeated at the end of the survey ("How old were you when you went on your first date?"). Consistency checks such as these are effective in validating response reliability and typically yield low failure rates. Section II presented participants with a hypothetical dating scenario in which they rated a male’s attractiveness based on specific behaviors exhibited on a first date. Attractiveness was rated on a 5-point Likert scale (1 = very unattractive to 5 = very attractive). Participants responded to 10 vignettes, each presented in two contexts: Part A (short-term relationship) and Part B (long-term relationship). Participants responded to 10 vignettes, each corresponding to a different combination of risk domain and norm adherence. Each vignette was evaluated in two relationship contexts: Part A (short-term relationship) and Part B (long-term relationship). In the short-term condition, participants were instructed to evaluate the male target as a potential casual, non-committed partner, whereas in the long-term condition they evaluated him as a potential committed, enduring partner. The study employed a fully within-subjects design, with all participants evaluating all vignettes in both relationship contexts. Vignettes were presented in randomized order to control for potential sequence effects. For each vignette, participants first evaluated the behavior in a short-term mating context, followed by a long-term mating context; the order of relationship contexts was therefore not counterbalanced. Scenarios To address known limitations in vignette-based research – specifically the confounding effects of physical attractiveness, social desirability, and resource level – this study standardized these variables. In Section II, participants were introduced to a hypothetical male character, “Adrian,” who was described as attractive, earning AUD 119,000 (Australian dollars) annually, and popular among friends. This information was presented once at the beginning of the task and remained constant across all conditions. The vignettes themselves contained only the behavioral descriptions listed in Table 1 and differed exclusively with respect to risk domain and norm adherence. No other attributes varied across vignettes. Controlling these traits ensured that variation in attractiveness ratings could be attributed primarily to the behavioral cues (i.e., domain-specific risk-taking) rather than unrelated mate-value factors (see e.g. Rhodes, 2006 ). Data collection Simulation-based power estimation indicated that, to detect a difference in attractiveness ratings between short- and long-term relationships with an effect size of d = 0.2 (as per Sylwester & Pawłowski, 2011 ) in any specific risk domain, a sample size of 1,000 would be more than adequate (100% power). This sample size would also provide a strong chance of detecting smaller effects that may be present in some risk domains (88% power for d = 0.1). Participants were recruited via the Amazon Mechanical Turk (MTurk) platform, yielding a total sample of 1,000 respondents. The majority of participants were from the United States (n = 981), with additional respondents from the United Kingdom (n = 11), India (n = 3), Nigeria (n = 2), Canada (n = 1), South Africa (n = 1), and Russia (n = 1). Three eligibility criteria were applied: participants had to (1) identify as female, (2) be over 18 years of age, and (3) identify as heterosexual. These criteria were chosen to align with the study’s focus on heterosexual female perceptions of male attractiveness, framed within the context of sexual selection, where risk-taking behaviors are interpreted as potential mate advertisement strategies. Participants ranged in age from 18 to 100 years (M = 31.61, SD = 7.83). Demographic variables were self-reported. Data analysis Prior to finalizing the dataset, each of the 1,000 survey responses underwent rigorous quality checks. Duplicate entries (identified by repeated Amazon MTurk worker IDs) were excluded to ensure each response represented a unique individual. Responses that failed attention checks were also removed (n = 9). In addition, all open-ended responses were manually reviewed for potential AI-generated content. For example, elaborate responses such as “The mean age of dating in America is 17, but different families have different rules” to a straightforward question about the participant’s own first date were flagged and excluded (n = 6). Replacement responses were collected until 1,000 high-quality datasets were obtained (see Supplementary Information). Formal response-time exclusion criteria were not applied; data quality was instead assessed using the checks described above. All analyses were conducted in R version 4.4.0 (R Core Development Team, 2024) (see Supplementary Information for the analysis script). To analyze female-rated attractiveness of male risk-taking, we fitted a linear mixed-effects model (LMM) using the lmerTest package (Kuznetsova et al., 2017 ). A cumulative link mixed model (CLMM) is arguably more appropriate for the data, but the estimates are more difficult to interpret; we also fitted a CLMM, using the clmm function from the ordinal package (Christensen, 2019 ), and the results were comparable (see Supplementary Information). The dependent variable was the attractiveness rating of each risk-taking behavior – measured on a 5-point Likert scale (1 = very unattractive; 5 = very attractive) – in both short- and long-term mating contexts. This ordinal variable was treated as an ordered factor. The fixed effects included in the model were relationship context (short-term vs. long-term, with short-term as the reference category), risk domain (ethical, financial, health, recreational, and social, with ethical as the reference category), and norm adherence (norm-conforming vs. norm-violating, with norm-conforming as the reference category). In addition, we included all of the interactions between the fixed effects (relationship context by risk domain, relationship context by norm adherence, risk domain by norm adherence, relationship context by risk domain by norm adherence). We fitted the most maximal random effect structure that would converge, which included random intercepts for participant and by-participant random slopes for norm adherence. To interpret the fixed effects, we focused on pairwise contrasts using the estimated marginal means, calculated using the emmeans package in R (Lenth, 2023 ), and using Holm-Bonferroni correction for multiple comparisons. This allowed for comparisons of attractiveness ratings across risk domains, norm adherence levels, and relationship contexts, while accounting for random effects. Results The linear mixed-effects model converged successfully (maximum gradient < 0.001; Hessian positive definite). The random intercept variance was 0.22 and the random slope variance was 0.01 (correlation with intercept = − .49). Residual variance was 0.54. The intraclass correlation (ICC) was 0.29 in the norm-conforming condition and 0.25 in the norm-violating condition, indicating that 29% and 25% of the variance in attractiveness ratings was respectively attributable to between-participant differences. The results model examining the attractiveness of male risk-taking across different domains, relationship contexts and norm-adherence levels are presented in Table 2 . As most interaction effects were statistically significant, to interpret the fixed effects we focus on pairwise contrasts based on the estimated marginal means. Estimated marginal means for attractiveness ratings by risk domain, relationship context and norm-adherence level are visually illustrated in Fig. 1 . Table 2 The risk-taking model for the sexually selected attractiveness of risk-taking males across various risk domains, relationship contexts and norm-adherence levels. Fixed Effect Estimate 95% CI SE t p Intercept 3.86 [3.80, 3.91] 0.03 139.84 < .001 Relationship Context (Long-Term) −0.61 [− 0.68, − 0.55] 0.03 −18.67 < .001 Norm Adherence (Violating) −0.59 [− 0.65, − 0.52] 0.03 −17.73 < .001 Risk Domain (Financial) −1.08 [− 1.14, − 1.02] 0.03 −32.74 < .001 Risk Domain (Health) 0.16 [0.10, 0.23] 0.03 4.93 < .001 Risk Domain (Recreational) 0.04 [− 0.03, 0.11] 0.03 1.25 .213 Risk Domain (Social) −0.07 [− 0.14, − 0.01] 0.03 −2.22 .026 Relationship Context (Long-Term) × Norm Adherence (Violating) 0.14 [0.06, 0.24] 0.05 3.07 .002 Relationship Context (Long-Term) × Risk Domain (Financial) 0.08 [− 0.01, 0.16] 0.05 1.70 .089 Relationship Context (Long-Term) × Risk Domain (Health) 0.30 [0.21, 0.39] 0.05 6.38 < .001 Relationship Context (Long-Term) × Risk Domain (Recreational) −0.17 [− 0.26, − 0.08] 0.05 −3.59 < .001 Relationship Context (Long-Term) × Risk Domain (Social) 0.14 [0.04, 0.23] 0.05 2.92 .003 Norm Adherence (Violating) × Risk Domain (Financial) 0.20 [0.11, 0.29] 0.05 4.24 < .001 Norm Adherence (Violating) × Risk Domain (Health) 0.27 [0.18, 0.36] 0.05 5.76 < .001 Norm Adherence (Violating) × Risk Domain (Recreational) −0.44 [− 0.54, − 0.35] 0.05 −9.54 < .001 Norm Adherence (Violating) × Risk Domain (Social) 0.10 [0.01, 0.19] 0.05 2.13 .033 Relationship Context (Long-Term) × Norm Adherence (Violating) × Risk Domain (Financial) 0.14 [0.01, 0.26] 0.07 2.17 .030 Relationship Context (Long-Term) × Norm Adherence (Violating) × Risk Domain (Health) −0.29 [− 0.42, − 0.17] 0.07 −4.42 < .001 Relationship Context (Long-Term) × Norm Adherence (Violating) × Risk Domain (Recreational) 0.37 [0.24, 0.49] 0.07 5.58 < .001 Relationship Context (Long-Term) × Norm Adherence (Violating) × Risk Domain (Social) 0.19 [0.06, 0.32] 0.07 2.83 .005 Relationship context and risk domain Female respondents’ ratings of the attractiveness of male risk-taking varied across risk domain and relationship context. For example, health risk-taking was consistently rated as the most attractive domain, and financial as the least. To test whether risk-taking was rated as more attractive for short-term relationships than for long-term relationships across the risk domains, we examined the pairwise contrasts for short-term vs. long-term relationships in each domain (Table 3 ). As expected, across all five domains attractiveness ratings were significantly higher for risk-taking in short-term relationships than in long-term relationships. The size of the difference varied across domains, with ethical and recreational risk-taking showing the largest attractiveness difference between short-term and long-term relationships. This suggests that the domain of risk behavior moderates the attractiveness of male risk-taking. Table 3 Pairwise contrasts between relationship contexts (i.e., short-term vs. long-term) across risk domains, based on estimated marginal means. Risk Domain Estimate 95% CI SE Cohen’s d z p Ethical 0.54 [0.50, 0.59] 0.02 0.74 23.32 < .001 Financial 0.39 [0.35, 0.44] 0.02 0.53 16.85 < .001 Health 0.39 [0.35, 0.44] 0.02 0.53 16.81 < .001 Recreational 0.53 [0.48, 0.57] 0.02 0.72 22.61 < .001 Social 0.31 [0.27, 0.36] 0.02 0.43 13.48 < .001 Risk domain and norm adherence To test whether norm-conforming risk-taking was rated as more attractive than norm-violating risk-taking across the risk domains and relationship contexts, we examined the pairwise contrasts for norm-conforming vs. norm-violating risk behavior in each domain/context combination (Table 4 ). As hypothesised, risk-taking that involved norm violation was consistently rated as significantly less attractive. The size of the difference varied, with short-term relationships usually showing a stronger effect of norm adherence than long-term relationships (i.e., for all domains except health). The largest effect of norm adherence was for recreational risk-taking in short-term relationships. Although moderated by risk domain and relationship context, norm adherence overall appeared to enhance the attractiveness of male risk-taking. Note that the effect of norm adherence for financial risk-taking in long-term relationships (the weakest here) was not statistically significant in the CLMM, so this comparison should be treated with caution. Table 4 Pairwise contrasts between norm-adherence levels (i.e., norm-conforming vs. norm-violating) across risk domains and relationship contexts, based on estimated marginal means. Risk Domain/Relationship Context Estimate 95% CI SE Cohen’s d z p Ethical, Short-Term 0.59 [0.52, 0.65] 0.03 0.80 17.73 < .001 Ethical, Long-Term 0.44 [0.38, 0.51] 0.03 0.60 13.41 < .001 Financial, Short-Term 0.39 [0.33, 0.46] 0.03 0.53 11.78 < .001 Financial, Long-Term 0.10 [0.04, 0.17] 0.03 0.14 3.14 .002 Health, Short-Term 0.32 [0.25, 0.38] 0.03 0.43 9.63 < .001 Health, Long-Term 0.47 [0.40, 0.53] 0.03 0.64 14.10 < .001 Recreational, Short-Term 1.03 [0.97, 1.10] 0.03 1.40 31.13 < .001 Recreational, Long-Term 0.52 [0.46, 0.59] 0.03 0.71 15.73 < .001 Social, Short-Term 0.49 [0.42, 0.55] 0.03 0.66 14.74 < .001 Social, Long-Term 0.16 [0.09, 0.22] 0.03 0.22 4.80 < .001 Discussion We conducted an online survey in which 1,000 heterosexual females evaluated the attractiveness of male behaviors across several domains of risk-taking. The findings revealed that health-related risk-taking was perceived as the most appealing, while financial risk-taking was consistently rated as the least attractive. Additionally, risk-taking behaviors that aligned with prevailing social norms were generally viewed more favorably, regardless of domain. Domain specificity in the attractiveness of male risk-taking behaviors Our findings indicate that health-related risk-taking such as speeding or ignoring medical symptoms was rated as the most attractive form of male risk-taking, particularly in the context of short-term mating. This contrasts somewhat with previous studies. For instance, (Wilke et al., 2006) found that health-related risks such as smoking, excessive drinking, drug use, unprotected sex, and neglecting seat belt use were consistently perceived as unattractive. Similarly, (Farthing, 2005), who focused more narrowly on drug-related risks, reported a clear preference for partners who avoided heavy alcohol or drug consumption. One likely explanation for these discrepancies lies in the specific types of health risks represented in the vignettes. However, speeding – a behavior included in both our study and in Farthing (2005), where it was classified as a form of physical risk-taking – was rated as unattractive in his study but as mildly attractive in ours. Certain health-related risks may serve as signals of a male’s physical fitness and underlying genetic quality, traits that are particularly valued in short-term mating contexts (Buss & Schmitt, 1993; Zahavi, 1975). From an evolutionary perspective, such preferences are consistent with hypotheses about adaptive pressures in ancestral environments. In small-scale societies males who excel at a physically risky behavior, hunting, often receive social and reproductive benefits (Apicella, 2014; Smith, Bird, & Bird, 2003). Although health-related risk-taking was interpreted here primarily as a potential signal of physical fitness or genetic quality, alternative psychological mechanisms may also contribute to its attractiveness. For instance, some health-related risks may elicit caregiving or nurturing responses, particularly in short-term contexts, where concern for a partner’s vulnerability could increase perceived intimacy or emotional engagement. Distinguishing between attraction driven by mate-quality signaling and attraction driven by caretaker-oriented responses will be an important direction for future research. The attractiveness of recreational risk-taking , such as engaging in extreme sports or participating in underground fights, differed significantly between short- and long-term mating contexts, with these activities receiving higher attractiveness ratings in the former. Such behaviors may function as reliable signals of reproductive or survival advantages that are typically valued by females when selecting a short-term mate (Wilke et al., 2006). However, because high-risk recreational activities also increase the likelihood of serious injury or death (and thus reduce a male’s capacity to provide for and protect offspring), preferences for such behaviors would not be expected in the context of long-term relationships (Farthing, 2005; Wilke et al., 2006). Notably, Farthing (2005) found that physical risk-taking was perceived as most attractive when embedded within an altruistic or heroic narrative. Financial risks were consistently rated as the least attractive form of risk-taking, particularly in the context of long-term relationships. This finding aligns with (Wilke et al., 2006) domain-specific risk-taking framework, which suggests that financial risks are typically viewed as high-risk, low-reward behaviors. Unlike health or social risks, which may convey genetic fitness or social capital (Bliege Bird, Smith, & Bird, 2001), financial risk-taking is more likely to signal irresponsibility or instability, traits that undermine a male’s desirability as a long-term partner (Kruger et al., 2007). While financial risk-taking can, in some contexts, indicate ambition and potential for resource acquisition, our findings suggest that such risks more reliably signal negative attributes that conflict with female preferences for reliability and financial security in long-term mates. This preference for economic stability over financial risk may have deep evolutionary roots; for example, among Hadza hunter-gatherers, men who adopted consistent provisioning strategies were more successful in supporting families (Marlowe, 2010). Thus, although financial risk-taking might reflect ambition, its potential costs – particularly in terms of perceived mate value – appear to outweigh its benefits in long-term mating contexts. However, our vignette may not have captured the full spectrum of financial behaviors, such as calculated entrepreneurial risks, which could be perceived differently. Future research should incorporate a broader array of financial risk scenarios to assess whether some forms are seen as more attractive than others. Additionally, our findings indicate that financial risk-takers are perceived as unattractive not only in long-term but also in short-term relationship contexts. This supports the proposition that financial stability may serve as a proxy for genetic quality, as it reflects traits such as cognitive ability and foresight (see e.g. Dohmen, Falk, Huffman, & Sunde, 2018), factors that can influence an individual’s capacity for successful resource acquisition. From this perspective, irresponsible financial decisions may be perceived as signaling lower cognitive or planning abilities, which could reduce perceived mate value. This is particularly relevant in short-term mating contexts, where genetic inheritance is often prioritized over long-term provisioning potential. These findings open a promising avenue for future research, offering more nuanced insights into the role of financial decision-making in female mate preferences across different relationship contexts. Importantly, other forms of financial risk-taking may function differently in mating contexts. A substantial literature on costly signaling and conspicuous consumption demonstrates that visible, status-oriented financial expenditures can increase male attractiveness, particularly in short-term mating contexts (e.g., Griskevicius et al., 2007; Hennighausen et al., 2016; Sundie et al., 2011). Such behaviors may signal resource-holding potential or status rather than irresponsibility. The financial risk vignettes used in the present study focused on speculative or gambling-like investment decisions and therefore represent only a narrow subset of financial risk-taking. Accordingly, the present findings should not be generalized to financial risk-taking more broadly, and future research should distinguish between different forms of financial risk and their signaling functions. Social risk-taking such as expressing unpopular opinions or taking on leadership roles was evaluated positively overall, but its attractiveness differed between relationship contexts. Specifically, social risk-taking received higher attractiveness ratings in short-term mating contexts than in long-term contexts, where ratings were lower but remained generally positive. This form of risk-taking may function as a signal of social status or dominance, traits that are often valued by women in potential mates (Sadalla, Kenrick, & Vershure, 1987). Kruger et al. (2007) argue that contemporary male social risk-taking may stem from ancestral within-group competition, where individuals vied for prestige and resources. The modern preference for socially bold males, as observed in this study, may be interpreted as consistent with evolutionary accounts emphasizing the role of status acquisition in mating success. Exploring this evolutionary connection further could illuminate how traits once adaptive in ancestral environments continue to shape mating preferences today. Moreover, given that perceptions of social risk are culturally contingent, cross-cultural research is needed to determine the extent to which these mate preferences are universally expressed or culturally variable. For ethical risk-taking , we found a pronounced difference in perceived attractiveness between short-term and long-term mating contexts. Specifically, ethical risk-taking was rated as moderately attractive in short-term mates (as long as they were norm-conforming). These findings refine and extend those of Wilke et al. (2006), who reported that risk-taking in the ethical domain (where all examples carried a negative connotation, such as stealing or cheating) was consistently perceived as aversive. One possible explanation for our results is that the attractiveness in one of our vignettes may have been driven primarily by the status-related element of the scenario (“working as a lawyer”) rather than by the actual behavior of defending guilty clients. In other words, women may have been responding to the prestige and competence implied by the occupation, rather than to the ethically ambiguous aspect of the risk itself. Norm adherence and its influence on sexually selected attractiveness of risk-takers Our findings show that risk-taking involving norm violations was consistently rated as significantly less attractive. For recreational risk-taking, norm-conforming activities were perceived as significantly more attractive than norm-violating ones, especially in the context of short-term relationships. This suggests that female preferences for risk-taking are highly context-dependent, with a pronounced preference for behaviors that align with socially accepted norms. Recreational risks that conform to societal expectations such as participation in organized outdoor sports or physically demanding but sanctioned activities may signal desirable traits like physical fitness, discipline, and prosocial engagement. In contrast, norm-violating risks, such as illegal or overtly reckless activities, were rated significantly lower in attractiveness. This pattern likely reflects the perception that such behaviors could expose a mate to danger and signal traits such as impulsivity, poor judgment, and disregard for social norms, characteristics that are considered undesirable in both short- and long-term mating contexts. Engaging in physical risk-taking purely for thrill-seeking, especially when such behaviors contravene social norms, may therefore be perceived as unjustifiable or even self-serving, particularly when they fail to signal long-term benefits such as resource acquisition or social status (Farthing, 2005). Instead of bravery or competence, these behaviors may signal recklessness, undermining the male’s attractiveness. This interpretation is consistent with Blakemore & Mills' (2014) work on social heuristics, which suggests that people are especially attuned to the social acceptability of others’ behaviors. When male recreational risk-taking is socially endorsed, it may enhance attractiveness by demonstrating the ability to take calculated risks without jeopardizing social standing or future stability (Bliege Bird et al., 2001). Across both short- and long-term relationship contexts, females rated norm-conforming health risks as more attractive than norm-violating ones. This likely reflects the fact that norm-conforming health risks, like certain recreational risks, can signal mate-relevant traits such as physical competence which are particularly appealing in short-term mating contexts where displays of strength and vitality are prioritized (Buss & Schmitt, 1993; Farthing, 2005). Notably, these findings suggest that social competence and norm adherence can also enhance male attractiveness, even in short-term scenarios. The reduced appeal of norm-violating social risks may reflect how social identity and contextual cues influence female mate choice. Norm-conforming social risk-taking can signal leadership, prestige, and alignment with group norms, traits that are desirable in both short- and long-term relationships (Henrich & Gil-White, 2001). Such behaviors may indicate confidence, social intelligence, and the capacity to navigate hierarchies, i.e., qualities associated with partnership stability and resource access in long-term contexts, and social influence and excitement in short-term ones. These findings align with Henrich & Gil-White's (2001) prestige-based signaling theory, which posits that norm-conforming behaviors reflect responsibility and competence. By contrast, while norm-violating social risks might signal dominance or boldness, their attractiveness appears reduced due to associations with recklessness or disregard for social cohesion. In traditional societies, lower social status has often entailed reduced access to mates and resources (Von Rueden & Jaeggi, 2016), and modern aversion to norm-breaking may reflect these evolutionary pressures. However, norm-violating behaviors may still enhance a man’s status or attractiveness if they are perceived as serving group interests, which could offset the usual negative effect of breaking social norms on his desirability as a mate. While the present study found norm-violating social risks to be generally less attractive, future work should investigate whether their perceived mate value changes when such risks are motivated by prosocial or group-serving intentions. Limitations A limitation of this study concerns the operationalization of norm violation. By defining norm-violating behaviors primarily in legal terms, some vignettes may have conveyed not only norm nonconformity but also increased partner risk or reputational costs. As a result, reduced attractiveness may reflect aversion to the broader social and relational implications of norm-violating risk-taking rather than to norm violation alone. Future research should aim to disentangle norm adherence from perceived partner risk and social consequences. A related limitation concerns the broader construct validity of the vignette manipulations. Although focus-group piloting confirmed that vignettes were reliably categorized by risk domain and norm adherence, norm-violating scenarios in some domains also differed from norm-conforming versions in moral valence, illegality, emotional salience, or perceived severity. As a result, differences in attractiveness ratings cannot be attributed to norm adherence alone. Rather, the present findings reflect evaluations of norm-violating risk-taking as it is commonly encountered in everyday contexts, where norm violations are often bundled with moral and legal implications. This issue may be particularly salient in the health domain, where norm-violating behaviors (e.g., dangerous driving) are typically higher in intensity and personal relevance than norm-conforming behaviors (e.g., self-neglect), and therefore differences in attractiveness may reflect perceived partner danger or severity in addition to norm adherence. Future research should independently manipulate norm adherence, perceived risk intensity, and moral valence to more precisely isolate their effects. A further limitation is that each risk domain was represented by a small number of vignette scenarios rather than a broad set of items. As a result, the present findings cannot be generalized to all forms of risk-taking within a given domain, but instead reflect evaluations of the specific behaviors depicted. Because the study employed a fully within-subjects design in which participants evaluated all vignettes across two relationship contexts, substantially increasing the number of items per domain would have increased cognitive load and participant fatigue, potentially reducing response number and quality. Future research using between-subjects or mixed designs could incorporate a wider range of vignettes per domain to improve generalizability. In addition, future work could incorporate participant co-creation of vignette scenarios, direct assessments of perceived risk magnitude, and calibration of risk perceptions to contemporary norms to further strengthen construct validity. Another limitation is that manipulation checks were conducted during vignette development but not in the main sample. Although focus-group piloting confirmed that vignettes were reliably perceived as representing the intended risk domains and norm-adherence categories, we did not directly assess participants’ perceptions of riskiness or norm conformity during the attractiveness task itself. Future studies should incorporate independent manipulation checks alongside outcome measures to more fully validate vignette-based designs. In addition, we did not collect measures of participants’ health status or broader socioecological context, which have been shown to moderate female preferences for male risk-taking (Grueter et al., 2023). Future studies should incorporate these variables to assess how individual condition and context interact with domain-specific risk-taking and norm adherence. We also did not assess individual differences such as participants’ own risk attitudes or tendencies toward socially desirable responding, both of which may influence evaluations of partner attractiveness (Dohmen et al., 2011; Grueter et al., 2023; Paulhus, 1991). As a result, we were unable to determine whether such traits moderated preferences for male risk-taking across domains or levels of norm adherence. Future research should incorporate individual-difference measures to better disentangle dispositional influences from domain- and context-specific effects. An additional consideration concerns the within-subjects design, which required participants to evaluate multiple vignettes across domains and relationship contexts. While this approach increases statistical power and controls for individual differences in baseline preferences, it may also increase cognitive load and introduce carry-over, contrast, or demand effects. Although vignette order was randomized and multiple data-quality checks were implemented, future studies could employ between-subjects or mixed designs to further reduce these concerns. In addition, the order of relationship contexts was not counterbalanced, raising the possibility that order effects (e.g., anchoring or contrast effects) may have influenced attractiveness ratings. Finally, although we drew on the DOSPERT domain structure for conceptual guidance, our vignettes do not constitute a direct operationalization of the DOSPERT scale. Accordingly, our findings should be interpreted as reflecting evaluations of domain-labeled risk contexts under varying levels of norm adherence, rather than as tests of attractiveness differences between canonical DOSPERT items. Future directions We found that not all forms of risk-taking are equally attractive to females across different relationship contexts and levels of norm adherence. Nonetheless, males frequently engage in risky behaviors such as physical confrontations that do not align with female preferences (Wilson & Daly, 1985). This suggests that male risk-taking is not solely driven by mate attraction but may also be shaped by male-male competition (Baker Jr & Maner, 2008; Farthing, 2005; Salas-Rodríguez et al., 2022), and more proximately, by peer influence and ingrained gender norms that associate risk-taking with masculinity (Bem, 1974). Rather than functioning exclusively as a signal of mate value, risk-taking may serve to fulfil societal expectations or earn social approval from male peers. This is particularly evident during adolescence and early adulthood, when social heuristics, i.e. cognitive shortcuts based on peer behavior, are especially influential. Adolescent males, in particular, are prone to engaging in risky behaviors when influenced by peers (Gardner & Steinberg, 2005). Peer-driven forms of risk-taking, such as reckless driving or extreme physical stunts, may be unattractive to females yet remain prevalent among males. This disconnect between peer-influenced behaviors and female preferences highlights the need for further investigation into the interplay between different drivers of male risk-taking, including female choice, male-male competition, and social dynamics. Future research could also explore the influence of online environments, where mechanisms of social validation may promote risky behaviors that conflict with female preferences for norm-adherent traits. Another promising avenue for future research is the role of impression management in shaping female mate preferences. The relatively low attractiveness ratings assigned to norm-violating risk behaviors raise an important question: do women genuinely find norm-conforming risks more attractive, or are their responses influenced by broader societal norms dictating what should be considered attractive? To disentangle social desirability from genuine sexual attraction, future studies could employ implicit measures such as physiological responses to determine whether stated preferences align with subconscious attraction to different forms of risk-taking. Individuals who score higher on certain dimensions of risk may also be more inclined to prefer partners who exhibit risk-taking behaviors in those same domains. This assortative pattern may extend to the acceptance of norm-violating risk-taking. Moreover, an individual’s health status (in interaction with public health conditions) has been shown to influence preferences for male physical risk-taking (Grueter et al., 2023), a pattern that could plausibly generalize to other risk domains. Cultural context further shapes how risk-taking is perceived, as shown by Apalkova et al. (2018). In some cultures, risk-taking may signal dominance and strength, while in others, it is interpreted as impulsive or irresponsible. These differences highlight the role of societal values (particularly those related to masculinity and responsibility) in shaping both male risk-taking behaviors and female mate preferences, especially when norm adherence is taken into account. Although our sample lacked substantial cultural diversity, future cross-cultural research could investigate how cultural norms interact with evolved mate preferences to influence the attractiveness of different forms of risk-taking. Declarations Funding Sources This study was supported by the University of Western Australia. Ethics Approval and Consent to Participate Declaration This study was approved by the University of Western Australia’s Human Research Ethics Committee (approval number 2024/ET000421). Informed consent was obtained from all individual participants included in the study. Clinical trial number Not applicable. References Apalkova, Y., Butovskaya, M. L., Bronnikova, N., Burkova, V., Shackelford, T. K., & Fink, B. (2018). Assessment of male physical risk-taking behavior in a sample of Russian men and women. Evolutionary Psychological Science, 4 (3), 314-321. Apicella, C. L. (2014). Upper-body strength predicts hunting reputation and reproductive success in Hadza hunter–gatherers. Evolution and Human Behavior, 35 (6), 508-518. Baker Jr, M. D., & Maner, J. K. (2008). Risk-taking as a situationally sensitive male mating strategy. Evolution and Human Behavior, 29 (6), 391-395. Baker Jr, M. 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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-7919701","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":631177300,"identity":"35c5c607-7e45-4d37-956d-f6b8458ba9d5","order_by":0,"name":"Cyril C. Grueter","email":"data:image/png;base64,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","orcid":"","institution":"University of Oxford","correspondingAuthor":true,"prefix":"","firstName":"Cyril","middleName":"C.","lastName":"Grueter","suffix":""},{"id":631177301,"identity":"2fd9b8c0-4bd5-4c43-81cd-ebf49b7fc802","order_by":1,"name":"Bradley Walker","email":"","orcid":"","institution":"University of Western Australia","correspondingAuthor":false,"prefix":"","firstName":"Bradley","middleName":"","lastName":"Walker","suffix":""},{"id":631177302,"identity":"9ad24cef-594e-4762-8201-89df931536da","order_by":2,"name":"David Coall","email":"","orcid":"","institution":"Edith Cowan University","correspondingAuthor":false,"prefix":"","firstName":"David","middleName":"","lastName":"Coall","suffix":""},{"id":631177303,"identity":"3211a48f-b826-4d7f-9328-58b5765ee961","order_by":3,"name":"Nicolas Fay","email":"","orcid":"","institution":"University of Western Australia","correspondingAuthor":false,"prefix":"","firstName":"Nicolas","middleName":"","lastName":"Fay","suffix":""}],"badges":[],"createdAt":"2025-10-22 08:31:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7919701/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7919701/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108182022,"identity":"97e1c8dd-8be8-4a46-acc9-8ff0f7b65df7","added_by":"auto","created_at":"2026-04-30 08:59:05","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":75575,"visible":true,"origin":"","legend":"\u003cp\u003eEstimated marginal means of attractiveness ratings by risk domain, relationship context, and norm adherence. Each panel shows the result for a different risk domain. The y-axis represents the estimated marginal means of attractiveness ratings, while the x-axis shows the different norm adherence levels. Relationship context is indicated by colour. Error bars show bootstrapped 95% confidence intervals. Norm-conforming behaviors generally received higher attractiveness ratings than norm-violating behaviors, and attractiveness ratings were higher for short-term relationships than for long-term relationships. In general health was the most attractive risk domain, while financial was the least attractive.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7919701/v1/01ba3a3a5b21e43f1f8e62cc.png"},{"id":108803879,"identity":"74d461a5-0c0c-4baf-8c42-d01b093fec1d","added_by":"auto","created_at":"2026-05-08 15:09:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":518054,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7919701/v1/d7dd2b55-2dda-439f-8dd6-b88ff4aa9801.pdf"},{"id":108073251,"identity":"1a5c6136-116d-488e-95ff-d21f5003900e","added_by":"auto","created_at":"2026-04-29 06:18:41","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":15044,"visible":true,"origin":"","legend":"","description":"","filename":"Suppl.InformationDataDictionary.docx","url":"https://assets-eu.researchsquare.com/files/rs-7919701/v1/e5468fa50546b759a5e079bd.docx"},{"id":108182081,"identity":"d76f68e7-aefc-46f3-bb1e-652ddd39fca8","added_by":"auto","created_at":"2026-04-30 08:59:07","extension":"csv","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":677135,"visible":true,"origin":"","legend":"","description":"","filename":"Suppl.InformationData.csv","url":"https://assets-eu.researchsquare.com/files/rs-7919701/v1/bccb905e8ee7b5896dca5e4b.csv"},{"id":108182011,"identity":"21ade5b3-3c12-46ee-ab18-1589b4c0c3f5","added_by":"auto","created_at":"2026-04-30 08:59:04","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":9355783,"visible":true,"origin":"","legend":"","description":"","filename":"Suppl.InformationAnalysisscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7919701/v1/3c3b9af316798dfe03d6c919.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Not All Risks Are Equal: Female Preferences for Male Risk-Taking as a Function of Domain and Social Norms","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRisk-taking is a sexually dimorphic trait, with consistent evidence indicating that males are more predisposed to engage in risky behavior than females (Byrnes, Miller, \u0026amp; Schafer, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Charness \u0026amp; Gneezy, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). This sex difference has been documented across diverse domains, including financial decisions (e.g., gambling), and high-adrenaline activities (e.g., extreme sports and reckless driving) (Greitemeyer, Kastenm\u0026uuml;ller, \u0026amp; Fischer, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Males tend to take more risks because their reproductive success is both more variable and more dependent on outcompeting rivals (Trivers, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e1972\u003c/span\u003e; Wilson \u0026amp; Daly, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e1985\u003c/span\u003e). In contrast, female reproductive success hinges on selecting high-quality mates. From a sexual selection perspective, non-lethal risk-taking may function as a signal of genetic or intrinsic quality to potential partners (Pawlowski, Atwal, \u0026amp; Dunbar, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Wilke, Hutchinson, Todd, \u0026amp; Kruger, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Wilson \u0026amp; Daly, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e1985\u003c/span\u003e). Females therefore find risk-takers desirable as mates (Wilke et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), but mainly for short-term liaisons (Apalkova et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Bassett \u0026amp; Moss, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Grueter, Goodman, Fay, Walker, \u0026amp; Coall, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Kelly \u0026amp; Dunbar, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Sylwester \u0026amp; Pawłowski, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). In support of its sexual signalling function, males are more likely to display risk-taking behavior if a female is present (McAlvanah, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Pawlowski et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; but see Goodman, Grueter, \u0026amp; Coall, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and even more so if she is romantically available (Baker Jr \u0026amp; Maner, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), perhaps because the odds of gaining reproductive benefits will be greater. By contrast, risk-averse males are considered more attractive as long-term partners (Sylwester \u0026amp; Pawłowski, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). In long-term relationships, where both partners share reproductive goals and invest heavily in raising offspring, risk-taking tends to be unattractive to both men and women. Supporting this, research has found that individuals who frequently engage in risk-taking behaviors report a lower desire to marry (Willoughby \u0026amp; Dworkin, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Overall, general risk-taking attitudes, as seen in males today, appear to be a manifestation of female mate choice.\u003c/p\u003e \u003cp\u003eWhile there is a consensus that risk-taking is a sexually selected trait that enhances male attractiveness, empirical findings reveal important discrepancies. A generalized view of risk-taking has been increasingly challenged, with critics emphasizing the need to distinguish between different types or \u003cb\u003edomains of risk\u003c/b\u003e (Hanoch, Johnson, \u0026amp; Wilke, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Weber, Blais, \u0026amp; Betz, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Given that women tend to find risk-taking males more attractive, particularly in short-term mating contexts, one might expect a consistent pattern of androcentrism in risk-taking, especially among males in their reproductive prime, for whom the potential reproductive payoffs of increased mate access are greatest (Wilson \u0026amp; Daly, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e1985\u003c/span\u003e). However, Weber et al. (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) demonstrated that this sex difference in risk-taking behavior holds across only four of five examined domains (ethical, health, financial, and recreational, but not social), which highlights the domain-specific nature of risk-taking (see also Kruger, Wang, \u0026amp; Wilke, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Wilke et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAttractiveness judgments also vary by domain: risk-taking tends to enhance male attractiveness in physical and social domains, but not in financial contexts (Sylwester \u0026amp; Pawłowski, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Wilke et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Physical risk-taking, in particular, may serve as an honest signal of traits related to physical condition and mate value, such as strength, athleticism, and vitality (Farthing, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), and can be perceived as indicative of genetic quality (Kelly \u0026amp; Dunbar, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Sylwester \u0026amp; Pawłowski, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Supporting this interpretation, Fessler, Tiokhin, Holbrook, Gervais, \u0026amp; Snyder (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) found that men who voluntarily engaged in physical risks were perceived as more formidable than those who were risk-averse, which underscores the social and evolutionary relevance of risk behavior in specific domains.\u003c/p\u003e \u003cp\u003eAn important but understudied dimension of risk-taking in relation to attractiveness is whether the behavior conforms to or violates prevailing \u003cb\u003esocial norms\u003c/b\u003e. Social norms, i.e., culturally transmitted rules and expectations, play a powerful role in shaping human behavior and have likely exerted significant evolutionary pressure on our psychology (Cialdini \u0026amp; Goldstein, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). According to Chudek \u0026amp; Henrich (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), humans possess a \"norm psychology,\" described as a suite of psychological adaptations for detecting, internalizing, adhering to, and enforcing the shared behavioral standards of one\u0026rsquo;s community. Individuals who violate these norms are perceived less favorably, especially when the norm in question is salient (Wenegrat, Abrams, Castillo-Yee, \u0026amp; Romine, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). Given the importance of social cohesion and reputation in human social life, it stands to reason that norm adherence may also influence perceptions of interpersonal attractiveness. Specifically, risk-taking behaviors that violate social norms may be seen as socially undesirable and thus reduce perceived attractiveness. Supporting this, Petraitis, Lampman, Boeckmann, \u0026amp; Falconer (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) found that contemporary norm-violating risk behaviors such as media piracy or not wearing a seatbelt were rated as unattractive by males and females. In contrast, risk-taking associated with evolutionarily relevant challenges, such as handling fire or confronting dangerous animals, was rated as particularly attractive when performed by men. These findings suggest the attractiveness of risk-taking is context-dependent and moderated by both domain relevance and normative conformity. However, to our knowledge, no previous study has systematically examined the attractiveness of norm-conforming versus norm-violating risk-taking across multiple domains.\u003c/p\u003e \u003cp\u003eTaken together, prior work on domain-specific risk-taking and the role of normative conformity suggests that different forms of risk-taking should vary systematically in their perceived attractiveness across relationship contexts. Based on this work, we expected financial risk-taking to be rated as relatively unattractive, particularly in long-term contexts, whereas physical and health-related risk-taking were expected to be more attractive in short-term contexts. For other domains, including ethical and social risk-taking, we adopted a more exploratory approach given mixed theoretical predictions. Accordingly, the current study tests three main predictions. First, female participants are expected to rate male risk-taking as more attractive in short-term than in long-term relationship contexts. Second, risk-taking behaviors that violate social norms will, on average, be rated as less attractive than norm-conforming behaviors within the same domain and relationship context. Third, the magnitude and direction of these effects are expected to vary across risk domains, reflecting domain-specific theoretical expectations. The study hypotheses were not preregistered.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eVignettes\u003c/h2\u003e \u003cp\u003eTo assess the domain specificity of risk-taking attractiveness, we used the DOSPERT framework (Weber et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) as a conceptual guide to generate domain-labeled descriptions of male risk-taking across five domains (ethical, financial, health, recreational, and social). The vignettes were not intended to replicate DOSPERT items directly, but to represent broader classes of risk-related outcomes within each domain. Each domain was further divided into norm-conforming and norm-violating variants, resulting in ten distinct vignettes depicting different types of male risk-taking behavior (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Importantly, norm adherence (conforming vs. violating) was manipulated orthogonally and systematically across all domains, allowing us to examine the independent and interactive effects of risk domain and norm violation on perceived attractiveness.\u003c/p\u003e \u003cp\u003eNorm-violating behavior was defined as any act that infringes on formal or informal social norms (Van Kleef, Homan, Finkenauer, G\u0026uuml;ndemir, \u0026amp; Stamkou, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2011\u003c/span\u003e); in this study, norm adherence was operationalized through legality, with norm-violating behaviors involving illegal or antisocial actions (e.g., not paying for a meal or driving over the speed limit). We excluded heroic acts from the design, as they contain an altruistic component (e.g., saving someone from a burning building), which could confound interpretations by making it unclear whether attractiveness is driven by the risk-taking itself or by altruism.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTen vignettes depicting male risk-taking across five domains (ethical, financial, health, recreational, social), each presented in a norm-conforming and norm-violating variant to assess domain-specific attractiveness.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRisk-taking domain\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNorm-conforming\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNorm-violating\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdrian reveals that he works as a lawyer and sometimes has to defend people whom he knows are guilty.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdrian suggests that you both run away after your meal without paying since you both did not fancy the taste much.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFinancial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDuring dinner, you both start talking about investing in bitcoin when Adrian reveals he loves investing in risky stock as it feels extremely rewarding when it does well.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdrian reveals that he often gives money to a friend to invest in a sport betting scheme, but he asks you not to tell anyone as it is not necessarily legal.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIn conversation, you discover that Adrian has had a headache for a few days but he does not think he needs to get it checked as it will pass.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAfter dinner, Adrian drives you to dinner and notice him over speeding even whilst turning.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRecreational\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWhen discussing weekend plans, Adrian tells you he loves trekking and is going to a high-risk 28 km trek down south this weekend.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWhen discussing guilty pleasures, Adrian reveals he often goes to underground fights as its more rewarding than boxing in the gym \u0026ndash; until the police raids it!\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTalking about social lives, Adrian reveals how he does not mind letting go of friends if the connection is not deep enough. He would rather be alone than have a large group of superficial friends as a safety net.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTalking about public nudity, Adrian says \u0026ldquo;I still don\u0026rsquo;t understand how public nudity makes people so upset. We live in a free country!\u0026rdquo;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eFocus groups\u003c/h3\u003e\n\u003cp\u003eAll vignettes (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) intended for the questionnaire were evaluated for content validity through an iterative focus-group piloting process to ensure they accurately captured the intended risk-taking behaviors and their subtypes (i.e., domain and norm adherence). In total, 25 participants from the general public were involved across multiple small focus-group sessions conducted during vignette development. Participants were recruited via personal and professional networks and had no formal affiliation with the behavioral sciences. No detailed demographic data beyond general background were collected for focus-group participants, as the purpose of this stage was qualitative refinement rather than hypothesis testing.\u003c/p\u003e \u003cp\u003e Participants were provided with definitions of each behavior type to ensure a shared baseline understanding. For each vignette, participants identified the behavioral trait represented with respect to both risk domain and norm-adherence status, and rated its clarity on a 5-point Likert scale (1\u0026thinsp;=\u0026thinsp;very unclear, 5\u0026thinsp;=\u0026thinsp;very clear). This procedure allowed us to verify that domain classification and norm adherence were reliably perceived as distinct dimensions. Vignettes with a mean clarity score below 4, or those misidentified by three or more participants, were revised and reassessed in subsequent focus-group sessions. In addition to quantitative ratings, participants provided qualitative feedback via open-ended response forms, which informed minor wording adjustments. The vignette development process was not preregistered and did not involve formal coding rules beyond these predefined clarity and classification criteria.\u003c/p\u003e\n\u003ch3\u003eQuestionnaire layout\u003c/h3\u003e\n\u003cp\u003eThe final questionnaire was developed in Qualtrics and consisted of two sections. Section I collected demographic information, including age, country of residence, and sexual orientation, as well as an attention check repeated at the end of the survey (\"How old were you when you went on your first date?\"). Consistency checks such as these are effective in validating response reliability and typically yield low failure rates.\u003c/p\u003e \u003cp\u003eSection II presented participants with a hypothetical dating scenario in which they rated a male\u0026rsquo;s attractiveness based on specific behaviors exhibited on a first date. Attractiveness was rated on a 5-point Likert scale (1\u0026thinsp;=\u0026thinsp;very unattractive to 5\u0026thinsp;=\u0026thinsp;very attractive). Participants responded to 10 vignettes, each presented in two contexts: Part A (short-term relationship) and Part B (long-term relationship). Participants responded to 10 vignettes, each corresponding to a different combination of risk domain and norm adherence. Each vignette was evaluated in two relationship contexts: Part A (short-term relationship) and Part B (long-term relationship). In the short-term condition, participants were instructed to evaluate the male target as a potential casual, non-committed partner, whereas in the long-term condition they evaluated him as a potential committed, enduring partner.\u003c/p\u003e \u003cp\u003eThe study employed a fully within-subjects design, with all participants evaluating all vignettes in both relationship contexts. Vignettes were presented in randomized order to control for potential sequence effects. For each vignette, participants first evaluated the behavior in a short-term mating context, followed by a long-term mating context; the order of relationship contexts was therefore not counterbalanced.\u003c/p\u003e\n\u003ch3\u003eScenarios\u003c/h3\u003e\n\u003cp\u003eTo address known limitations in vignette-based research \u0026ndash; specifically the confounding effects of physical attractiveness, social desirability, and resource level \u0026ndash; this study standardized these variables. In Section II, participants were introduced to a hypothetical male character, \u0026ldquo;Adrian,\u0026rdquo; who was described as attractive, earning AUD 119,000 (Australian dollars) annually, and popular among friends. This information was presented once at the beginning of the task and remained constant across all conditions. The vignettes themselves contained only the behavioral descriptions listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and differed exclusively with respect to risk domain and norm adherence. No other attributes varied across vignettes. Controlling these traits ensured that variation in attractiveness ratings could be attributed primarily to the behavioral cues (i.e., domain-specific risk-taking) rather than unrelated mate-value factors (see e.g. Rhodes, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cp\u003eSimulation-based power estimation indicated that, to detect a difference in attractiveness ratings between short- and long-term relationships with an effect size of \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.2 (as per Sylwester \u0026amp; Pawłowski, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) in any specific risk domain, a sample size of 1,000 would be more than adequate (100% power). This sample size would also provide a strong chance of detecting smaller effects that may be present in some risk domains (88% power for \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.1). Participants were recruited via the Amazon Mechanical Turk (MTurk) platform, yielding a total sample of 1,000 respondents. The majority of participants were from the United States (n\u0026thinsp;=\u0026thinsp;981), with additional respondents from the United Kingdom (n\u0026thinsp;=\u0026thinsp;11), India (n\u0026thinsp;=\u0026thinsp;3), Nigeria (n\u0026thinsp;=\u0026thinsp;2), Canada (n\u0026thinsp;=\u0026thinsp;1), South Africa (n\u0026thinsp;=\u0026thinsp;1), and Russia (n\u0026thinsp;=\u0026thinsp;1). Three eligibility criteria were applied: participants had to (1) identify as female, (2) be over 18 years of age, and (3) identify as heterosexual. These criteria were chosen to align with the study\u0026rsquo;s focus on heterosexual female perceptions of male attractiveness, framed within the context of sexual selection, where risk-taking behaviors are interpreted as potential mate advertisement strategies. Participants ranged in age from 18 to 100 years (M\u0026thinsp;=\u0026thinsp;31.61, SD\u0026thinsp;=\u0026thinsp;7.83). Demographic variables were self-reported.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003ePrior to finalizing the dataset, each of the 1,000 survey responses underwent rigorous quality checks. Duplicate entries (identified by repeated Amazon MTurk worker IDs) were excluded to ensure each response represented a unique individual. Responses that failed attention checks were also removed (n\u0026thinsp;=\u0026thinsp;9). In addition, all open-ended responses were manually reviewed for potential AI-generated content. For example, elaborate responses such as \u003cem\u003e\u0026ldquo;The mean age of dating in America is 17, but different families have different rules\u0026rdquo;\u003c/em\u003e to a straightforward question about the participant\u0026rsquo;s own first date were flagged and excluded (n\u0026thinsp;=\u0026thinsp;6). Replacement responses were collected until 1,000 high-quality datasets were obtained (see Supplementary Information). Formal response-time exclusion criteria were not applied; data quality was instead assessed using the checks described above.\u003c/p\u003e \u003cp\u003eAll analyses were conducted in R version 4.4.0 (R Core Development Team, 2024) (see Supplementary Information for the analysis script). To analyze female-rated attractiveness of male risk-taking, we fitted a linear mixed-effects model (LMM) using the lmerTest package (Kuznetsova et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). A cumulative link mixed model (CLMM) is arguably more appropriate for the data, but the estimates are more difficult to interpret; we also fitted a CLMM, using the clmm function from the ordinal package (Christensen, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), and the results were comparable (see Supplementary Information). The dependent variable was the attractiveness rating of each risk-taking behavior \u0026ndash; measured on a 5-point Likert scale (1\u0026thinsp;=\u0026thinsp;very unattractive; 5\u0026thinsp;=\u0026thinsp;very attractive) \u0026ndash; in both short- and long-term mating contexts. This ordinal variable was treated as an ordered factor.\u003c/p\u003e \u003cp\u003e The fixed effects included in the model were relationship context (short-term vs. long-term, with short-term as the reference category), risk domain (ethical, financial, health, recreational, and social, with ethical as the reference category), and norm adherence (norm-conforming vs. norm-violating, with norm-conforming as the reference category). In addition, we included all of the interactions between the fixed effects (relationship context by risk domain, relationship context by norm adherence, risk domain by norm adherence, relationship context by risk domain by norm adherence). We fitted the most maximal random effect structure that would converge, which included random intercepts for participant and by-participant random slopes for norm adherence.\u003c/p\u003e \u003cp\u003eTo interpret the fixed effects, we focused on pairwise contrasts using the estimated marginal means, calculated using the emmeans package in R (Lenth, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and using Holm-Bonferroni correction for multiple comparisons. This allowed for comparisons of attractiveness ratings across risk domains, norm adherence levels, and relationship contexts, while accounting for random effects.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe linear mixed-effects model converged successfully (maximum gradient\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Hessian positive definite). The random intercept variance was 0.22 and the random slope variance was 0.01 (correlation with intercept\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.49). Residual variance was 0.54. The intraclass correlation (ICC) was 0.29 in the norm-conforming condition and 0.25 in the norm-violating condition, indicating that 29% and 25% of the variance in attractiveness ratings was respectively attributable to between-participant differences. The results model examining the attractiveness of male risk-taking across different domains, relationship contexts and norm-adherence levels are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. As most interaction effects were statistically significant, to interpret the fixed effects we focus on pairwise contrasts based on the estimated marginal means. Estimated marginal means for attractiveness ratings by risk domain, relationship context and norm-adherence level are visually illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\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\u003eThe risk-taking model for the sexually selected attractiveness of risk-taking males across various risk domains, relationship contexts and norm-adherence levels.\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=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFixed Effect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\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\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[3.80, 3.91]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e139.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRelationship Context (Long-Term)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[\u0026minus;\u0026thinsp;0.68, \u0026minus;\u0026thinsp;0.55]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;18.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorm Adherence (Violating)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[\u0026minus;\u0026thinsp;0.65, \u0026minus;\u0026thinsp;0.52]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;17.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRisk Domain (Financial)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[\u0026minus;\u0026thinsp;1.14, \u0026minus;\u0026thinsp;1.02]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;32.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRisk Domain (Health)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.10, 0.23]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRisk Domain (Recreational)\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\u003e[\u0026minus;\u0026thinsp;0.03, 0.11]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.213\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRisk Domain (Social)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[\u0026minus;\u0026thinsp;0.14, \u0026minus;\u0026thinsp;0.01]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;2.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRelationship Context (Long-Term) \u0026times; Norm Adherence (Violating)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.06, 0.24]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRelationship Context (Long-Term) \u0026times; Risk Domain (Financial)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[\u0026minus;\u0026thinsp;0.01, 0.16]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.089\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRelationship Context (Long-Term) \u0026times; Risk Domain (Health)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.21, 0.39]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRelationship Context (Long-Term) \u0026times; Risk Domain (Recreational)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[\u0026minus;\u0026thinsp;0.26, \u0026minus;\u0026thinsp;0.08]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;3.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRelationship Context (Long-Term) \u0026times; Risk Domain (Social)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.04, 0.23]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorm Adherence (Violating) \u0026times; Risk Domain (Financial)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.11, 0.29]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorm Adherence (Violating) \u0026times; Risk Domain (Health)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.18, 0.36]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorm Adherence (Violating) \u0026times; Risk Domain (Recreational)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[\u0026minus;\u0026thinsp;0.54, \u0026minus;\u0026thinsp;0.35]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;9.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorm Adherence (Violating) \u0026times; Risk Domain (Social)\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\u003e[0.01, 0.19]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRelationship Context (Long-Term) \u0026times; Norm Adherence (Violating) \u0026times; Risk Domain (Financial)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.01, 0.26]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.030\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRelationship Context (Long-Term) \u0026times; Norm Adherence (Violating) \u0026times; Risk Domain (Health)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[\u0026minus;\u0026thinsp;0.42, \u0026minus;\u0026thinsp;0.17]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;4.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRelationship Context (Long-Term) \u0026times; Norm Adherence (Violating) \u0026times; Risk Domain (Recreational)\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\u003e[0.24, 0.49]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRelationship Context (Long-Term) \u0026times; Norm Adherence (Violating) \u0026times; Risk Domain (Social)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.06, 0.32]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eRelationship context and risk domain\u003c/h3\u003e\n\u003cp\u003eFemale respondents\u0026rsquo; ratings of the attractiveness of male risk-taking varied across risk domain and relationship context. For example, health risk-taking was consistently rated as the most attractive domain, and financial as the least. To test whether risk-taking was rated as more attractive for short-term relationships than for long-term relationships across the risk domains, we examined the pairwise contrasts for short-term vs. long-term relationships in each domain (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). As expected, across all five domains attractiveness ratings were significantly higher for risk-taking in short-term relationships than in long-term relationships. The size of the difference varied across domains, with ethical and recreational risk-taking showing the largest attractiveness difference between short-term and long-term relationships. This suggests that the domain of risk behavior moderates the attractiveness of male risk-taking.\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\u003ePairwise contrasts between relationship contexts (i.e., short-term vs. long-term) across risk domains, based on estimated marginal means.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRisk Domain\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ez\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\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\u003eEthical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.50, 0.59]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e23.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFinancial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.35, 0.44]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.35, 0.44]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRecreational\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.48, 0.57]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e22.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.27, 0.36]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eRisk domain and norm adherence\u003c/h2\u003e \u003cp\u003eTo test whether norm-conforming risk-taking was rated as more attractive than norm-violating risk-taking across the risk domains and relationship contexts, we examined the pairwise contrasts for norm-conforming vs. norm-violating risk behavior in each domain/context combination (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). As hypothesised, risk-taking that involved norm violation was consistently rated as significantly less attractive. The size of the difference varied, with short-term relationships usually showing a stronger effect of norm adherence than long-term relationships (i.e., for all domains except health). The largest effect of norm adherence was for recreational risk-taking in short-term relationships. Although moderated by risk domain and relationship context, norm adherence overall appeared to enhance the attractiveness of male risk-taking. Note that the effect of norm adherence for financial risk-taking in long-term relationships (the weakest here) was not statistically significant in the CLMM, so this comparison should be treated with caution.\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\u003ePairwise contrasts between norm-adherence levels (i.e., norm-conforming vs. norm-violating) across risk domains and relationship contexts, based on estimated marginal means.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRisk Domain/Relationship Context\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ez\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\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\u003eEthical, Short-Term\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.52, 0.65]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e17.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthical, Long-Term\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.38, 0.51]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFinancial, Short-Term\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.33, 0.46]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFinancial, Long-Term\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\u003e[0.04, 0.17]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth, Short-Term\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.25, 0.38]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e 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\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.46, 0.59]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial, Short-Term\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.42, 0.55]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial, Long-Term\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.09, 0.22]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe conducted an online survey in which 1,000 heterosexual females evaluated the attractiveness of male behaviors across several domains of risk-taking. The findings revealed that health-related risk-taking was perceived as the most appealing, while financial risk-taking was consistently rated as the least attractive. Additionally, risk-taking behaviors that aligned with prevailing social norms were generally viewed more favorably, regardless of domain.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDomain specificity in the attractiveness of male risk-taking behaviors\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eOur findings indicate that \u003cstrong\u003ehealth-related risk-taking\u003c/strong\u003e such as speeding or ignoring medical symptoms was rated as the most attractive form of male risk-taking, particularly in the context of short-term mating. This contrasts somewhat with previous studies. For instance, (Wilke et al., 2006) found that health-related risks such as smoking, excessive drinking, drug use, unprotected sex, and neglecting seat belt use were consistently perceived as unattractive. Similarly, (Farthing, 2005), who focused more narrowly on drug-related risks, reported a clear preference for partners who avoided heavy alcohol or drug consumption. One likely explanation for these discrepancies lies in the specific types of health risks represented in the vignettes. However, speeding \u0026ndash; a behavior included in both our study and in Farthing (2005), where it was classified as a form of physical risk-taking \u0026ndash; was rated as unattractive in his study but as mildly attractive in ours.\u003c/p\u003e\n\u003cp\u003eCertain health-related risks may serve as signals of a male\u0026rsquo;s physical fitness and underlying genetic quality, traits that are particularly valued in short-term mating contexts (Buss \u0026amp; Schmitt, 1993; Zahavi, 1975). From an evolutionary perspective, such preferences are consistent with hypotheses about adaptive pressures in ancestral environments. In small-scale societies males who excel at a physically risky behavior, hunting, often receive social and reproductive benefits (Apicella, 2014; Smith, Bird, \u0026amp; Bird, 2003).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAlthough health-related risk-taking was interpreted here primarily as a potential signal of physical fitness or genetic quality, alternative psychological mechanisms may also contribute to its attractiveness. For instance, some health-related risks may elicit caregiving or nurturing responses, particularly in short-term contexts, where concern for a partner\u0026rsquo;s vulnerability could increase perceived intimacy or emotional engagement. Distinguishing between attraction driven by mate-quality signaling and attraction driven by caretaker-oriented responses will be an important direction for future research.\u003c/p\u003e\n\u003cp\u003eThe attractiveness of \u003cstrong\u003erecreational risk-taking\u003c/strong\u003e, such as engaging in extreme sports or participating in underground fights, differed significantly between short- and long-term mating contexts, with these activities receiving higher attractiveness ratings in the former. Such behaviors may function as reliable signals of reproductive or survival advantages that are typically valued by females when selecting a short-term mate (Wilke et al., 2006). However, because high-risk recreational activities also increase the likelihood of serious injury or death (and thus reduce a male\u0026rsquo;s capacity to provide for and protect offspring), preferences for such behaviors would not be expected in the context of long-term relationships (Farthing, 2005; Wilke et al., 2006). Notably, Farthing (2005) found that physical risk-taking was perceived as most attractive when embedded within an altruistic or heroic narrative.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFinancial risks\u003c/strong\u003e were consistently rated as the least attractive form of risk-taking, particularly in the context of long-term relationships. This finding aligns with (Wilke et al., 2006) domain-specific risk-taking framework, which suggests that financial risks are typically viewed as high-risk, low-reward behaviors. Unlike health or social risks, which may convey genetic fitness or social capital (Bliege Bird, Smith, \u0026amp; Bird, 2001), financial risk-taking is more likely to signal irresponsibility or instability, traits that undermine a male\u0026rsquo;s desirability as a long-term partner (Kruger et al., 2007). While financial risk-taking can, in some contexts, indicate ambition and potential for resource acquisition, our findings suggest that such risks more reliably signal negative attributes that conflict with female preferences for reliability and financial security in long-term mates. This preference for economic stability over financial risk may have deep evolutionary roots; for example, among Hadza hunter-gatherers, men who adopted consistent provisioning strategies were more successful in supporting families (Marlowe, 2010). Thus, although financial risk-taking might reflect ambition, its potential costs \u0026ndash; particularly in terms of perceived mate value \u0026ndash; appear to outweigh its benefits in long-term mating contexts. However, our vignette may not have captured the full spectrum of financial behaviors, such as calculated entrepreneurial risks, which could be perceived differently. Future research should incorporate a broader array of financial risk scenarios to assess whether some forms are seen as more attractive than others.\u003c/p\u003e\n\u003cp\u003eAdditionally, our findings indicate that financial risk-takers are perceived as unattractive not only in long-term but also in short-term relationship contexts. This supports the proposition that financial stability may serve as a proxy for genetic quality, as it reflects traits such as cognitive ability and foresight (see e.g. Dohmen, Falk, Huffman, \u0026amp; Sunde, 2018), factors that can influence an individual\u0026rsquo;s capacity for successful resource acquisition. From this perspective, irresponsible financial decisions may be perceived as signaling lower cognitive or planning abilities, which could reduce perceived mate value. This is particularly relevant in short-term mating contexts, where genetic inheritance is often prioritized over long-term provisioning potential. These findings open a promising avenue for future research, offering more nuanced insights into the role of financial decision-making in female mate preferences across different relationship contexts.\u003c/p\u003e\n\u003cp\u003eImportantly, other forms of financial risk-taking may function differently in mating contexts. A substantial literature on costly signaling and conspicuous consumption demonstrates that visible, status-oriented financial expenditures can increase male attractiveness, particularly in short-term mating contexts (e.g., Griskevicius et al., 2007; Hennighausen et al., 2016; Sundie et al., 2011). Such behaviors may signal resource-holding potential or status rather than irresponsibility. The financial risk vignettes used in the present study focused on speculative or gambling-like investment decisions and therefore represent only a narrow subset of financial risk-taking. Accordingly, the present findings should not be generalized to financial risk-taking more broadly, and future research should distinguish between different forms of financial risk and their signaling functions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSocial risk-taking\u003c/strong\u003e such as expressing unpopular opinions or taking on leadership roles was evaluated positively overall, but its attractiveness differed between relationship contexts. Specifically, social risk-taking received higher attractiveness ratings in short-term mating contexts than in long-term contexts, where ratings were lower but remained generally positive. This form of risk-taking may function as a signal of social status or dominance, traits that are often valued by women in potential mates (Sadalla, Kenrick, \u0026amp; Vershure, 1987). Kruger et al. (2007) argue that contemporary male social risk-taking may stem from ancestral within-group competition, where individuals vied for prestige and resources. The modern preference for socially bold males, as observed in this study, may be interpreted as consistent with evolutionary accounts emphasizing the role of status acquisition in mating success. Exploring this evolutionary connection further could illuminate how traits once adaptive in ancestral environments continue to shape mating preferences today. Moreover, given that perceptions of social risk are culturally contingent, cross-cultural research is needed to determine the extent to which these mate preferences are universally expressed or culturally variable.\u003c/p\u003e\n\u003cp\u003eFor \u003cstrong\u003eethical risk-taking\u003c/strong\u003e, we found a pronounced difference in perceived attractiveness between short-term and long-term mating contexts. Specifically, ethical risk-taking was rated as moderately attractive in short-term mates\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e(as long as they were norm-conforming). These findings refine and extend those of Wilke et al. (2006), who reported that risk-taking in the ethical domain (where all examples carried a negative connotation, such as stealing or cheating) was consistently perceived as aversive. One possible explanation for our results is that the attractiveness in one of our vignettes may have been driven primarily by the status-related element of the scenario (\u0026ldquo;working as a lawyer\u0026rdquo;) rather than by the actual behavior of defending guilty clients. In other words, women may have been responding to the prestige and competence implied by the occupation, rather than to the ethically ambiguous aspect of the risk itself.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNorm adherence and its influence on sexually selected attractiveness of risk-takers\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eOur findings show that risk-taking involving norm violations was consistently rated as significantly less attractive. For recreational risk-taking, norm-conforming activities were perceived as significantly more attractive than norm-violating ones, especially in the context of short-term relationships. This suggests that female preferences for risk-taking are highly context-dependent, with a pronounced preference for behaviors that align with socially accepted norms. Recreational risks that conform to societal expectations such as participation in organized outdoor sports or physically demanding but sanctioned activities may signal desirable traits like physical fitness, discipline, and prosocial engagement. In contrast, norm-violating risks, such as illegal or overtly reckless activities, were rated significantly lower in attractiveness. This pattern likely reflects the perception that such behaviors could expose a mate to danger and signal traits such as impulsivity, poor judgment, and disregard for social norms, characteristics that are considered undesirable in both short- and long-term mating contexts.\u003c/p\u003e\n\u003cp\u003eEngaging in physical risk-taking purely for thrill-seeking, especially when such behaviors contravene social norms, may therefore be perceived as unjustifiable or even self-serving, particularly when they fail to signal long-term benefits such as resource acquisition or social status (Farthing, 2005). Instead of bravery or competence, these behaviors may signal recklessness, undermining the male\u0026rsquo;s attractiveness. This interpretation is consistent with Blakemore \u0026amp; Mills\u0026apos; (2014) work on social heuristics, which suggests that people are especially attuned to the social acceptability of others\u0026rsquo; behaviors. When male recreational risk-taking is socially endorsed, it may enhance attractiveness by demonstrating the ability to take calculated risks without jeopardizing social standing or future stability (Bliege Bird et al., 2001).\u003c/p\u003e\n\u003cp\u003eAcross both short- and long-term relationship contexts, females rated norm-conforming health risks as more attractive than norm-violating ones. This likely reflects the fact that norm-conforming health risks, like certain recreational risks, can signal mate-relevant traits such as physical competence which are particularly appealing in short-term mating contexts where displays of strength and vitality are prioritized (Buss \u0026amp; Schmitt, 1993; Farthing, 2005). Notably, these findings suggest that social competence and norm adherence can also enhance male attractiveness, even in short-term scenarios.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe reduced appeal of norm-violating social risks may reflect how social identity and contextual cues influence female mate choice. Norm-conforming social risk-taking can signal leadership, prestige, and alignment with group norms, traits that are desirable in both short- and long-term relationships (Henrich \u0026amp; Gil-White, 2001). Such behaviors may indicate confidence, social intelligence, and the capacity to navigate hierarchies, i.e., qualities associated with partnership stability and resource access in long-term contexts, and social influence and excitement in short-term ones. These findings align with Henrich \u0026amp; Gil-White\u0026apos;s (2001) prestige-based signaling theory, which posits that norm-conforming behaviors reflect responsibility and competence. By contrast, while norm-violating social risks might signal dominance or boldness, their attractiveness appears reduced due to associations with recklessness or disregard for social cohesion. In traditional societies, lower social status has often entailed reduced access to mates and resources (Von Rueden \u0026amp; Jaeggi, 2016), and modern aversion to norm-breaking may reflect these evolutionary pressures.\u003c/p\u003e\n\u003cp\u003eHowever, norm-violating behaviors may still enhance a man\u0026rsquo;s status or attractiveness if they are perceived as serving group interests, which could offset the usual negative effect of breaking social norms on his desirability as a mate. While the present study found norm-violating social risks to be generally less attractive, future work should investigate whether their perceived mate value changes when such risks are motivated by prosocial or group-serving intentions.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eLimitations\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eA limitation of this study concerns the operationalization of norm violation. By defining norm-violating behaviors primarily in legal terms, some vignettes may have conveyed not only norm nonconformity but also increased partner risk or reputational costs. As a result, reduced attractiveness may reflect aversion to the broader social and relational implications of norm-violating risk-taking rather than to norm violation alone. Future research should aim to disentangle norm adherence from perceived partner risk and social consequences.\u003c/p\u003e\n\u003cp\u003eA related limitation concerns the broader construct validity of the vignette manipulations. Although focus-group piloting confirmed that vignettes were reliably categorized by risk domain and norm adherence, norm-violating scenarios in some domains also differed from norm-conforming versions in moral valence, illegality, emotional salience, or perceived severity. As a result, differences in attractiveness ratings cannot be attributed to norm adherence alone. Rather, the present findings reflect evaluations of norm-violating risk-taking as it is commonly encountered in everyday contexts, where norm violations are often bundled with moral and legal implications. This issue may be particularly salient in the health domain, where norm-violating behaviors (e.g., dangerous driving) are typically higher in intensity and personal relevance than norm-conforming behaviors (e.g., self-neglect), and therefore differences in attractiveness may reflect perceived partner danger or severity in addition to norm adherence. Future research should independently manipulate norm adherence, perceived risk intensity, and moral valence to more precisely isolate their effects.\u003c/p\u003e\n\u003cp\u003eA further limitation is that each risk domain was represented by a small number of vignette scenarios rather than a broad set of items. As a result, the present findings cannot be generalized to all forms of risk-taking within a given domain, but instead reflect evaluations of the specific behaviors depicted. Because the study employed a fully within-subjects design in which participants evaluated all vignettes across two relationship contexts, substantially increasing the number of items per domain would have increased cognitive load and participant fatigue, potentially reducing response number and quality. Future research using between-subjects or mixed designs could incorporate a wider range of vignettes per domain to improve generalizability. In addition, future work could incorporate participant co-creation of vignette scenarios, direct assessments of perceived risk magnitude, and calibration of risk perceptions to contemporary norms to further strengthen construct validity.\u003c/p\u003e\n\u003cp\u003eAnother limitation is that manipulation checks were conducted during vignette development but not in the main sample. Although focus-group piloting confirmed that vignettes were reliably perceived as representing the intended risk domains and norm-adherence categories, we did not directly assess participants\u0026rsquo; perceptions of riskiness or norm conformity during the attractiveness task itself. Future studies should incorporate independent manipulation checks alongside outcome measures to more fully validate vignette-based designs.\u003c/p\u003e\n\u003cp\u003eIn addition, we did not collect measures of participants\u0026rsquo; health status or broader socioecological context, which have been shown to moderate female preferences for male risk-taking (Grueter et al., 2023). Future studies should incorporate these variables to assess how individual condition and context interact with domain-specific risk-taking and norm adherence.\u003c/p\u003e\n\u003cp\u003eWe also did not assess individual differences such as participants\u0026rsquo; own risk attitudes or tendencies toward socially desirable responding, both of which may influence evaluations of partner attractiveness (Dohmen et al., 2011; Grueter et al., 2023; Paulhus, 1991). As a result, we were unable to determine whether such traits moderated preferences for male risk-taking across domains or levels of norm adherence. Future research should incorporate individual-difference measures to better disentangle dispositional influences from domain- and context-specific effects.\u003c/p\u003e\n\u003cp\u003eAn additional consideration concerns the within-subjects design, which required participants to evaluate multiple vignettes across domains and relationship contexts. While this approach increases statistical power and controls for individual differences in baseline preferences, it may also increase cognitive load and introduce carry-over, contrast, or demand effects. Although vignette order was randomized and multiple data-quality checks were implemented, future studies could employ between-subjects or mixed designs to further reduce these concerns. In addition, the order of relationship contexts was not counterbalanced, raising the possibility that order effects (e.g., anchoring or contrast effects) may have influenced attractiveness ratings.\u003c/p\u003e\n\u003cp\u003eFinally, although we drew on the DOSPERT domain structure for conceptual guidance, our vignettes do not constitute a direct operationalization of the DOSPERT scale. Accordingly, our findings should be interpreted as reflecting evaluations of domain-labeled risk contexts under varying levels of norm adherence, rather than as tests of attractiveness differences between canonical DOSPERT items.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFuture directions\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe found that not all forms of risk-taking are equally attractive to females across different relationship contexts and levels of norm adherence. Nonetheless, males frequently engage in risky behaviors such as physical confrontations that do not align with female preferences (Wilson \u0026amp; Daly, 1985). This suggests that male risk-taking is not solely driven by mate attraction but may also be shaped by male-male competition (Baker Jr \u0026amp; Maner, 2008; Farthing, 2005; Salas-Rodr\u0026iacute;guez et al., 2022), and more proximately, by peer influence and ingrained gender norms that associate risk-taking with masculinity (Bem, 1974). Rather than functioning exclusively as a signal of mate value, risk-taking may serve to fulfil societal expectations or earn social approval from male peers. This is particularly evident during adolescence and early adulthood, when social heuristics, i.e. cognitive shortcuts based on peer behavior, are especially influential. Adolescent males, in particular, are prone to engaging in risky behaviors when influenced by peers (Gardner \u0026amp; Steinberg, 2005). Peer-driven forms of risk-taking, such as reckless driving or extreme physical stunts, may be unattractive to females yet remain prevalent among males. This disconnect between peer-influenced behaviors and female preferences highlights the need for further investigation into the interplay between different drivers of male risk-taking, including female choice, male-male competition, and social dynamics. Future research could also explore the influence of online environments, where mechanisms of social validation may promote risky behaviors that conflict with female preferences for norm-adherent traits.\u003c/p\u003e\n\u003cp\u003eAnother promising avenue for future research is the role of impression management in shaping female mate preferences. The relatively low attractiveness ratings assigned to norm-violating risk behaviors raise an important question: do women genuinely find norm-conforming risks more attractive, or are their responses influenced by broader societal norms dictating what should be considered attractive? To disentangle social desirability from genuine sexual attraction, future studies could employ implicit measures such as physiological responses to determine whether stated preferences align with subconscious attraction to different forms of risk-taking.\u003c/p\u003e\n\u003cp\u003eIndividuals who score higher on certain dimensions of risk may also be more inclined to prefer partners who exhibit risk-taking behaviors in those same domains. This assortative pattern may extend to the acceptance of norm-violating risk-taking. Moreover, an individual\u0026rsquo;s health status (in interaction with public health conditions) has been shown to influence preferences for male physical risk-taking (Grueter et al., 2023), a pattern that could plausibly generalize to other risk domains. Cultural context further shapes how risk-taking is perceived, as shown by Apalkova et al. (2018). In some cultures, risk-taking may signal dominance and strength, while in others, it is interpreted as impulsive or irresponsible. These differences highlight the role of societal values (particularly those related to masculinity and responsibility) in shaping both male risk-taking behaviors and female mate preferences, especially when norm adherence is taken into account. Although our sample lacked substantial cultural diversity, future cross-cultural research could investigate how cultural norms interact with evolved mate preferences to influence the attractiveness of different forms of risk-taking.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding Sources\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the University of Western Australia.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval and Consent to Participate Declaration\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the University of Western Australia’s Human Research Ethics Committee (approval number 2024/ET000421). Informed consent was obtained from all individual participants included in the study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eApalkova, Y., Butovskaya, M. L., Bronnikova, N., Burkova, V., Shackelford, T. K., \u0026amp; Fink, B. (2018). Assessment of male physical risk-taking behavior in a sample of Russian men and women. \u003cem\u003eEvolutionary Psychological Science, 4\u003c/em\u003e(3), 314-321.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eApicella, C. L. (2014). Upper-body strength predicts hunting reputation and reproductive success in Hadza hunter\u0026ndash;gatherers. \u003cem\u003eEvolution and Human Behavior, 35\u003c/em\u003e(6), 508-518.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eBaker Jr, M. D., \u0026amp; Maner, J. K. (2008). 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An evolutionary domain-specific risk scale. \u003cem\u003eEvolutionary Behavioral Sciences, 8\u003c/em\u003e(3), 123.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eWilloughby, B. J., \u0026amp; Dworkin, J. (2009). The relationships between emerging adults\u0026apos; expressed desire to marry and frequency of participation in risk-taking behaviors. \u003cem\u003eYouth \u0026amp; Society, 40\u003c/em\u003e(3), 426-450.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eWilson, M., \u0026amp; Daly, M. (1985). Competitiveness, risk taking, and violence: The young male syndrome. \u003cem\u003eEthology and Sociobiology, 6\u003c/em\u003e(1), 59-73.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eZahavi, A. (1975). Mate selection\u0026mdash;a selection for a handicap. \u003cem\u003eJournal of Theoretical Biology, 53\u003c/em\u003e, 205-214. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Risk-taking behavior, mate preferences, social norms, domain-specific risk, attractiveness, sexual selection ","lastPublishedDoi":"10.21203/rs.3.rs-7919701/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7919701/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMale risk-taking is often considered an attractive trait in the context of short-term mating, as it may signal underlying qualities such as strength, confidence, or genetic fitness. However, little is known about how the domain of risk-taking (e.g., health vs. financial risk-taking) affects perceived attractiveness, or whether conformity to social norms mediates this perception. Using an online questionnaire, we collected attractiveness ratings from 1,000 heterosexual female participants for various male behaviors representing different domains of risk-taking (ethical, financial, health, recreational, and social). Results show that health-related risk-taking was rated as the most attractive in short-term relationship contexts, whereas financial risk-taking was consistently rated the least attractive. Importantly, risk-taking that conformed to social norms was viewed more positively across all domains and both relationship contexts, suggesting that norm adherence plays a key role in shaping female preferences. These findings highlight that the appeal of risk-taking is not uniform; rather, it is contingent on both the domain of risk and the normative context in which the behavior is embedded.\u003c/p\u003e","manuscriptTitle":"Not All Risks Are Equal: Female Preferences for Male Risk-Taking as a Function of Domain and Social Norms","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-29 06:18:36","doi":"10.21203/rs.3.rs-7919701/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5f94058a-ef88-4126-b55e-06ade027a407","owner":[],"postedDate":"April 29th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-29T06:18:37+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-29 06:18:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7919701","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7919701","identity":"rs-7919701","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}