# Why honor heroes? The emergence of extreme altruistic behavior as a by-product of praisers' self-promotion

Jean-Louis Dessalles

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Jean-Louis Dessalles. Why honor heroes? The emergence of extreme altruistic behavior as a by-product of praisers’ self-promotion. Evolution and Human Behavior, 2025, 46 (1), pp.106656. 10.1016/j.evolhumbehav.2025.106656. hal-04920385

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# Why honor heroes? The emergence of extreme altruistic behavior as a by-product of praisers’ self-promotion

Jean-Louis Dessalles*

LTCI, Télcom Paris, Institut Polytechnique de Paris, 19 Place Marguerite Perey, F-91120 Palaiseau, France

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## ARTICLE INFO

### Keywords:

- Costly signaling

- Social signaling

- Heroes

- Praise

## ABSTRACT

Heroes are people who perform costly altruistic acts. Few people turn out to be heroes, but many spontaneously honor heroes by commenting, applauding, or enthusiastically celebrating their deeds. The existence of a praising audience leads individuals to compete to attract the crowd's admiration. The outcome is a winner-take-all situation in which only one or a few individuals engage in extreme altruistic behavior. The more difficult part is to explain the crowd's propensity to pay tribute from an individual fitness optimization perspective. The model proposed here shows how heroic behavior and its celebration by a large audience may emerge together. This situation is possible if admirers use public praise as a social signal to promote their own commitment to the values displayed by the hero.

The existence of individuals who risk their fitness and, at times, their lives to perform altruistic acts poses a challenge from a Darwinian perspective. Yet, this behavior becomes more comprehensible in the presence of an audience that rewards such acts with elevated social status and glory. In a world where language was not used to comment on the actions of others, there would be no glory. Individuals could at best gain the esteem of a few direct witnesses to their actions (Hawkes & Bliege Bird, 2002). Glory arises from the human tendency to comment on and praise the deeds of third parties. The present study aims to demonstrate, first, that extreme altruism can emerge in the presence of social praise and, second, that praise itself may function as a viable social signaling strategy. In summary, heroism and praise reinforce one another, serving as distinct yet complementary mechanisms for demonstrating social value. The following sections review a few studies on heroic behavior, with a focus on its relationship with praise, as well as research on praise and its potential role as a social signal. A social signaling model is then presented to explain the co-emergence of praising behavior and heroism, followed by its implementation in an agent-based simulation. The paper concludes with a discussion of the limitations and contributions of this research.

## 1. Why are there heroes?

High-risk or high-cost altruistic behavior is not uncommon among humans (Becker & Eagly, 2004; Kelly & Dunbar, 2001; Smirnov et al.,

2007). It concerns all genders (Banks, 2019; Basak, 2015; Becker & Eagly, 2004; Bloom, 2011; Mama & Okazawa-Rey, 2012), notwithstanding authors' frequent tendency to give priority to male heroism by focusing on warfare (Kelly & Dunbar, 2001; Rusch et al., 2015; Smirnov et al., 2007). Heroic behavior is generally restricted to prosocial acts that expose the actor to the risk of dying or suffering serious physical consequences (Becker & Eagly, 2004; Goethals & Allison, 2012; Rusch, 2022; Stenstrom & Curtis, 2012). More inclusive definitions include social heroism, in which the costs are less dramatic but are certain to occur, often over extended time frames (Franco et al., 2011), as when nurses were called heroes at the beginning of the COVID-19 pandemic (Einboden, 2020). Risk-taking may even be optional, as when athletes are honored as “sports heroes” (Bifulco & Tirino, 2018).

As a case of extreme altruism among nonrelatives, heroic behavior is problematic from a Darwinian perspective: heroes seem to favor the propagation of others' genes at the expense of their own. Though some authors invoke group selection (Johnson, 1996; Lukaszewski et al., 2016) or kin selection (Smirnov et al., 2007), most studies link the existence of heroes to the presence of an audience ready to admire them (e. g. Goode, 1978; Hahl & Zuckerman, 2014; Kafashan et al., 2014, 2016; Rusch et al., 2015). The systematic appraisal of heroic acts contributes to the development of a hero's fame (Hahl & Zuckerman, 2014) and, indirectly, leads to status hierarchies where heroes often occupy dominant positions (Garfield et al., 2019; Gould, 2002). The prestige that emerges from public approval confers significant advantages and

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: dessalles@telecom-paris.fr.</footnote>
privileges on heroes (Henrich & Gil-White, 2000) and, as will be examined below, may be sufficient to explain why some individuals choose to perform risky altruistic actions. The other, more challenging side of the question is why people choose to bestow prestige, status, and honor upon heroes in the first place.

## 2. Admirers' motives

Expressions of support for prominent figures are ubiquitous in human societies (Goethals & Allison, 2012; Klapp, 1949; Schrift, 2001). Their contemporary versions range from liking content on social media (Hess et al., 2022; Khan, 2017) to becoming activists or devoted fans (Chadborn et al., 2017; Théberge, 2006). Public praise can be manifested formally during celebrations (De Regt, 2018), but also in a more mundane form during daily conversations (Algoe & Haidt, 2009). Conversational praise seems to be ubiquitous among human cultures, including in hunter-gatherer societies (Wiessner, 2014).

It could be argued that people are drawn to others' feats merely because such actions are, by definition, extraordinary. Human conversation is primarily oriented toward unexpected events (Dessalles, 2016) and admirable actions are just one instance of surprising occurrence among many. However, comments on heroic feats are rarely limited to expressing surprise. On the one hand, most gossip about exploits is accompanied by appraisals, with a significant proportion of these being positive (Ben-Ze'ev & Goodman, 1994; Levin & Arluke, 1985; McDonald et al., 2007). On the other hand, heroic acts are not mere unexpected accomplishments like those found in the *Guinness Book of Records*. They are typically characterized by intentional risk and altruism, performed to earn public approval.

As noted by Kafashan et al. (Kafashan et al., 2016), overt judgments of heroism may involve marginal costs that, although small in comparison to the sacrifices made by heroes, are not always negligible. Public displays of support, such as participating in celebrations or defending heroes in discussions, require time and energy. In addition, granting elevated status and accompanying privileges to heroes can result in lesser status and reduced access to valuable resources for the judge. It may also put one's social capital at risk (e.g. supporting NSA whistleblower Edward Snowden may limit access to job opportunities). Mere positive gossip, by revealing one's opinions, may sometimes have negative consequences (but also positive ones, as suggested in the present study).

How does expressing admiration bring a return on investment? Marks of deference might be seen as a way for admirers to approach competent models and get some support in return (Gould, 2002) or learn from them (Henrich & Gil-White, 2000). However, ethnographic data do not provide strong support for the social learning component of the prestige model (Garfield et al., 2019). Moreover, admiration is often expressed toward unattainable targets that may not be aware of specific supporting acts (Bifulco & Tirino, 2018). Granting prestige and esteem could be a way of giving heroes control over collective decisions (Chapais, 2015; Garfield et al., 2019), e.g. by letting them punish uncooperative acts (Redhead et al., 2021). Esteem can also be withdrawn and heroes denigrated, as a way for supporters to keep control over them (Goode, 1978; Hahl & Zuckerman, 2014). Investing in praise to control who controls is unlikely to be worth the cost, though. As for any invocation of collective benefit, the return on marginal investment is divided by the number of supporters. Why spend time and energy singing the praises of some hero if the rest of the audience does it for us? Alternatively, the readiness to praise third parties could be understood as part of a reciprocal exchange of valuable information about capable individuals. However, reciprocal exchanges of valuable information are exposed to the problem of free-riding; it must rely on precise bookkeeping of the informational value of each contribution, which is not confirmed by observation (Dessalles, 2020). Furthermore, reducing praise to its mere informational content does not account for the emphasis and enthusiasm often displayed when it is expressed. The

present study offers a novel perspective, in which praise functions as a social signal.

## 3. Praise as a second-order signal

After Greta Thunberg rose to prominence in 2018, politicians sought to be photographed with her to demonstrate their ecological commitment (Murphy, 2021). Similarly, speaking positively about her during conversations became a means of signaling concern for climate change. For some authors, speaking well of prominent figures is a way of drawing their attention to oneself (Anderson et al., 2020; Gould, 2002). The central hypothesis of the present study is that the prime target of praise is not the “hero” but rather the immediate witnesses of the praising act (Fig. 1). As Tannen puts it: “Talking about someone who is not there is a way of establishing rapport with someone who is there.” (Tannen, 1990, p. 107). Observations show that positive gossip correlates with friendship ties among colleagues (Grosser, Lopez-Kidwell, Labianca, & Devillard, 2010) and children (Lansford et al., 2006). The primary motivation for displaying fan identity (Chadborn et al., 2017) or participating in patriotic celebrations (De Regt, 2018) is not entertainment, learning, or self-enhancement, but rather the pursuit of new friendships.

How is praise conducive to making and keeping friends? According to Tannen: “By agreeing about their evaluation of someone else, people reinforce their shared values and world views.” Individuals tend to affiliate with people who share their values. Social network algorithms favor this tendency by leveraging common ‘likes’ to make predictive recommendations about friendships (Agarwal & Bharadwaj, 2013). Values become apparent only when one performs actions in line with them. For example, a climate-conscious individual might choose to carpool and offset the carbon footprint of a trip. However, such actions are visible only when they are performed. This is where praise comes into play. Similar to Facebook’s ‘thumb-ups’, praise serves as a way to signal one’s values. For instance, supporting Greta Thunberg’s recent actions provides an opportunity to discuss one’s own contributions to climate efforts while remaining conversationally relevant (Dessalles, 2016).

From this perspective, praising is a social signal, i.e., a signal used to make friends. Since it is about feats which are social signals themselves (Kafashan et al., 2016), one may call it a *second-order signal*, a signal about a signal. Blame, as the mirror image of praise (Jordan et al., 2016; Lie-Panis & Dessalles, 2023), is also a second-order signal. At the collective level, blame leads to social signal leveling (Lie-Panis & Dessalles, 2023). The following model demonstrates how positive second-order

<figure><img src="image_3.png" /><figcaption>Fig. 1. The prime target of praise is not the “hero”.</figcaption></figure>
signals, in contrast, can lead to the opposite effect, where a few individuals distinguish themselves by displaying extreme signals.

## 4. Model

The model described here shows that a few individuals can benefit from adopting high-cost behavior within a population where praise is used to advertise one's social value. Consider a population of N agents that differ by one hidden characteristic *q* (e.g. altruism or bravery). They may advertise it by sending a costly signal *S*(*q*) (e.g. performing altruistic acts or risky feats). For the sake of simplicity, characteristics *q* are supposed to be uniformly distributed over the range [0,1]. Signal cost *C*(*S*, *q*) is supposed to increase with *S* (its partial derivative *C*₁ is positive) and to decrease with *q* (*C*₂ < 0). With the latter assumption, competence *q* appears as the readiness/ability to emit the costly signal. Let's call it competence. In addition to the signaling strategy *S*(*q*), agents can choose whether to praise a top signaler or remain silent.

Agents meet systematically in pairs for two rounds. During the observation round, agents see each other's signal with probability $p_1 \le 1$. This limited visibility reflects the fact that the opportunities to demonstrate the signal (e.g. through actions) are few and far between, with only a few witnesses. The restriction to dyadic encounters is intended to prevent individuals from observing everyone at once, though it could be partially relaxed. During the communication round (Fig. 1), agents can choose to praise the best signaler they encountered during the first round (referred to as their “hero”), incurring a cost $c_p$. Praising makes the hero’s signal visible to the potential friend (no deception). Additionally, the potential friend will also observe the admirer’s signal with probability $p_p$. This reflects the idea that the admirer has an opportunity to draw attention to their own actions or, at the very least, their own values. The potential friend then decides whether to affiliate with the praiser based on the praiser’s signal (if visible). An agent may affiliate with up to *m* signalers and attract up to *k* followers. These limitations represent the bounded amount of time individuals can spend with each other (Dessalles, 2014).

After these two rounds of encounters, payoffs are computed. Agents receive a payoff *F* for each affiliate they attract, with the maximal social payoff being *k* × *F*. Let us first examine the basic social game (without praise), before analyzing the effect of praise on the emergence of “heroes”.

### 4.1. Basic social signaling (without praise)

In a situation of perfect visibility ($p_1 = 1$), the best signalers are certain to attract *k* followers each. The system evolves toward a situation in which the most competent individuals $q > \hat{q}_1$ emit the same signal $S_1$, while other individuals don't invest in the signal whatsoever (Fig. 2).

<figure><img src="image_4.png" /><figcaption>Fig. 2. Binary signaling at equilibrium. The blue curve shows the signal level $S(q)$ (competence $q$ is supposed to be uniformly distributed between 0 and 1). Signal cost $C(S, q)$ for a given signal intensity $S$ is proportional to the green curve (signals are cheaper as $q$ gets closer to 1).</figcaption></figure>

This all-or-nothing signaling pattern is an evolutionarily stable strategy (ESS) (see supplementary material). It is a situation of honest social signaling (Gintis et al., 2001), here generalized to continuous values. If $m < k$, then $\hat{q}_1 = 1 - \frac{m}{k}$.

If $p_1 < 1$, the situation of Fig. 2 still holds, though with a different value of $\hat{q}_1$. If the expected number $p_1 \times N$ of individuals who witness a given signal is such that $m < p_1 N < k$, then $\hat{q}_1 = 1 - \frac{m}{p_1 N}$. If we note $f(p_1) = \min(p_1 N, k)$ the average number of followers per signaler, the signalers' benefit is: $f(p_1) \times F - C(S_1, q)$. The signal $S_1$ displayed by more competent individuals is such that there is no incentive for a less competent individual to compete. $S_1$ stabilizes at a level that makes the benefit equal to zero at the threshold (see supplementary material). It is the solution of:

$$C(S_1, \hat{q}_1) = f(p_1)F. \quad (1)$$

### 4.2. Signaling in the presence of praise

Praise may seem like a peculiar behavior. While it enhances the reputation of the admired individual, it also invites comparison with the praiser's often inferior qualities. However, the model demonstrates that praising behavior can still emerge, not as a means to please the explicit target, but rather as a strategy to attract potential friends who are more accessible. Agents may increase the visibility of their signal from $p_1$ to $p_2 \approx p_1 + p_p$ by selecting a top-signaler to praise. Praising acts generate a side-effect, which translates into a bonus *s* for the praised individual. This bonus represents the contribution of praise to the hero's fame. It is important to note that praises are not social links in the model. Praising (often inaccessible) heroes is by essence an asymmetrical relation, allowing a “hero” to receive an unbounded number of praises. It is distinct from real affiliations among friends which require spending time together. At equilibrium, non-signalers have no incentive to praise, as this would only make their absence of signal more visible. Signalers will systematically praise as soon as the cost of praising $c_p$ satisfies:

$$(f(p_2) - f(p_1)) \times F > c_p. \quad (2)$$

When praise makes rarely observed events ($p_1$ small) visible ($p_2$ significant), this condition is simply: $(k - p_1 N)F > c_p$. The existence of praise moves the signaling threshold from $\hat{q}_1$ up to $\hat{q}_2 = \min(1 - \frac{m}{p_2 N}, 1 - \frac{m}{k})$ and the signal from $S_1$ to a new value $S_2$ obtained by substituting $p_1$ for $p_2$ in (1):

$$C(S_2, \hat{q}_2) + c_p = f(p_2)F. \quad (3)$$

The total windfall to be shared among “heroes” is $P = N(1 - \hat{q}_2)s$. Competition for attracting praise may lead to a winner-take-all situation in which the most competent individual sends an intense signal $S_H$ to win *P* (Roberts, 1998).

A situation in which *H* mostly competent individuals (“heroes”) send the same signal $S_H$ and receive the same amount of praise $\frac{p_H}{H}$ is however possible (Fig. 3). This multi-hero situation ($H > 1$) occurs if increasing the signal level by a minimal noticeable relative value $\delta$ involves a prohibitive cost for the top-most individual:

$$\left(P - \frac{P}{H}\right) < C(S_H \times (1 + \delta), 1) - C(S_H, 1). \quad (4)$$

If $\delta$ is small enough, one gets a first-order approximation for this condition:

$$P \left( 1 - \frac{1}{H} \right) < \delta S_H \times C_1(S_H, 1). \quad (5)$$

One may suppose that the signal cost increases asymptotically as the signal approaches a limit *L* (e.g. available time, acceptable risk) and that performers measure the relative distance to that limit when evaluating
<figure><img src="image_5.png" /><figcaption>Fig. 3. Emergence of three signal levels depending on competence.</figcaption></figure>

costs. With these assumptions, the cost first derivative close to the limit L has the form $C_1(S, 1) = \frac{c_0}{L-S}$, where $c_0$ is a coefficient that controls the steepness when approaching L. A condition for observing $H > 1$ “heroes” is (see supplementary information for details):

$$S_H \geq \frac{L}{1 + \frac{c_0 \delta}{P \left(1 - \frac{1}{H}\right)}} \quad (6)$$

The number of “heroes” is obtained by observing that the least competent/motivated among them should have a positive benefit:

$$kF + \frac{P}{H} \geq C \left(S_H, 1 - \frac{H-1}{N}\right) \quad (7)$$

If (7) is not satisfied for $H = 2$, there can be only one “hero” whose signal $S_{H=1}$ is just costly enough to discourage the second-most competent individual:

$$C \left(S_{H=1}, 1 - \frac{1}{N}\right) \geq kF + \frac{P}{2} \quad (8)$$

Otherwise, one can plug the lower bound of (6) into (7) to get a constraint on $c_0$ to get more than one “hero” (see supplementary information):

$$kF + \frac{P}{H} \geq \frac{c_0}{1 - \frac{H-1}{N}} \log \left( 1 + \frac{P \left(1 - \frac{1}{H}\right)}{c_0 \delta} \right) \quad (9)$$

Fig. 4 shows that based on constraint (9), the number of “heroes” grows with the population’s size for a given cost coefficient $c_0$. The simulations detailed below confirm that praising and heroism co-evolve until signals stabilize around three distinct levels: 0, $S_2$, and $S_H$.

## 5. Simulation

The purpose of implementing the model in an agent-based simulation is to assess its robustness in situations where matching among friends is imperfect. In both the simulation and real life, individuals may abandon some friendships in favor of more appealing alternatives. These former friends may take time to establish new affiliations. As a result, social matching remains incomplete, with good signalers ending up with fewer than m accepted affiliations or less than k affiliates.

Agents’ behavior is controlled by two flexible behavioral traits that agents learn: the intensity of their signal and their probability of praising some “hero”. These traits are initially set at 0. Agents may try out other values of the traits, mostly locally and, with a small probability, by making a significant change. They keep a limited memory of the most profitable values and restart their exploration from them. An agent dies when reaching maximum age and is reborn as a blank agent with the same competence. As in the model, interactions are kept local:

<figure><img src="image_6.png" /><figcaption>Fig. 4. Number of “heroes” when more than one can coexist. Signaling cost is proportional to the cost coefficient $c_0$ (x-axis). Marginal cost is supposed to grow asymptotically when the signal gets close to a limit L: $C_1(S, q) = \frac{c_0}{qL-S}$. One can see that larger populations (here $50 \le N \le 1000$) can support more “heroes”. Parameters are $k = 5$, $F = 10$, $p_2 = 0.3$, $s = 3$ (hence $P = 1.2N$), $\delta = 0.1$, and $L = 100$. When $c_0$ is too large to fall within the curve (for a given N), there can be only one “hero”.</figcaption></figure>

individuals observe each other’s signals during dyadic encounters with probability $p_1$ and follow the best signaler available. The structure of interactions and the calculation of payoffs follow the model’s specifications.

Fig. 5 (a) shows the signal intensity $S_H$ attained by top-signalers. It reaches values close to the maximal asymptotic limit $L = 100$, either within large groups or when the payoff s for being praised is significant. One can see in Fig. 5 (b) that individuals are more prone to invest in praising third parties when the default visibility $p_1$ of their own signal is smaller than 0.2. Fig. 6 confirms that the average number of “heroes” grows with group size.

## 6. Discussion

This study offers a proof of concept to show that heroism may emerge from the existence of a praising audience, while praising itself appears as a second-order social signal. Individuals praise heroes to draw attention to their own signal, so as not to be mistaken for non-signalers. With these simple assumptions, the population splits into three discrete categories: non-signalers, signalers, and super-signalers (“heroes”) who are praised by signalers. The crucial assumption underlying the phenomenon is that some social signals are not fully visible or accessible, and that praising increases their visibility.

The model does not claim to capture the complexity of real situations. The competence underlying any real-life performance cannot be reduced to a mere number, as it depends on complex situational characteristics (strength, skill, training, experience, boldness) and on the motivation to prioritize signaling over addressing more immediate needs. Also, the model considers one single social dimension valued by all. Human groups establish social bonds based on a variety of values, such as sports, musical performance, and even “trendiness”. Though the signal sent by “heroes” amplifies the signal sent by “normal” people along some dimension, the model could be extended to deal with several values simultaneously.

Signals and praise are considered truthful in the model because of their cost (Zahavi, 1975). A more realistic representation would include hypocrisy as a negative signal (as in Lie-Panis & Dessalles, 2023). The model is also agnostic about psychological motivations. Admiration may be accompanied by intense subjective experience (respect, joy, inspiration, awe, sympathy, enthusiasm, humility...). The model only
<figure><img src="image_7.png" /><figcaption>**Fig. 5.** Simulation results. (a) Intensity of signals emitted by the top signalers, depending on group size *N* and the bonus *s* > 0 received by praised individuals. (b) Average praising probability among signalers, depending on baseline visibility (*p*₁) and the additional visibility (*p*₂) due to the praising act. Default parameters are *k* = 5, *m* = 2, *F* = 10, *c*₀ = 100, *c*ₚ = 3, *L* = 100; In (a): *p*₁ = 0.1, *p*₂ = 0.3; In (b) *N* = 50, *s* = 3.</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>**Fig. 6.** Emergence of several “heroes” in large populations. Number of top signalers (“heroes”) (in red, right scale), shown with the signal sent by these “heroes” (*S*<sub>H</sub>) (green) and “non-heroes” (*S*<sub>2</sub>) (blue) as a function of population size (*N*). Under the conditions of this simulation, one expects three “heroes” on average in a group of 500. Parameters are the same as for Fig. 5, with *c*₀ = 100. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

addresses the public aspect of admiration. However, the reason why praising feats may be associated with intense emotions becomes less mysterious if admiration has a social signaling function.

The paper proposes a mechanism that solves two mysteries at once: the disproportionate investment by certain individuals in altruistic behavior, and the existence of an audience willing to expend time and effort to commend their actions. Both behaviors can be understood as forms of social signaling: extreme altruists signal their virtues to elicit praise, while those offering praise signal their altruistic orientation to attract social bonds. This dual-signaling framework is stable and resistant to free-rider issues. It also explains how humble achievers (Hawkes & Bliege Bird, 2002) or unwilling heroes can still attain status. In the latter case, individuals who never claimed to have performed heroic acts ('All I did was my duty') may nevertheless be acclaimed by the crowd (Garcia, 2003, p. 238). In the model's logic, their actions are elevated to heroic status when praising individuals use them as an opportunity to display their own patriotism.

The significance of second-order social signals should not be underestimated. While negative evaluations lead to signal leveling (Lie-

Panis & Dessalles, 2023), positive evaluations offer an opportunity to a few individuals who issue exaggerated signals to garner widespread approval. Such individuals, particularly heroes, are deeply invested in their reputations, with some even risking their lives to defend their ‘honor.’ In more everyday contexts, second-order signals help to better understand phenomena such as the rise of influencers on social media or the role of positive gossip in conversations. Sociological studies have often treated praise at face value, assuming it is what it appears to be. As suggested by the present study, though, fervent cheers at a concert broadcast live in a fan zone are not intended for the distant performer on stage, no more than a eulogy is for the deceased. Positive evaluations are never socially neutral; rather, they serve as strategic displays of the values we wish to share with others in our social circles. This new understanding of the function of praise raises interesting research questions. One is to understand why heroes may, in some cases, go as far as choosing to sacrifice their lives for the group (Whitehouse, 2018). Another is how the model might apply to contexts where multiple, potentially contradictory competencies are simultaneously valued in a population, possibly leading to societal segmentation.

## CRediT authorship contribution statement

**Jean-Louis Dessalles:** Writing – review & editing, Writing – original draft, Software, Methodology, Formal analysis, Conceptualization.

## Acknowledgements

The authors would like to thank Julien Lie-Panis for initial discussions about the model and Pat Barclay for his insightful comments on an earlier draft. This work was supported by Telecom-Paris.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.evolhumbehav.2025.106656.

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