– by Kristopher Smith

(Image description: Women in a Tanzanian fishing village “planting” shared algae lines during low tide.)

Long-distance relationships, whether they be friends, family, or colleagues, are found across the world. And while you might think that long-distance relationships are products of planes, phones, or the internet, long-distance relationships have actually long been an important component of human sociality. For example, obsidian trade networks in East Africa stretching hundreds of kilometers indicate humans were maintaining long-distance relationships as early as 700,000 years ago. Why do people bother being friends with people who live so far away from them?

Friends are one strategy people use to manage risk. Life is unpredictable, and while today you might be well-fed and in good health, tomorrow you might find your fortunes down. Friends help deal with this uncertainty by providing help to one another during down times, giving each other support whenever they need it, regardless of who helped whom last – that is, engaging in need-based sharing. Tracking debts between close-distance friends is a faux pas across cultures, and friends are expected to help each other as long as they are able to help–denying help because a previous act of support has not been reciprocated can be a quick way to end a friendship. Need-based helping is more robust compared to tit-for-tat reciprocity; if friends only helped each other when they can be paid back, then when someone was most in need of help, their friend would be unwilling to help due to concerns of not being repaid. By always helping each other when one of them is in need, friends can be assured they have the support to get through hard times.

But risks in the environment are often clustered in time and space, hitting everyone in a given area at the same time. In these cases, close-distance friends might not be able to provide help. For example, if you are a fisherman depending on a daily catch to feed your family, a large storm that stops you from going out to the ocean for days can spell trouble. And your friends might literally be in the same boat, unable to help you because they too need help. However, if you have friends further down the coast where the storm has not hit, you can reach out to them for support. In other words, long-distance friends are useful for providing access to resources not available in your local environment. Even when times are good, these friends can get you resources you cannot get locally, such as a place to stay while traveling or information about job opportunities in another city.

While long-distance friends can be useful for supplementing help from close-distance friends by providing access to non-local resources, they also present additional challenges. First, long-distance friends are seen less frequently. This makes it harder to monitor whether a friend is declining to help because they are intentionally refusing to help–and thus defecting on their cooperative partner–or are simply unable to help at the time. Second, they are unlikely to belong to the same cooperative institutions, which among close-distance friends can help smooth over disagreements and provide outside enforcement of cooperative agreements. For example, when friends attend the same religious congregation, they can appeal to their religious leader to mediate conflict between them, such as by reminding a stingy friend of the virtue of helping. Taken together, it is easier for long-distance friends to cheat the need-based sharing common among friends, and it is harder to encourage them to help via institutions. As a result, long-distance friends may be less likely to rely on need-based sharing and more carefully track debts between each other, only providing help when accounts are nearly balanced between each other.

Rural villages along the coast of the Western Indian Ocean in Tanzania primarily rely on small-scale fishing for their food and income. The productivity of different fishing spots changes across seasons, and to access better fishing spots, fishermen must travel to different villages, where they meet and befriend locals. Other professions connected to fisheries, such as fish processors and fishmongers, also travel to where business is booming, forming useful relationships beyond their home villages. As a result of people following the fish, they build a sprawling network of long-distance friendships between villages. Do these long-distance friends provide the same kind of need-based support as friends living in the same village?

To answer this, my collaborators and I interviewed 917 people in 21 fishing villages in the Tanga Region of Tanzania, asking participants about their friendships in their village and neighboring villages. We randomly chose one friend in their village and one friend in another village to ask about different kinds of help the participant had received from each friend, such as advice, loaned tools, and cash help in the form of loans and gifts. Loans and gifts offer interesting insight into account keeping because loans have an explicit expectation that the help is repaid in the future–it is literally tracking debts between friends, which is usually not the case for gifts.

Unsurprisingly, people were more likely to receive help in general from close-distance than long-distance friends. However, contrary to our prediction, long-distance friends were actually a little less likely to give loans than gifts, while close-distance friends were a little more likely to give loans than gifts. Long-distance friends did give larger loans than gifts, which at first glance is consistent with our predictions, but looking at why these loans were given revealed that long-distance friends were much more likely to give a loan for business investments compared to close-distance friends. And help as a business investment was the largest kind of help given, for both loans and gifts and for close- and long-distance friends. That is, long-distance friends are often providing loans for big-ticket items like new boats or ring nets. In many of these villages, that kind of cash is just not available to give out, and people are likely piecing together investments from their wider social network, receiving support that is unavailable locally.

While there were some differences between close- and long-distance friends, more notable was the similarity between them. Regardless of distance, the biggest kinds of help given by friends was advice, help when sick, and help with getting work done. And even among cash help, the differences between close- and long-distance friends were small. Previous research on long-distance relationships have focused on ritual relationships, such as osotua among the Maasai, where social and supernatural sanctions help reinforce the cooperative relationships, making them robust sources of support. Here, even among friendships with no special status, distance does not impede support flowing freely between friends.

The fact that long-distance friends provide the same quality support as close-distance friends has important implications for cooperation more broadly. Many collective action projects, such as managing open-access fisheries, require people to cooperate at a distance with people living in other communities. This can be a hard obstacle to overcome. Just like cooperation between friends, difficulty in monitoring others and lack of shared institutions can breed a lack of trust and interest in cooperating with people who live in other communities, causing between-community collective action efforts to fail. But if long-distance friends provide support to one another, then this can foster interdependence between them–their fortunes are tied together, such that a person providing help to their friend indirectly benefits because their friend is more able to help them in the future. This interdependence makes it more likely for them to participate in between-community collective action because of their mutual benefits from it. Long-distance friends then can be a foundation to build-up larger cooperative efforts, bridging communities with their shared interest, an idea my collaborators and I are currently testing in this context.

A friend in need is a friend indeed. And as it turns out, this is true regardless of whether your friend is your neighbor or lives a world away. People lean on their friends to get through hard times and long-distance friends can provide some kinds of help that close-distance friends might not be able to provide, no strings attached.

Read the original article: Smith, K.M., Pisor, A.C., Aron, B., Bernard, K., Fimbo, P., Kimesera, R., & Borgerhoff Mulder, M. (2023). Friends near and afar, through thick and thin: comparing contingency of help between close-distance and long-distance friends in Tanzanian fishing villages. Evolution & Human Behavior, 44(5), 454-465.

– By Amy Zhao and Brendan Zietsch

Ariana Grande and Ethan Slater have been making headlines due to their alleged affair, which occurred while both parties were married to their respective partners. Another aspect of their relationship that hasn’t gone unnoticed is Ethan Slater’s striking resemblance to Ariana Grande’s brother, Frankie.

This is just one example of a couple who look like siblings. Many of you may even have your own stories of having mistaken a couple for being siblings, a phenomenon so common that it has led to the creation of a popular Instagram account called Siblings or Dating.

Despite all this anecdotal evidence, there has been mixed empirical evidence as to whether couples actually share facial similarities. But even if we assume that couples do have similar faces, it’s unclear why someone would even date another person who looks similar to themselves in the first instance.

Some researchers have proposed that couples might look similar because couples share the same lifestyles, diet, exercise, stressors, etc. and so, couples’ faces become more and more similar over time. However, there is mixed evidence to support this hypothesis.

Instead of couples’ faces converging over time, an alternative theory is that individuals prefer facially similar partners to begin with; that is, individuals find similar faces attractive. However, there is mixed evidence for this association too. The studies from which these mixed findings have been derived are flawed in several ways. Firstly, these studies have exclusively measured facial attractiveness through participants’ ratings of 2D images. These images consist of photographs of real-life participants, or they are computer generated/digitally morphed faces. There are several implications of using images to obtain attractiveness ratings. Firstly, humans have relied (until most recently) on face-to-face interactions to interact and form judgments about another person, especially another potential mate. And secondly, morphed faces can lack realistic features such as facial hair, skin texture, or head hair. Therefore it is unclear whether past findings can validly reflect real-life behaviours and preferences. In addition, facial traits (such as similarity, averageness, and masculinity) are often subjectively rated by participants. These facial trait ratings may be conflated with ratings of facial attractiveness and can be subject to other biases (e.g. the halo effect).

We conducted a large speed-dating experiment with 682 participants and there were 2285 speed-dating interactions. Participants speed-dated members of the opposite sex for 3 minutes at a time. After these interactions, participants rated their speed-dating partners on facial attractiveness, and kindness and understanding. We landmarked the facial photographs of all participants and calculated objective, data-driven scores for facial averageness, facial similarity, and facial masculinity to address the limitations of subjective ratings of such facial traits.

We found that participants rated partners who had geometrically similar faces to their own as more attractive, showing that individuals do in fact have a preference for facially similar opposite-sex individuals.

The question still remains, why do people even find facially similar people more attractive in the first case?

We found that people with similar faces also rated speed-dating partners as more kind and understanding, even when controlling for the ethnicity of the participant and partner. Our findings are in line with an alternative explanation where a preference for similarity is not driven by the attractiveness of similar faces per se, but that similar faces may signal kinship. While cues of kinship can decrease sexual desirability due to the costs of inbreeding, the same cues can increase positive social behaviour by promoting increased chances of survival via altruistic and cooperative behaviour from others (inclusive fitness benefits). There is also evidence that positive assortative mating (mating with someone who is similar) can result in net positive reproductive benefits, as a small degree of genetic relatedness is associated with higher fertility.

Sure, we have shown that individuals do prefer similar faces, but does this preference extend to real-life mate choice?

We’re not sure. Actual mate choice involves the mutual desire to form a romantic partnership, but these decisions are constrained by the mating market. For instance, individuals with high mate value are unlikely to pair with low mate value individuals. Given that not everyone can satisfy their preferences, individuals must make compromises. In addition, the effects we found in our study are small, and therefore these preferences may not carry through to realised mate choice. It would be a worthwhile future endeavour to investigate how the objective facial characteristics measured in our study relate to real couples.

Read the original article: Zhao, A.A.Z., Harrison, K., Holland, A., Wainwright, H.M., Cecatto, J.-M., Sidari, M.J., Lee, A.J., & Zietsch, B.P. (2023). Objectively measured facial traits predict in-person evaluations of facial attractiveness and prosociality in speed-dating partners. Evolution & Human Behavior, 44(4), 315-323.

– by Marcell Székely & John Michael

Imagine that you and your neighbor collaborate to clear away weeds from the common garden where strawberries grow. Assuming you and your neighbor spend about the same amount of time weeding, a reasonable expectation is that you would divide up the strawberries equally.  In contrast, if your neighbor has spent more time weeding than you have, you might think it reasonable that they should receive a greater share of the strawberries. Or if your neighbor spends less time than you weeding, you might think it reasonable to expect more strawberries for yourself.

This everyday example illustrates that we have a sense of fairness, which leads us to divide rewards of joint actions equitably among contributors. Moreover, it shows that this sense of fairness is not just a blind tendency to divide rewards equally but that it takes effort contributions into account. And indeed, recent research has confirmed that our intuitions about this hypothetical scenario are reflected in people’s actual behavior in controlled scenarios. For example, Frohlich and colleagues found that when participants were placed in one room and had to proofread a text to correct spelling errors for joint rewards, they divided their collectively earned rewards in proportional to individual effort costs. Even more strikingly, recent studies in developmental psychology have even shown that three-year-old children take effort costs into account to achieve a fair distribution of rewards. For example, Kanngiesser and Warneken found that three-year-old children kept more stickers after working more than their partner.

But now let’s tweak our hypothetical example: imagine that you and your neighbor partner are tidying up the stairwell together. Notice that in this version of the hypothetical scenario, there is no tangible reward that could be divided up at the end, so fairness cannot be assured through the division of rewards. So what would you do if you perceived that your neighbor was investing a higher level of effort than you? Or a lower level of effort?

In a new article, we describe a series of experiments which we conducted to investigate how people deal with such scenarios. In the experiments, we adapted an effort discounting task developed by Hartmann and colleagues. In the adapted paradigm, people are asked to make decisions about how much effort to invest (namely, by spending time pressing the spacebar repeatedly) for different monetary rewards. This makes it possible to quantify the costs people associate with different levels of effort and to investigate how people trade off those costs against monetary reward.

We turned this paradigm into a social task by letting participants observe their partner performing an effortful cognitive task in order to ‘unlock’ the effort discounting task. Previous research has shown that people spontaneously track others’ ongoing effort investments, and indeed do so accurately. Moreover, we were confident that participants would track the partner’s effort investment using these stimuli because this effort perception manipulation was successfully used in Székely and Michael and Chennells and Michael. Sometimes, participants were rewarded jointly, sometimes they were rewarded separately. This enabled us to test whether participants chose to invest different degrees of effort against monetary reward based on their relationship to the other person. In other words, it enabled us to investigate how social context modulates people’s decisions about how much effort different rewards are ‘worth.’

Our main finding was that people invested more effort when they were rewarded jointly with their partner than when they were rewarded separately. This provides evidence that when rewards cannot be divided to ensure fairness, people instead boost their effort investment as a means of ensuring fairness with a cooperation partner. In other words, the sense of fairness governs not only the distribution of rewards but the allocation of effort in joint actions.

This is important because, while most research on the evolution of the sense of fairness has focused on the division of rewards, a great many cooperative endeavors now, as in deep evolutionary history, do not produce any distributable rewards at all. One reason for this is that the success of cooperative endeavors is always at least to some extent uncertain. Like individual endeavors, they may not succeed because of exogenous factors or lack of competence. In addition, they may not succeed because the collaborative partner may encounter a tempting outside option and so may prematurely withdraw from the joint activity. For example, hunting and foraging in ancestral environments were uncertain endeavors, and they may not have yielded any rewards to distribute at all. Moreover, even if a joint activity yields a reward, this reward may not be divisible. For example, tidying up the stairwell or building a shelter together may not produce any divisible rewards. In such activities, there is nothing to distribute at the end, but it is still possible to distribute the effort costs. This may explain why our participants were willing to invest more effort in a joint action, and thereby to increase the potential rewards for themselves and their partner, when their partners had apparently invested a high degree of effort, even if the partner’s effort investment did not increase the potential rewards.

These findings also open up important new avenues of research on the evolution of cooperation: most of the empirical studies investigating the evolutionary origins and motivational mechanisms to cooperate have focused on the exchange of money, and their results are commonly used to support claims about humans’ willingness and motivation to cooperate more generally. While this focus on monetary rewards is justifiable, people often have to make decisions about how to divide up other resources as well as costs in the context of cooperative endeavors – and had to do so in our deep evolutionary past. It is therefore crucially important to extend the scope of our focus to resources other than monetary rewards. The current findings are an invitation to take up this important challenge.

The findings also provide new impulses for future research about whether and how the sense of fairness may be sensitive to specific features of cooperation partners, such as their competence. While we intentionally bracketed out questions about the relative competence of different actors for our study, parties to cooperative endeavors often contribute with different levels of competence. We look forward to the results of future research investigating how this is reflected in people’s decisions about the distribution of rewards and allocation of effort costs.

Read the original article: Székely, M., & Michael, J. (2023). In it together: evidence of a preference for the fair distribution of effort in joint action. Evolution & Human Behavior, 44(4), 339-348.

– by Megan Bishop & Brian Lerch

Large-scale, advanced cooperative action that transverses familial lines is uniquely, exceptionally human. It lies in the foundation of our economic and political infrastructure, our corporate workflow, the small talk we make with the grocery cashier –evidence of our cooperative, pro-social nature is everywhere. Put simply, the evolution of human cooperation has bound us all to each other in a way that far transcends anything seen elsewhere in the animal kingdom. Such an incredible phenomenon begs a multitude of questions, but upon beginning the work leading up to my recent publication, The influence of language upon the evolution of cooperation, I became particularly enthralled by one: how?

Pursuing this broad question led me to another uniquely, exceptionally human development: advanced spoken language proficiency. The evolution of advanced human cooperation and language have been argued to have a reciprocal relationship, where the evolution of one drives the evolution of the other and vice versa. While the existence of large-scale cooperation assumes an ability to coordinate via complex language systems, few mathematical models examined the ways that language promotes cooperative action, despite the utility of mathematical models for analyzing how evolutionary mechanisms align with argued verbal logic.

Human cooperation has important roots in collaborative foraging, which is well modeled by a stag hunt game. Here, a group of early human foragers are presented with two options: individually hunt a hare with a guaranteed fitness benefit or risk the opportunity cost of the hare to collaborate with others in pursuit of hunting a stag. Upon the successful hunt of the stag, the fitness payoff would be evenly split amongst the group, with a much higher payoff than the hare. However, this depends on successful cooperation. Without successful cooperation, the stag hunt fails, leaving cooperators without the fitness benefit from the hare or the stag. Given the important evolutionary origins of collective human foraging, we build upon an existing stag-hunt model and include language in three various mechanisms to analyze its implications on early human cooperation.

First, we analyze an “enhancement” mechanism of language, where we assume better language systems allow for more complex coordination among cooperators, allowing them to pursue larger game with a greater fitness payoff. Here, our findings align with verbal arguments: more proficient language always makes conditions more favorable for cooperation. In our model, cooperation steadily evolves when language proficiency is better. However, this is limited by the proportion of cooperators in the population. Regardless of language proficiency, language cannot promote the evolution of cooperation with too few cooperators present.

Next, we studied an “assortment” mechanism of language, where we assume better language systems allow cooperators to communicate with, and therefore identify, other cooperators. In other words, with better language proficiencies, cooperators are more likely to join groups with other cooperators. Like our “enhancement” mechanism, our findings align with verbal arguments that better languages always favor the evolution of cooperation when enough cooperators are present in the population. However, this “assortment” mechanism is especially beneficial for cooperation when there is a high frequency of cooperators, leading to larger benefits of cooperation than is seen in the “enhancement” mechanism.

Finally, and most interestingly, is our “threshold” mechanism. Here, we assume better language proficiencies allow humans to achieve successful cooperation with fewer cooperators participating. This mechanism challenges the idea that better language proficiencies always favor the evolution of cooperation. We find that while better languages allow cooperation to evolve in populations with fewer cooperators, the overall fitness of individual cooperators decreases with better language. Upon reflection, this makes sense. Even when a smaller group can establish advanced complex coordination with improved communication, more defectors are able to “free-ride” off of the cooperators’ payoffs.

So, I answer my question of how? with an answer that frustrates answer-seeking scientists like myself, yet is universally true about our world: it’s complicated. It is likely a combination of these mechanisms that has influenced the evolution of human cooperation. For instance, perhaps the benefits of our assortment mechanism were slowed by the effects of our “threshold” mechanism. However, certain environments might not allow for the existence of one or more mechanisms, such as an environment with tightly constrained group membership would not enable positive assortment. The nuance between language and cooperation depends greatly on the mechanism of language. This model pokes and probes at our existing understanding and how language influences cooperation, unveiling that there is greater nuance than what is commonly assumed. My hope is that by challenging such a long-standing assumption, the scientific community continues to grow in our study and understanding of the fascinating, unique phenomenon we call human cooperation.

Read the original article: Bishop, M.E., & Lerch, B.A. (2023). The influence of language on the evolution of cooperation. Evolution & Human Behavior, 44(4), 349-358.