Image: A vintage and somewhat unscientific map of mental functions. But how fine grained are these adaptations?

– by Thomas J. H. Morgan

From the outside, the brain is a medium sized, pinkish-grey blob. At the microscopic level, it’s a densely woven mesh of countless neurons. In between these two extremes is the brain’s functional structure – the mind, or more concretely, how those neurons are wired together to solve problems and manage behavior.

A popular metaphor for the mind is the Swiss-army knife. Like the brain, the Swiss-army knife has a large-scale external appearance (red, cigar-shaped, with silver rotating appendages) and a microscopic constitution (atoms and molecules). However, it also has a functional mid-level structure, being composed of subunits that solve particular problems – opening bottles, filing nails, driving screws, scaling fish, cutting wires, and so on.

As useful as it is, the Swiss-army knife has limitations. Screwdrivers and fish scalers aren’t much use without screws or a means to catch fish. Someone heading out into the unknown might instead prefer a kit of fewer but more widely applicable tools. Say, an axe and a crowbar. Rather being designed for specific problems, these items can be put to work in many contexts and so can handle unforeseen situations more successfully.So, what does the mind look like – a bespoke Swiss-army knife with many task-specific solutions, or an adaptable survival kit of just a few broadly applicable tools? This is the question we set out to answer. We recruited groups of participants and had them collectively complete a task. The task we chose was mate-choice copying – participants evaluated how attractive they found photos of other people, and then they saw what other participants thought and could alter their decision.

Mate-choice copying is particularly relevant to this question as it sits at the interface of two fields – evolutionary psychology, which has collected lots of data on human mate choice, and cultural evolution, which has extensively studied copying and social influence. In addition, while some prior work on mate choice copying has suggested it is managed by a mental adaptation evolved specifically for this task, other work has argued the mental system is broader and handles decision making and learning more generally.

Prior work led by Dr. Sally Street had already found that humans were equally influenced by what others thought about the attractiveness of human faces, human hands and abstract art, but we wanted to dig into the details. Studies of copying across many other contexts have documented that humans are influenced by majorities (“conformity”) as well as by successful individuals (“prestige”). We tested whether these same patterns occurred in mate-choice copying. In addition, we collected data from both men and women to see if there were gender differences in copying. Our reasoning was that the more mate-choice copying differed between genders or from copying in other contexts then the more likely it was underpinned by a mate-choice specific adaptation.

Our results showed the opposite. Participants were influenced by each other’s decisions, and analysis of the patterns in this copying found strong evidence of both conformity and prestige. In addition, there was strong evidence that both men and women copied in the same way. We thus concluded that rather than a bespoke system, mate choice-copying is underpinned by the same copying mechanisms that are active in other contexts – it’s a crowbar, not a Swiss-army knife.

There are two important points though. First, although we suggest the mind is made of general-purpose systems, these systems are still adaptations – they are just adaptations for broad classes of problems, as opposed to specific tasks. Second, even broadly applicable systems can be flexibly tuned to meet particular needs. For instance, copying is known to be adjusted according to factors like confidence and risk. Through factors like these, even broad ranging systems can be adaptively tuned for particular tasks, just like a crowbar can be applied with different amounts of force. Thus, the mind might have fewer solutions than the Swiss-army knife metaphor suggests, but its adaptive power comes from the flexible use of the tools at its disposal.

Read the original article: Foreman, M., & Morgan, T.J.H. (2024). Prestige, conformity and gender consistency support a broad-context mechanism underpinning mate-choice copying. Evolution and Human Behavior, 45(1), 58-65.

– by Eric Schniter, Michael Gurven, & Hillard Kaplan

Humans depend on culturally transmitted knowledge and skills crucial for their survival. But how is essential culture transmitted?

In economically modern human societies, most youngsters are formally educated alongside their similar-aged peers by unrelated teachers from an older generation. Adults in modern societies often develop into specialists, learning novel specialized skills from their coworkers and prestigious peers. But is this pattern of “horizontal” and “oblique” transmission from unrelated others the most fundamental mode of human culture transmission?

Evidence from subsistence groups like the Tsimane suggest that the patterns of cultural transmission, schooling, and skill development seen in economically modern society are not universal to all humans and were likely uncommon among our ancestors who relied on multi-generational broad social structures of kin-based exchange.

In our recent study in Evolution & Human Behavior, we brought a life history approach to the study of transmission vectors that considers how changes in embodied capital, combined with gender and relatedness, affect the costs and benefits to both the influencer and the learner. We emphasize the investments made in knowledge and skill across the human lifespan, and the returns on these investments at different ages that manifest in the array of competencies we see in a cross-section of society. We adapt the vector framework introduced by Luigi Cavalli-Sforza and Marcus Feldman to describe vertical, oblique, and horizontal cultural transmission relationships (see Figure 1).

Figure 1. Schematic representation of the intra- and inter-generational cultural transmission vectors, both related and unrelated that potentially influence receivers’ culture acquisition. Our cultural transmission study focuses on the relationships in bold fonts and colors.

So, what did we find? When do people learn, from whom, and how?

Our study focusing on 421 adult Tsimane forager-farmers native to Bolivia sheds light on how essential skills and specific domains of knowledge find their way from person to person and from one generation to the next. We examined the transmission vectors and styles of influence behind Tsimane people’s acquisition of 92 skills, ranging from those involving foraging, domestic chores, crafts and tool manufacture, childcare, music, and more. This transitioning community, living at the intersection of traditional foraging and farming practices and the rapidly changing demands of market integration, provides a unique lens to understand the vectors and influences driving cultural transmission.

We used a survey approach to gather knowledge about the skills that people had, their proficiency with those skills, and their reports of who influenced their skill acquisition by teaching, correction, helpful example, encouragement, or discouragement. Tsimane people report acquiring basic competency with most skills by the end of childhood and developing their skill proficiency further throughout adulthood.

Older, same-sex relatives predominantly influence Tsimane culture transmission, and in a variety of ways. They lead by example, directly instruct, correct learner’s mistakes, and sometimes just encourage and positively reinforce behaviors. Older adults are named most for these influences, despite the greater amount of time that kids spend interacting with same-generation peers while developing proficiency with most skills.

95% of culture transmission is reported as coming from within families (87% blood relatives, 8% related by marriage) and 75% from older kin. Vertical transmission from parents is most common, followed by oblique transmission from older kin. For example, grandparents –revered for their wisdom and experience—play a crucial role in imparting difficult-to-acquire skills that demand less physical strength but more complex knowledge, such as music performance, storytelling, and crafting. In contrast, peers of the same generation tend to positively influence the acquisition of modern, market-oriented skills, like finding wage labor opportunities and using machines for farming.

While similar-aged peers facilitate the acquisition of modern skills, they are reported to be the least helpful when it comes to positively influencing traditional skill acquisition. Unlike older kin, peers of the same generation are more likely to be competitors during the skill acquisition process, explaining why they are reported to be the mostly likely contributors of discouragement, a negative influence intended to inhibit skill acquisition.

We examined a diverse set of essential Tsimane skills and types of knowledge that has been conserved for untold generations, underscoring the important role of older adults and the multi-generational support of youngsters and young adults. This dominant pattern of kin-biased cultural transmission from older to younger generations mirrors the Tsimane pattern of kin-biased net food transfers between generations and within extended families (see here and here).

In subsistence societies, essential skills continue to develop for decades after they have been acquired (e.g., see here), forming older adults into “banks” of accumulated cultural solutions and practical knowledge (e.g., see here, and here). Our findings are consistent with the view that older-to-younger cultural transmission is the result of selection for a long lifespan favoring peak abilities for information retrieval and transmission to younger kin at late ages.

Generations of cultural isolation likely helped the Tsimane conserve their multigenerational system of cooperation and downward traditional culture transmission. However with rapid modernization, the current and future generations will face new challenges. Older adults’ contributions to their forager-farmer economy have been possible because of their comparative advantages and accumulated experience that comes with age. As transitioning societies increasingly value novel skillsets, and as younger generations develop comparatively greater proficiency and productivity with these modern skills, the opportunities for positive cultural influence and transfers from younger to older generations will grow. These results suggest that successful aging among adults, in both Tsimane and economically modern societies, will hinge on both upward inter-generational flows of knowledge and resources, and younger generations maintaining an appreciation for older adults’ roles as trusty helpers, educators, and experts of cultural traditions.

Read the original paper: Schniter, E., Kaplan, H. S., & Gurven, M. (2023). Cultural transmission vectors of essential knowledge and skills among Tsimane forager-farmers. Evolution and Human Behavior.

– by Anne Pisor

Is intergroup aggression part of human nature? Or is intergroup tolerance part of human nature?

Yes and yes.

While such simple headlines make great clickbait, they oversimplify – yet both have a grain of truth. Humans are both profoundly hostile and profoundly tolerant toward out-groups. We’re profoundly flexible.

Flexible intergroup relations have deep evolutionary roots. Though chimpanzees are often thought of as hostile toward out-group members and bonobos as tolerant, there is some variation in chimpanzee intergroup relations and quite a bit of variation in bonobos. Given the range of opportunities for conflict and cooperation in humans – from defending resources and protecting ourselves, to exchanging resources and ideas – the profound flexibility of human intergroup relations shouldn’t surprise us.

Indeed, as Cody Ross and I write in a new piece in Evolution & Human Behavior, our behavior toward members of other groups is influenced by a whole pathway of things, starting with context (are resources scarce?) and individual characteristics (did my past interactions go poorly?) that affect our beliefs, attitudes, and motivations – our internal states. Context, individual characteristics, and internal states influence how we behave.

For example, take research led by Cody in rural Colombia, where indigenous Emberá and Afrocolombians live side by side – sometimes with discord, sometimes without. In one community, Emberá have more control over natural resources and there’s a history of intergroup tensions. Even though Afrocolombians are wealthier than Emberá in that community, Afrocolombians feel that intergroup tensions are high and cooperation low – and they share less money and food with Emberá households accordingly. In another community with less unequal resource control and less history of intergroup tensions, wealthy Afrocolombians share with poorer Emberá. The impact of context on our internal states and our subsequent cooperative behavior is not to be underestimated.

Capturing the profound flexibility in human intergroup relations requires methods that document it accurately. In our Evolution & Human Behavior article, Cody and I focus on a subset of intergroup relations called parochial altruism: in-group favoritism paired with out-group hostility. Variance in parochial altruism is best documented using a combination of methods, we argue, like experiments that measure who people prefer to share with, observations on who they actually share with in everyday life, and self-reported internal states.

Using just one method can generate bias. For example, as Cody has documented in Colombia, people may not reciprocate sharing in real life because they can’t afford it, but will preferentially repay real-life sharing in an experiment when researchers provide the money. In short, to triangulate reality, researchers must approach it from a variety of angles.

If intergroup behavior is so flexible in humans, why are simple headlines about it so common? One reason is that they often seem to either confirm or challenge our own internal states – “yes, of course humans are inherently violent”; “no, violence isn’t part of human nature!” Our own internal states are themselves products of our experiences and cultural contexts, among other things. Journalists know that headlines that confirm or challenge our internal states excite our emotions, making us more likely to click.

But these headlines are also common because researchers have their own internal states too, and these influence what research actually gets done. As Brian Hare and Vanessa Woods remind us in Survival of the Friendliest, research on intergroup violence ticked up after World War II: researchers wanted to understand how atrocities could be committed on such massive scales. Science can be guided by researchers’ own experiences or by history, which impact researchers’ beliefs about intergroup hostility, tolerance, and peace.

When there is a greater diversity of experiences among researchers, science is the better for it. It increases the diversity of research questions researchers ask, and having a range of research questions can better tap the range of the human experience – our flexibility in intergroup relations, for one. In turn, this data can be leveraged for policy recommendations and action – for example, what features of context, individual characteristics, and internal states have the greatest downstream influence on intergroup behavior? Which lever should we pull to intervene, reducing intergroup hostility?

As Cody and I highlight, answering these diverse questions about intergroup relations requires data that accurately reflect the diversity of human experiences – our profound flexibility. Ensuring data collection reflects research questions, triangulating across multiple types of measures, striving to ensure measures reflect reality – this will improve our understanding of human intergroup relations and the upstream factors that influence it.

War in Israel and Gaza, tensions over fossil fuels at COP28, impasses in parliaments and US Congress – these stories bring to mind humans’ profound intergroup hostility. Communities helping one another after a storm, organizations standing together on picket lines – these bring to mind humans’ profound intergroup tolerance. For researchers and non-researchers alike, our beliefs, attitudes, and motivations are affected by what’s around us and by our past experiences. When researchers bring that diversity of experiences to science, our understanding of human nature can also become more accurate – but only if researchers also get accurate measures of what humans are up to. Existing data already undercut the narrative that humans are doomed to favor in-groups and be hostile toward out-groups: the data instead reveal profound variability. Recognizing this variability can push scientists to challenge their own preconceptions about human nature and to draw upon a more diverse toolkit, improving our understanding of the flexibility of intergroup relations. The better we understand it, the better positioned we’ll be to address the pressing social issues of the 21st century.

Read the original article here: Pisor, A.C., & Ross, C. (in press). Parochial altruism: what it is and why it varies. In press at Evolution & Human Behavior.

Author’s note: the author thanks Cody Ross, Kris Smith, and Eleonora Zanetti for helpful comments on this piece. Photo credit: Karl Frost.

– by Diego Guevara Beltrán, Jessica Ayers, Lee Cronk, & Athena Aktipis

Thirty-four experts on cooperation walk into a bar and begin to discuss the ‘true’ meaning of the term ‘reciprocity.’ A biologist offers one definition. She says, “we observe reciprocity when the gift of some food, or another favor, increases the probability for the donor to receive the same thing, or something else, from the receiver”.

Perhaps because that approximates one of Robert Trivers’ definitions of reciprocal altruism, all experts nod in agreement with the biologist. However, not being quite content with this definition, an economist retorts “[that the] formulation is rather sloppy.” His colleague adds that “reciprocity should be conceptualized as reciprocal strategies in repeated games, normative obligations, and individual preferences to respond to kind acts with kind acts and to unkind acts with unkind acts.”

“Yes, yes, but how do we operationalize reciprocity?” asks one psychologist. He says, “The best statistical definition is the contemporaneous correlation of two individuals’ responses within time-series regression analyses and as partner effects among these variables.” Intrigued but bewildered, another psychologist adds “reciprocity is reciprocity, that’s what I was taught! Because he gave to him and he gave to me, then I gave to her, albeit indirectly. But I couldn’t keep track of who gave what to whom, what I owed to him, or who I should groom. Maybe we should just avoid this definitional animosity and just call all transfers something-something reciprocity!”

A review of the literature yields a proliferation of reciprocity-related terms – thirty-four at last count. That might lead you to believe that experts indeed agreed with the bewildered psychologist. Perhaps experts want to avoid definitional animosity and decided to call all transfers “something-something” reciprocity. With several conflicting, overlapping, and even a few redundant terms, it was clear there is no consensus among scholars regarding what truly constitutes reciprocity. In fact, these terms continue to proliferate, as evidenced by the preceding paper in the same issue of Evolution and Human Behavior (i.e., “dynamic indirect reciprocity”). This lack of consensus and the continued proliferation of terms motivated us to review the term reciprocity and survey cooperation experts about it.

In our study, we asked 85 cooperation experts to rate the extent to which they believe thirty scholarly definitions of the term reciprocity were truly reciprocity. Some of these definitions (e.g., indirect reciprocity: return is expected from someone other than recipient of benefit) are common in the literature, while others (e.g., homeomorphic reciprocity: exchange of things that are the same) not so much.

We first wanted to know whether experts’ responses would help us identify underlying dimensions of reciprocity (i.e., characteristics that cluster some, but not other, terms together). Applying factor analyses, we find that the scholarly definitions of the term reciprocity can be represented by four broad dimensions: (1) Balanced Transfers (i.e., transfers that are of equal or equivalent value), (2) Reputation-based Transfers (i.e., transfers where individuals give to others who have given in the past and receive from others if they have given in the past, (3) Debt-based Transfers (i.e., transfers where individuals keep track of, and expect repayment for, what they give to others), and (4) Unconditional Transfers (i.e., transfers that do not revolve around concepts of debt or account keeping). This four-factor framework captures greater variation in types, or dimensions, of transfers than previous frameworks (e.g., direct, indirect, and generalized reciprocity).

We encourage experts to consider adopting these terms. In doing so, we can provide specific qualifiers to a transfer while at the same time reducing the possible number of dimensions that describe a transfer based on whether, or the extent to which, they are balanced, debt-based, reputation-based, or unconditional. For example, we saw that several experts used the same term (e.g., indirect reciprocity) to describe multiple different reciprocity terms. Hence, our framework would allow experts to avoid using overlapping terms to describe different dimensions of transfers, ultimately improving communication within and between disciplines.

We then assessed the level of consensus among experts regarding what truly constitutes reciprocity. There was no consensus regarding what should be considered reciprocity. However, over 90% of experts agreed that unconditional transfers (e.g., generalized reciprocity I: non-conditional sharing and giving of assistance) should not be considered reciprocity. So, although experts do not yet agree on what truly constitutes reciprocity, these findings suggest that the scholarly community can move beyond discussions of whether unconditional transfers should or should not be considered reciprocity. They should not be.

Instead, the scholarly community might consider focusing on outstanding disagreements, such as those identified in our study. For example, some experts believe that transfers are reciprocity only if agents have the intention to reward a giver or incentivize a receiver to give back, while others do not think intentionality is a defining feature of reciprocity. Similarly, some experts believe that reciprocity must involve a cost, while others believe that reciprocity does not involve cost or transfers with negative utility.

Focusing on these issues might help experts reach a consensus regarding what truly constitutes reciprocity. However, we might also supersede these disagreements by adopting the language of Balanced, Debt-based, Reputation-based, and Unconditional transfers. Does it matter for the study of evolution and human behavior whether unconditional giving is or is not reciprocity? We think not. What matters is that we reach a deeper understanding of the ultimate causes and proximate mechanisms underlying the diversity of transfers that have shaped the evolution of human behavior. While our framework does not include information about the ultimate causes of behavior, it provides information about the proximate mechanisms underlying an individual’s decision to engage in a transfer. Overall, we hope our study allows the scientific community to gauge the current level of consensus, or lack thereof, regarding the term reciprocity, and ease communication across academic fields.

Read the original article: Guevara Beltrán, D., Ayers, J.D., Munoz, A., Cronk, L., & Aktipis, A. (2023). What is reciprocity? A review and expert-based classification of cooperative transfers. Evolution & Human Behavior, 44(4), 384-393.