– by Luke Glowacki

When I set out to write my recent article The Controversial Origins of War and Peace: Apes, Foragers, and Human Evolution, I hoped to provide clarity on the heated debate about the role of war and peace in human evolution. Is warfare part of our evolutionary legacy, or did it emerge from the societal shifts brought about by agriculture and permanent settlements? This isn’t just an academic puzzle—it’s a question that cuts to the core of how we understand ourselves, our past, and the future of human coexistence. My goal was to peel back the oversimplification that commonly characterizes both sides and dig into the messy, complex reality of our evolutionary journey.

The debate often falls into two camps: “deep rooters” and “shallow rooters.” Deep rooters argue that warfare has ancient roots, embedded deep in our evolutionary history, possibly stretching back to our last common ancestor with chimpanzees and bonobos. They point to the brutal, coordinated violence among chimpanzees as evidence that our lineage is steeped in conflict. Deep rooters take the fact that hunter-gatherers often have war as evidence that war would have occurred among our Pleistocene ancestors. Shallow rooters, on the other hand, see war as a recent human invention, a consequence of agriculture, sedentary life, and hierarchical societies. War emerged only recently they argue, once we stopped foraging and started living in settled agricultural communities. They highlight the peaceful, cooperative lives of bonobos and some hunter-gatherers, painting a picture of a more harmonious human past.

But here’s the rub, the debate has often overlooked that both war and intergroup cooperation are parts of our evolutionary legacy, and the evidence is not nearly as clearcut as both camps sometimes assume. Simplifying human evolution into a binary of “war” or “peace” misses the fascinating complexity of our story. Chimpanzees do engage in deadly raids but using them as a direct blueprint for early human behavior ignores millions of years of evolution that followed our split. And while bonobos are often held up as paragons of peace, their societies aren’t entirely free of aggression. While lethal raids have not been reported for bonobos, they do appear to have rates of aggression that can rival chimpanzees. And neither species may be useful for understanding the last common ancestor humans shared with other apes. Further, we’ve had seven million years of evolution since sharing a common ancestor with chimpanzees and bonobos, raising questions about how much of our behavior is really inherited from a last common ancestor.

When I turned to the archaeological and ethnographic records of hunter-gatherers, the picture is  even more complex. It’s tempting to believe that warfare only appeared with the advent of agriculture, but that’s not what the evidence shows. Small-scale intergroup violence existed long before humans settled into farming communities, yet larger-scale violence appears to emerge only more recently. Just as often though, hunter-gatherers engaged in cooperative trade and alliances. And while many of them have war, war may be intermittent and even rare. Evidence of intergroup trade spanning hundreds of kilometers extends deep into the Paleolithic.

Our ancestors weren’t locked into one mode of interaction; they were adaptable, capable of both conflict and collaboration depending on the circumstances. And like humans everywhere, they likely used conflict and cooperation to obtain their goals depending on the circumstances. Because both cooperation and conflict have strong fitness relevance, both were likely important selective features in our species’ history.

What struck me—and what I hope strikes readers—is the dual legacy of war and peace in human evolution. Both violence and cooperation have been powerful forces shaping who we are. This duality resonates with the world we live in today, where acts of war and moments of profound cooperation coexist. We’re equipped for both, and it’s our environment, culture, and social structures that shapes which path we follow.

This understanding isn’t just intellectually satisfying—it’s hopeful. If peace is as much a part of our evolutionary toolkit as war, then striving for harmonious coexistence isn’t some naive dream; it’s a fundamental part of who we are. Recognizing this can shape how we approach conflict resolution and peacebuilding today. It reminds us that while conflict may be a part of our past, it is not an inevitable feature of our future.

Read the original article: Glowacki, L. (2024) The controversial Origins of war and peace: apes, foragers, and human evolution. Evolution & Human Behavior, 45(6), 106618.

– by Amy Zhao and Brendan Zietsch

Facial attractiveness studies have typically relied on asking people to rate facial photos of real-life participants or images of computer-generated faces. However, these ratings can be subjective and affected by rater biases. More recent studies (such as our own) have attempted to avoid subjective biases through the use of facial landmarks to derive objective measures of facial traits. However, these landmark-based measures ignore features thought to be relevant to face perception such as skin colour and contrast, hair, and eye colour. Here, we introduce deep neural networks as a method that combines the strengths of both approaches while addressing the limitations of facial landmarks.

Facial recognition neural network models are designed to extract abstract facial features from images. Each image input yields one set of multidimensional coordinates in feature space — a space representing the compressed version of the original image, with the number of dimensions representing the number of abstract facial features. The distance between two points (i.e. faces) in feature space reflects facial similarity, with similar faces represented by points that are closer together. While these coordinates lack direct interpretability, they effectively quantify abstract facial qualities that can be used to calculate facial traits relevant to facial attractiveness research.

We applied an existing facial recognition neural network model (VGG16) to facial images from our speed-dating study (n = 682). We used the extracted feature space coordinates to calculate traits such as facial averageness, similarity, and masculinity to predict in-person ratings of facial attractiveness and kindness. We then compared this neural network method to traditional manual (and automatic) landmark methods.

An issue that has been alluded to in past studies is that landmark measures of masculinity could be influenced by facial pitch (upward or downward tilt of a face). In our images, men tended to tilt their heads upward compared to women (there was a significant difference in facial pitch angle between genders). We found facial pitch was highly correlated with landmark measures of masculinity (-.17 ≤ r ≤ -.73). In contrast, there was little to no correlation between facial pitch and neural network measures of masculinity (.00 ≤ r ≤ -.23). Likely, gender differences in the way that men and women pose for photos might bias typical landmark masculinity measures. Here, we demonstrate that neural networks can extract facial information without being affected by limitations associated with landmarks.

Overall, facial measures derived from neural networks predicted in-person ratings, largely replicating what we found in our previous study using manual landmarks. Some differences were that neural network measures of masculinity robustly predicted facial attractiveness in men, whereas there was only context-dependent evidence for this in our previous study. We also found novel evidence for assortative preferences for facial masculinity. For example, participants with sex-atypical faces (a masculine woman or feminine man) revealed stronger preferences for a partner who was sex-typical (i.e. they rated sex-typical partners more attractive) than those with sex-atypical faces. We believe that we saw such effects in the context of neural network measures of masculinity due to increased visual information that was extracted from images of participants as well as decreased noise from participant facial pitch.

Neural network-derived measures had small to moderate correlations with landmark-based measures (.11 ≤ r ≤ .33), while manual and automatic landmarks were moderate to strongly correlated as expected (.29 ≤ r ≤ .86). Neural network masculinity measures were more accurate when it came to classifying the sex of the participant (95.6 % ≤ accuracy) compared to landmark measures (75.3% ≤ accuracy ≤ 88.8 %). Both low correlations between neural network and landmark measures as well as relatively higher sex-classification accuracy from neural network measures suggest that there is relevant information from facial photos that is uncaptured by landmarks. However, we did not find that neural networks were better (explained more variance) at predicting in-person ratings compared to other landmark measures.

While we found that neural network-derived measures do indeed predict in-person ratings, the underlying method is not well understood. Unlike landmarks, where we understand that the “average” male is one that most resembles the facial structure (as described by landmark coordinates) of the average male face, we are unclear as to what the “average” neural network face resembles – that is, what aspects of the face are contributing more or less to its position in feature space. While there are some ways in which we can use landmarks to describe (and visualise) shape variation, we are unaware of any straightforward way to visualise variation in feature space coordinates. (We note that we did create composite images of the top 20 participants for each trait.) While we controlled for ethnicity variables, this does not mitigate any systematic biases that may arise from imbalances in training face recognition models used by automatic landmarks and neural networks.

Given the lack of transparency behind neural network models, we suggest that researchers use caution when employing these methods. However, we also believe that neural networks are a fast, reproducible, and powerful way to extract visual information without the limitations associated with landmarks. A link to instructions and code for obtaining feature space values using neural networks is available in the full text of this paper.

Read the original paper: Zhao, A.A.Z., & Zietsch, B. (2024). Deep neural networks generate facial metrics that overcome limitations of previous methods and predict in-person attraction. Evolution & Human Behavior, 45(6), 106632.

– by Oliver Sng & Krystina Boyd-Frenkel

If you think about the last stranger you met, you will likely remember their race. But why do we care and think about others’ race? Laypersons sometimes suggest that humans have evolved to be racist. Evolutionary scholars know that the truth is far more complex. Instead, the somewhat surprising answer (at least to laypersons) is that, without modern transportation, our ancestors rarely encountered individuals who looked phenotypically different enough to qualify as a different ‘race’. As a result, natural selection couldn’t have shaped a psychology for interacting specifically with different races.

Why, then, do people care about others’ race today? One prominent answer has been that race is a cue to coalition—the groups that we work with, compete against, and generally solve life’s problems with. Our families, work colleagues, and political groups are all examples of coalitions. Considerable research has accumulated supporting the race-as-coalition perspective, including replications and re-analyses. Our recent work offers a second, complementary answer to this question: that race is a cue to ecology.

The “race-as-ecology” perspective proposes that people pay attention to and think about others’ race because people assume different races live in different (social) ecologies. In the U.S., people assume that Black individuals, relative to White individuals, live in harsher ecologies. Living in harsh ecologies – where early death from unavoidable causes like disease or violence is common – has been linked to traits including an earlier age of first reproduction, a more present-focused time perspective, and less investment in skill accumulation (e.g., education) (see two recent reviews here and here). If living in harsher ecologies influences people’s behaviors, then knowing about another person’s ecology may help us understand and predict their behavior.

We are aware of the ongoing debates in the life history literature, with important issues such as exactly why and how people respond to harsh ecologies, or whether life history “strategies” exist in our species. However, these do not necessarily affect our current work, as we focus on people’s perceptions of others, based on what people think others’ ecologies are. From our perspective, as long as (1) individuals living in harsher ecologies adopt different behaviors, and (2) certain racial groups are presumed to be living in ecologies of varying harshness, then it follows that (3) people will categorize others by their racial group because they assume different racial groups to be living in ecologies of different harshness. If so, one critical implication is that when individuals of different racial groups are presented as equally living in harsh ecologies (or not), people should care about their race less. In the presence of direct information about another’s ecology, their race isn’t useful information anymore. This is the essence of the race-as-ecology perspective.

In a set of three studies, we test these implications. Using a widely used method in the literature, sometimes referred to as the “who-said-what” method, American participants viewed photos of Black and White individuals paired with sentences presumably spoken by each person. Each Black or White person was randomly presented multiple times. After that, participants were given a surprise memory test, in which they were shown the sentences again, but now tried to remember who said each sentence. In our research, what matters are the mistakes participants make. If a participant misremembered what a Black person said as being said by a different Black person, they essentially confused the two Black individuals (a within-race confusion). However, if the participant misremembered what a Black person said as being said by a White person, they have instead confused two individuals of different races (a between-race confusion). More within-race (vs. between-race) confusions indicate that a person categorizes others by race, mentally grouping Black individuals together and White individuals together.

In our studies, half of our participants saw Black and White individuals presented with just their faces. The critical manipulation is that the other half of our participants saw the same Black and White individuals, but now presented in the ecologies/neighborhoods that they supposedly live in. Importantly, both Black and White individuals were presented evenly in both relatively harsh (or the opposite, referred to as “hopeful”) ecologies (see example photos below).

Sample race-with-ecology photos used in studies

What do we observe? First, participants categorized these individuals by their ecologies. In other words, they were more likely to confuse individuals living in harsh (or hopeful) ecologies with other individuals also living in harsh (or hopeful) ecologies. Second, participants categorized these individuals by their race, but they did so less when the Black and White individuals were shown in both harsh and hopeful ecologies. Hence, ecology information leads to the reduction of racial categorization.

Could just telling people to not pay attention to others’ race have the same effect? Past research has tried this and failed. In fact, getting people to stop thinking about race isn’t easy. The current “race-as-ecology” perspective provides insights into one way in which this can be achieved, complementing work from the “race-as-coalition” perspective.

There are puzzles that remain, and new puzzles that emerge. For example, even when race is paired with ecology, we do not see racial categorization completely disappearing (as is sometimes observed in work from the race-as-coalition perspective). So, people are still categorizing others by their race in the presence of ecology information. This suggests that other processes (like race-as-coalition) are still at play.

So, can race be “replaced”? Our answer is a partial yes. When people see others of different races, but living in different ecologies, they group others by their ecologies. In the minds of perceivers then, race is replaced by ecology. In other recent work, we also find that people hold ecology stereotypes—general beliefs about what individuals who live in harsh ecologies are like—and that these stereotypes exist across multiple societies. To the extent that people around the world think about others in terms of their ecology, there may be a range of other social categories, beyond race, that also have the potential to be “replaced” by ecology.

Read the original article: Sng, O., Boyd-Frenkel, K. A., & Williams, K. E. G. (2024). Can race be replaced? Ecology and race categorization. Evolution and Human Behavior, 45(6), 106630.

– by Marta Kowal

We might not spend much time thinking about how much we pursue beauty, yet we undoubtedly spend a lot of time actually doing it. In one of our large-scale, cross-cultural quantitative studies, 99% of our sample (93,158 participants from 93 countries) reported spending more than 10 minutes a day enhancing their physical attractiveness. These results were remarkable! Yet, after conducting that large-scale study, we couldn’t help but wonder: did we miss something? Or more precisely, what did we miss?

To explore this question, we adopted an emic approach, which emphasizes understanding cultures from an insider’s perspective. We conducted semi-structured, in-depth interviews with people from four distinct societies: the Cook Islands, Guatemala, Pakistan, and Poland. We asked three core questions: What do people do to enhance their physical attractiveness? How intensely do they engage in these behaviors? And why do they do it? This approach allowed us to go beyond predefined survey questions and uncover a richer, more nuanced understanding of beauty-enhancing behaviors around the world.

Our new study brought several fascinating findings.

First, enhancing physical attractiveness emerged as a universal behavior. Whether in Poland, Guatemala, Pakistan, or the Cook Islands, people actively engaged in practices to improve their appearance.

Second, we found a consistent gendered pattern: women spent more time than men improving their appearance, though the exact amount of time varied by culture. For instance, Cook Islander women reported typically spending 15–20 minutes a day on their appearance, while Polish and Guatemalan women reported dedicating around 30 minutes. Pakistani women topped the list, averaging 45 minutes daily. In contrast, men reported spending significantly less time—between 5–10 minutes in the Cook Islands and 15–30 minutes in Pakistan. This disparity reflects societal expectations and evolutionary pressures, as women’s physical appearance tends to play a larger role in mate selection and social evaluation across cultures.

Third, while many beauty-enhancing activities were universal—such as maintaining hygiene, hairstyling, wearing makeup, and choosing clothing—how people approached these behaviors varied widely across cultures. In Pakistan, gold jewelry and well-groomed beards were particularly emphasized, while in the Cook Islands, floral adornments held cultural significance. Poles prioritized body shape and balanced makeup, whereas Guatemalans focused on fashionable clothing.

Summarizing our findings, at its core, the drive to enhance physical appearance seems to serve two primary evolutionary purposes: competing for mates (inter-sexual competition) and outperforming rivals (intra-sexual competition). Whether it’s through a perfectly tailored outfit or shiny, well-kept hair, these efforts signal health, vitality, and desirability—traits that are universally valued.

On a more immediate level, participants identified different reasons for engaging in beauty-enhancing behaviors. Social media emerged as a powerful influence, with participants frequently citing it as both an inspiration and a source of pressure. However, motivations extended beyond digital trends.

Cultural and social norms often dictated how individuals presented themselves and the consequences of deviating from those norms. Religious beliefs also played a role, especially among Christian and Muslim participants, who mentioned spiritual motivations for enhancing their appearance. For example, a Guatemalan participant (Man11) reflected, “The way I look talks about my dad [God] in heaven.” Similarly, a Pakistani participant (Man6) explained, “We want to look good to God.” In the Cook Islands, a participant (Woman25) noted, “Because I am a bride. A bride for God, for Jesus.” These responses show how deeply cultural and religious values can influence the desire to look attractive.

One particularly striking finding came from the Cook Islands, where historical norms surrounding weight revealed a very different perspective on attractiveness. Traditionally, a man’s social status was linked to his wife’s size. A larger wife symbolized prosperity and the ability to provide. One participant (Man24) explained, “They [our fathers and grandfathers] praised themselves if their wives were big. My grandfather used to say things like, ‘I don’t want my wife to get blown away by the wind.’ … It’s hard to explain, but I’m more proud to have a big wife.” Another participant (Woman22) elaborated, “If your wife is skinny, you’re not feeding her. So, you know, it’s like a competition type of thing.”

These perspectives highlight how beauty standards are deeply rooted in cultural and historical contexts, challenging the notion of a single, universal standard of attractiveness.

While this research offers valuable insights, it’s important to acknowledge its limitations. The study relied on self-reported data, which can be influenced by memory biases and social desirability. Additionally, the four cultures studied—though diverse—don’t represent the full spectrum of human societies. Further research could explore other cultural contexts or use observational methods to provide a more nuanced understanding of beauty-enhancing behaviors.

So, why do people strive to enhance their physical attractiveness? From an evolutionary perspective, the ultimate goal is competitive advantage—whether in mate selection or social standing. However, the immediate reasons people cite often reflect cultural norms, personal preferences, and social influences. Ultimately, beauty is not just about appearances. It’s a complex interplay of biology, culture, and individual choice. The way we present ourselves to the world communicates who we are, what we value, and how we navigate our social and cultural environments.

The next time you pick out an outfit, apply makeup, or even choose a pair of shoes, you may want to take a moment to reflect. What message are you sending to the world? And how does your culture, your biology, and your personal preferences shape that message? In the end, the pursuit of beauty is as much about understanding ourselves as it is about appealing to others.

Read the original article here: Kowal, M., Sorokowski, P., Cardona, S. M., Castaňeda, A., & Faisal, C. M. N. (2024). Sex and cross-cultural comparison of self-enhancement practices: data from four distinct societies. Evolution & Human Behavior, 45(6), 106627.