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Great apes were using touch to reassure and keep the peace six million years before humans evolved – new research

During an eight-month-long research trip to the Democratic Republic of Congo (DRC) and Zambia, we studied consolation: the way bonobos and chimpanzees comfort each other after conflicts and distress. But early on, we kept noticing something that didn’t quite fit.
The animals were exchanging a lot of friendly physical contact, embracing, touching and kissing, in competitive situations before any clear conflict or distress signals had broken out. We started wondering what all that touching was actually for.
Our new research into how great apes manage social stress suggests it could be quite important, and that this behaviour dates back at least 6 million years, long before our species evolved. Like us, our closest ape cousins, the bonobos and chimpanzees, also navigate stressful and competitive situations every day.
One example is when they discover a tree laden with ripe fruit. Everyone wants to eat and conflicts of interest can often lead to fights. But something else caught our attention. More often, apes reach out to one another with friendly touch: embracing, patting and making gentle contact.
Our colleague Edwin van Leeuwen, a Dutch biologist, had developed a feeding task to measure social tolerance across great ape groups. This task is called the peanut swing. It involves filling a bamboo trough with peanuts and swinging it over the fence so the food lands in front of the apes. This spreads peanuts evenly across a small area while researchers record who gets food, and how much. We used this test to measure whether groups could share resources peacefully in close proximity.
Because the apes watched the trough being filled, we could observe how they behaved as anticipation built. Friendly touch clustered in the minutes just before the food arrived. Previous researchers called this celebration behaviour. But discovering food is not just a cause to celebrate. It is also tense and risky.
Could friendly physical contact be helping to keep that tension at bay?
To find out, we measured a five-minute anticipation period before the food was released. We watched everything the apes did: friendly contact like kissing, embracing and patting, as well as aggression and threats. Over several months, we filmed 116 apes from five groups across 60 sessions at two African sanctuaries, Lola ya Bonobo in the DRC and Chimfunshi in Zambia.
In both species, the answer was clear. Bonobos and chimpanzees who engaged in more reassuring friendly contact before the food arrived spent significantly more time feeding peacefully alongside others afterwards. This pattern held across most groups and was strongest in the most tolerant ones. Researchers consider tolerance – the willingness to share space without aggression – to be the foundation of cooperation and social learning across the animal kingdom. Our findings suggest that pre-emptive friendly touch may be part of how that tolerance is maintained.
The patterns followed the social structure of each species. In bonobos, female-female pairs were the most likely to show friendly reassurance. This reflects how bonobo society works: females form strong alliances to counter male aggression and occupy high status positions. In chimpanzees, it was males who reassured each other most, reflecting the importance of male coalitions in their societies.
For both apes, these patterns were clearest in the more tolerant groups. In groups with more authoritarian leaders, the pattern disappeared.
Some of the most striking behaviour we saw were among the chimpanzees. During tense moments, they placed fingers, hands and other body parts inside each other’s mouths. They also held each other’s genitals. For a species known for lethal aggression, these are extraordinarily risky things to do.
Perhaps the vulnerability is itself the signal of trust. You only offer a risky contact if you are confident the other animal won’t bite. And this communicates something important about how you see the relationship.
Touch is among humanity’s oldest forms of communication. Skin-to-skin contact between infants and caregivers is one of the first channels through which attachment forms, long before language develops. A 2006 study found holding a romantic partner’s hand can reduce threat-related brain activity. A 2001 paper found that a brief touch from a stranger can increase cooperation.
Research across five European cultures published in 2015 found that the area of the body you’ll let someone touch maps almost directly onto the strength of your emotional bond with them. There’s plenty of cultural variation though. Brits are famously more guarded about casual contact than other Europeans, and touch between men in many western contexts carries particular social weight.
Perhaps most strikingly, touch before competition makes a difference. A study of negotiators found that pairs who shook hands before bargaining cooperated more, lied less and reached better joint outcomes, even in situations where cooperation came at a personal cost. A handshake may signal something important about intent before a word is even spoken.
But physical contact matters even during competition. A 2010 study of all 30 teams in the US National Basketball Association during the 2008-09 season found that the teams who touched more during games early in the season, through fist bumps, hugs and high fives, performed better and played more cooperatively later. This held even after controlling for player salaries and preseason expectations. In another 2024 study of women’s college basketball, players who received more physical contact from teammates after missing their first free throw were more likely to score on the second.
We see the same thing in genuinely high-stakes situations too. Research analysing street conflicts using CCTV found that witnessing a fight increased anxiety in bystanders. But, those who then engaged in physical contact with others showed reduced anxiety afterwards. A separate study of consolation after street robberies found that the way bystanders approached and touched victims closely mirrored the post-conflict consolation we observe in chimpanzees.
Humans, bonobos and chimpanzees all use reassuring touch but our evolutionary lineages split over 6 million years ago. The simplest explanation is that reassuring touch predates all three of us, and is certainly far older than our own species. So let’s not forget that we hold within us powerful and ancient behaviour that can help us manage our social worlds and maintain peace.![]()
Jake Brooker, Research Associate in the Department of Biology, Utrecht University and Zanna Clay, Professor in the Department of Psychology, Durham University
This article is republished from The Conversation under a Creative Commons license. Read the original article.
Monday, 27 July 2026
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People can learn to spot AI faces – but the clues are no longer obvious
Deepfake faces generated via artificial intelligence (AI) have become so realistic that they routinely fool people, with some research suggesting there may be US$40 billion worth of deepfake-related fraud annually by 2027.
Not only do most people struggle to spot AI faces, but as long ago as 2023 we discovered some AI faces are “hyperreal” – they look more real than actual human faces. We also found people are overconfident they can spot AI faces, with the most confident people making the most errors.
Software-based deepfake detectors do exist, but they can’t really explain the reasons for their detections – and they suffer from serious weaknesses. Some can be fooled simply by converting the image type, such as from png to jpg.
But it turns out most people can learn to spot AI faces with an hour or so of practice. In new research published in PNAS, we show there’s a straightforward way to improve detection of deepfakes, by training people to pick up the tell-tale clues through experience rather than direct instruction.
The difference between human and AI faces
In our early research, we discovered a key difference between AI and human faces. AI faces are hyperaverage.
This means AI faces tend to be more symmetrical, proportional and attractive than human faces. But they’re less expressive and memorable – less likely to stand out in a crowd.
Intriguingly, people can accurately and reliably judge these qualities, but frequently misinterpret the clues. For example, people often think that faces that look a bit odd are AI-generated, when in fact human faces are more likely to have distinctive, unusual features.
Although most people struggle to decide whether a face is AI or real, there is one group who are naturally good at picking up on these clues. So-called super-recognisers, who have exceptional human face perception, seem to be attuned to hyperaverageness, making them better at spotting AI faces.
This made us wonder if, for those of us who aren’t super-recognisers, AI detection abilities can be trained like other forms of perceptual expertise.
Learning to spot AI
In our first study, we invited 45 participants into our lab at the Australian National University, and asked them to rate around 100 faces on six qualities that can be used to tell AI faces apart from real ones: distinctiveness, memorability, proportionality, symmetry, attractiveness and expressiveness.
We didn’t tell participants how these clues might help them distinguish an AI face from a real one – they had to figure that part out for themselves.
We told participants which faces were AI and which were human, but we didn’t tell them that the AI faces were more symmetrical or less expressive, for example. They had to learn these clues through experience rather than direct instruction.
Before and after training, we tested participants’ ability to tell AI faces apart from human ones with new faces that were not used in the training.
Training works
In one test, participants were shown three faces – two human and one AI – and asked to select the face that was AI. On this task, average accuracy doubled from 40% before training to 80% afterwards.
Impressively, all participants improved in their AI detection abilities and several achieved close to 100% accuracy. Participants also became faster and more confident in their correct judgements.
To test the robustness of these findings, the Different Minds Lab at the University of Victoria in Canada conducted a replication of the AI detection training with Canadian participants.
The Canadian lab obtained results that were as strong as those reported in the original Australian study. This shows the training is reliable and can work for different groups of people.
The training was also just as effective when it was administered online rather than in person, which suggests it could be a cost-effective remote intervention in deepfake detection.
A promising start
But this doesn’t mean we’ve solved the AI detection problem. Our training used faces produced with one particular generative AI model, called StyleGAN3.
This is one of the most realistic face generators available, but the technology is advancing rapidly and there are many other models.
Our method has potential to adapt to new models by updating the training images and using multimedia, but we don’t yet have evidence that this will work.
The clues we found for spotting AI faces may shift for other models. And other important questions remain: do the training benefits hold up over time? Is the training effective for people of all ages, including older adults or children?
How to improve your chances of spotting AI faces
If you want to get better at recognising AI-generated faces, looking at a lot of examples is a good start. You can see plenty at websites such as Which Face Is Real or This Person Does Not Exist.
While you’re looking, bear in mind the six key factors we identified:
- how distinctive is the face?
- how memorable is it?
- how proportional is it?
- how symmetrical is it?
- how attractive is it?
- how expressive is it?
This exercise may improve your deepfake radar. But the more important takeaway is that AI deepfakes are improving very quickly – they can easily fool us, even if we think we can spot them.
The clues are no longer obvious: they are not based on specific details but on facial impressions which people form rapidly and naturally, but which can be misleading.
At the same time, there is hope. We have shown it is possible to train people to detect AI faces. By combining our human-centred approach with algorithmic detection, we may yet keep up in this cat-and-mouse game of advancing technology.
Interested in undertaking the AI face detection training? You can register here.![]()
Amy Dawel, Clinical Psychologist and Associate Professor, School of Medicine and Psychology, Australian National University; Eric Mah, Postdoctoral Researcher, Department of Psychology, University of Victoria; Jim Tanaka, Professor of Psychology, University of Victoria, and Tanya George, Research Assistant, Australian National University
This article is republished from The Conversation under a Creative Commons license. Read the original article.
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