CapuchinAI Brings Automated Cognitive Testing to Wild Monkeys in Their Natural Habitat

AI Automates Cognitive Testing of Wild Monkeys

Summary: Researchers developed CapuchinAI, an open-source, battery-powered platform that automates cognitive studies of wild primates using facial recognition and touchscreen interaction. Field-tested in Costa Rica's Taboga Forest Reserve, the system uses YOLO computer vision to identify wild white-faced capuchins with 97 percent accuracy, deliver personalized touchscreen tasks via a Raspberry Pi microcomputer, and dispense food rewards automatically. By merging lab-level experimental control with real-world ecological contexts, CapuchinAI offers a low-cost, scalable framework for mapping individual cognitive variations across wild animal populations without removing them from their natural habitat. Key Facts * Closed-Loop Automated Testing: CapuchinAI integrates facial recognition vision, interactive touchscreen tasks, and an automated motor-driven food dispenser into a unified, battery-operated field box running on a single Raspberry Pi. * Computer Vision Accuracy: Trained on GoPro video datasets using the open-source YOLO (You Only Look Once) architecture, the vision pipeline identified specific individual capuchins in wild environments with 97 percent accuracy. * Rapid Habituation and Learning: Wild white-faced capuchins (Cebus capucinus) in Costa Rica quickly habituated to the physical platform, learning touchscreen-reward associations spontaneously without human intervention or hand-testing. * Tailored Experimental Protocols: The software detects individual identities to deliver personalized cognitive tasks across four main domains: learning speed, impulse control, cognitive flexibility, and working or long-term memory. * Resource Management and Anti-Monopolization: The automated software tracks individual participation limits per session, preventing dominant group members from monopolizing the testing box and ensuring balanced data collection across the troop. Source: Emory University Scientists created an AI system that uses facial recognition and real-time, touchscreen testing to automate cognitive studies of capuchin monkeys in the wild. The American Journal of Primatology published a proof-of-concept for the novel method -- dubbed CapuchinAI -- developed by researchers at Emory University and Georgia Institute of Technology. The article provides a roadmap for the first scalable, systematic way to evaluate and monitor the cognitive abilities of wild primates. "The primate brain didn't evolve in a lab, it evolved in complex, competitive environments," says Marcela Benítez, Emory assistant professor of anthropology and senior author of the paper. "Yet primate cognition is rarely studied in the wild because the experimental control needed to measure cognition is difficult in unpredictable environments." "CapuchinAI" integrates a compact, battery-powered computing system into a field-research platform. The system identifies an approaching monkey, presents a learning task tailored to that individual on a touchscreen, and automatically delivers a food reward if the monkey performs the task correctly. Field tests of the prototype in the Taboga Forest Reserve of Costa Rica found that CapuchinAI identifies individual capuchins with 97% accuracy, following training on still images and videos. Wild capuchins rapidly habituated and learned touchscreen-reward associations, demonstrating that the system provides a scalable field method for cognitive testing, while also mapping individual differences across tasks. 'The minds behind the personalities' "This project builds on the legacy of Frans de Waal," says Federico Sánchez Vargas, first author of the paper and an Emory PhD student of anthropology. De Waal pioneered studies of animal cognition as director of Emory's Living Links Center for the Advanced Study of Ape and Human Evolution, while also writing best-selling books that helped popularize the field. He passed away in 2024. In addition to lab-based behavioral experiments, de Waal "gave us intimate, beautiful portraits of the lives of primates, treating them as individuals," Sánchez Vargas says. "Our AI method allows us to more deeply understand individuals that we already have data on through field observation. We can now automate cognitive testing of them and quantify the findings. It's a way of getting into the minds behind the personalities. Studying individuals in their natural environments, where there are tons of variations in their life experiences, lets us learn how environmental influences shaped them." Co-authors of the paper include Jacob Abernethy, Georgia Tech associate professor of computer science; and Sai Rakshith Potluri, a former Georgia Tech graduate research assistant who is now a software engineer at ExtraHop in Seattle. The open-source paper includes a guide to the computer coding developed for the system, along with a blueprint to build a low-tech, low-cost field-research platform and to integrate all the components into a closed-loop pipeline. The authors hope other scientists will adapt their AI method to generate cognitive data spanning different species of wild primates, living in a range of environments. Bridging lab and field Benítez' work lies at the intersection of anthropology, psychology and evolutionary biology. She studies cooperation and other social behaviors in monkeys, including a captive population of tufted capuchins in a laboratory and wild, white-faced capuchins in the Taboga Forest Reserve of northeastern Costa Rica. She is a co-director of Capuchinos de Taboga, a research project launched in 2017 in collaboration with the Universidad Nacional Técnica of Costa Rica. Experiments with animals in labs can be tightly controlled. The results, however, may be skewed since the animal is not interacting within its natural environment. Animal behavior experiments in the wild provide valid social and ecological contexts but they are challenging to design and to control. "I'm trying to bridge that gap," Benítez says. She decided to investigate the potential of AI to achieve this aim. A seed grant from Emory's AI.Humanities program launched a collaboration between Benítez and Abernethy to design an AI model for facial recognition of wild capuchins. Abernethy and Potluri used an open-source software known as YOLO (You Only Look Once) to develop a model to run on a laptop. The researchers trained the model on high-quality GoPro imagery of six wild capuchins interacting with testing platforms in Taboga. Emory and Georgia Tech undergraduates performed the labor-intensive task of digitally placing "bounding boxes" to frame the faces of the monkeys in thousands of still images and videos tagged with their identities. The result was a facial-recognition system that could identify these six capuchins with 97% accuracy from static images, video and live footage in the field. DIY ingenuity Sánchez Vargas, who joined Emory as a graduate student in 2023, took on the next phase of the AI project: figuring out how to integrate the facial-recognition model into a field-friendly, scalable computer interface that could present tasks to interacting capuchins and dispense food rewards. "The model was great at identifying six monkeys, but there are 100 capuchins at the Costa Rica field site," he says. "And we didn't have high quality video of all of these individuals, which is needed to train the model." Sánchez Vargas' undergraduate degrees are in evolutionary biology and psychology. He is not an expert computer coder, but he dove into the challenge, using Python programming language to simplify and change the parameters of the original facial-recognition model. "I essentially dumbed it down," he says, so that instead of classifying individual capuchin faces, the system recognized any capuchin monkey -- and only capuchins. The idea, he explains, was to enable the system to trigger a webcam to record video whenever a capuchin approached a computer touchscreen, rapidly generating a larger, more up-to-date dataset of faces from the interacting monkeys. The resulting videos could then be used to keep training the facial-recognition model, expanding its face-recognition repertoire. A second Python script Sánchez Vargas developed uses a program called "pygame" to facilitate interactive stimuli for use in games or cognitive testing. The researchers created a simple stimulus to habituate the monkeys to the system: a blue-square covering the computer touchscreen that records when a capuchin touches it. At the monkey's touch, the script signals a motor circuit to dispense a food reward. The two Python scripts are integrated to run simultaneously on a Raspberry Pi, a tiny computer about half the size of an iPhone. The entire system can run for eight hours on a lightweight battery pack before it needs recharging. "It was important to us that our technology be both low cost and ecologically friendly," Sánchez Vargas says. Build it and they will come The next challenge was to create a wildlife-proof, weather-proof platform to house all the components of the system. "We built it in my garage," Benítez says. She and Sánchez Vargas bought planks of pine, deck sealant, rubber insulating strips and plastic piping from Home Depot. "A person helping us asked, 'What is the project you're working on?'" Benítez says. "We didn't go into it; it would have been a bit complicated to explain." Emory TechLab helped Sánchez Vargas create a 3D printed, plastic food dispenser. The two researchers put together a wooden box, only about 20 inches tall, to house the system's components: a webcam, a computer touchscreen, the Raspberry Pi, the food dispenser and a rotary motor to power the dispenser. "It was a lot of work but also a lot of fun," Sánchez Vargas says. "One of the best parts of working in animal cognition is getting creative, trying to put yourself into the mind of the animal you're studying, so you can develop a good way to engage them in experiments." Finally, it was time to test their creation in the field. In the field with dreams Upon arriving at the Capuchinos de Taboga research facility in Costa Rica, Sánchez Vargas remembers feeling a few last-minute jitters. Would the prototype function as envisioned? "We had to get up super early, at four in the morning," he says, "and drive a long way down this bumpy road to get to the research site." The team secured the CapuchinAI box to a platform, loaded its food dispenser with dried slices of forest banana, and waited nearby. "For the first couple of days that we did this, no capuchins visited," Sánchez Vargas says. "I was beginning to worry." The third day, however, a large male capuchin named Trompudo, which means "big snout," could not resist the scent of banana. "He climbs on the box and starts slapping the back of it," Sánchez Vargas recalls. "Finally, he slaps the touchscreen and a banana slice pops out." Trompudo gobbled up the food then put his hand on the screen again. Another banana slice popped out. "It was almost like you could see him realizing, 'Ah, that's what you have to do, touch the screen!'" Sánchez Vargas says. "It was amazing to watch an individual learn something so quickly." Different learning styles After Tompudo broke the ice, more monkeys began engaging with CapuchinAI. "Even I was surprised by their enthusiasm," Sánchez Vargas says. The researchers are already noting individual differences within the 16 monkeys who engaged with the CapuchinAI prototype during the pilot phase. Some capuchins are remarkably fast learners. Others take more time to figure out that they need to touch the screen to get a food reward. Individuals who investigated the box with their lips learned to kiss the screen to get a banana slice. Then there are the late adopters. They hang back and watch their friends interact with the box, seeing how the apparatus works before approaching it. The box stood up to the occasional aggressive moves of capuchins trying to bust it open. The facial-recognition software prevents the system from activating when other wildlife approach, but some animals still try to tinker with the box, including coatis. Members of the raccoon family, coatis are notorious for their ability to break into manmade containers and even houses. The box passed the coati test. The research team is now updating its facial-recognition model, training it on the recorded videos of the 16 capuchins from the pilot phase. They are developing cognitive experiments for four broad domains of cognition, including tests of individual capuchins' ability to learn, their level of impulse control, their cognitive flexibility, and their skill at both short- and long-term memory. If the model recognizes a capuchin it was trained on, it will present a specific cognitive test on the touchscreen, depending on what "level" an individual is at in the testing sequence. If the individual is unfamiliar, the model will assign the baseline habituation stimulus -- touch the screen to get a reward. Machine vision allows the model to quickly shift tests from one identified capuchin to another, so multiple individuals can participate. The system is also programmed to limit the number of food rewards an individual can receive during a session, discouraging a dominant capuchin from monopolizing the platform. A powerful new tool The researchers can draw from decades of accumulated observational data on the life histories of individuals in the capuchin population of Taboga Forest Reserve. CapuchinAI allows them to learn how variations in the capuchins' lives may have influenced their cognition. "We can explore outstanding questions about how the environment, individual experiences and behaviors connect to cognitive abilities," Benítez says. "Why are some individuals better at some tasks than others? How do different individuals adapt to different situations? How do different cognitive strategies relate to fitness?" "Frans de Waal said that you cannot study cognition if you don't understand the animals," adds Sánchez Vargas. "Our AI methodology doesn't replace the need for human researchers in the field. It's essential to have rich, observational datasets gathered by people working on the ground." The unusually large brains of primates and their advanced cognition compared to other animals make them key models for the study of how brains and minds evolve and adapt. The researchers believe their AI model is adaptable to other species of wild primates living in the wild for which scientists have recorded individual life histories. "It's a powerful new tool in the primatology toolkit," Benítez says. Funding: In addition to seed funding from AI.Humanities, the project was supported by the National Institute on Drug Abuse of the U.S. National Institutes of Health (R34DA061925), the U.S. National Science Foundation (BCS-2127373), the Lewis and Clark Fund for Exploration and Field Research; and the Emory Center for Mind, Brain and Culture. Key Questions Answered: Editorial Notes: * This article was edited by a Neuroscience News editor. * Journal paper reviewed in full. * Additional context added by our staff. About this neurodevelopment and mental health research news Author: Carol Clark Source: Emory University Contact: Carol Clark - Emory University Image: The image is credited to the researchers Original Research: Open access. "CapuchinAI 1.0: Development of a Machine Learning-Based Touchscreen Paradigm to Test Cognition in Wild Capuchins" by Federico Sánchez Vargas, Sai Rakshith Potluri, Jacob Abernethy, Marcela E. Benítez. American Journal of Primatology DOI:10.1002/ajp.70194 Abstract CapuchinAI 1.0: Development of a Machine Learning-Based Touchscreen Paradigm to Test Cognition in Wild Capuchins Advancing the study of primate cognition requires methods that preserve ecological validity while enabling the experimental control typical of laboratory research. We introduce CapuchinAI v1.0., a field-deployable touchscreen system currently integrating real-time species recognition with automated cognitive testing, providing a novel methodology for studying cognition in wild primates. Our approach combines an adapted version of a high-performing YOLOv7-based facial recognition model (MultipleCapuchins) with a portable Raspberry Pi touchscreen-reward apparatus designed for automated operation in natural habitats. The system detects approaching capuchins, initiates video recording, presents stimuli (in our initial deployment, a blue screen that rewards all touches), and dispenses food rewards. During a 2-week presentation to two habituated groups of wild white-faced capuchins (Cebus imitator) at the Taboga Forest Reserve, 16 individuals voluntarily interacted with the apparatus, 10 triggered rewards, and 8 formed and retained robust screen-reward associations. The rapid habituation and learning rates demonstrate the feasibility of deploying AI-mediated cognitive experiments in the wild. Ongoing development of CapuchinAI aims to address several long-standing challenges in field cognition research, with the goal of enabling: (1) autonomous, individualized task administration without researcher intervention; (2) standardized, repeatable trials across individuals and sessions; (3) scalable deployment across groups and sites; and (4) parallel data collection on behavior, identity, and performance. This methodology provides a blueprint for integrating machine learning and touchscreen testing to study within- and between-individual cognitive variation under natural conditions. CapuchinAI represents a significant step toward long-term comparative research on primate cognition, bridging the gap between lab and field.

New CapuchinAI system automates cognitive testing of wild primates

Scientists created an AI system that uses facial recognition and real-time, touchscreen testing to automate cognitive studies of capuchin monkeys in the wild. The American Journal of Primatology published a proof-of-concept for the novel method - dubbed CapuchinAI - developed by researchers at Emory University and Georgia Institute of Technology. The article provides a roadmap for the first scalable, systematic way to evaluate and monitor the cognitive abilities of wild primates. The primate brain didn't evolve in a lab, it evolved in complex, competitive environments. Yet primate cognition is rarely studied in the wild because the experimental control needed to measure cognition is difficult in unpredictable environments." Marcela Benítez, Emory assistant professor of anthropology and senior author of the paper "CapuchinAI" integrates a compact, battery-powered computing system into a field-research platform. The system identifies an approaching monkey, presents a learning task tailored to that individual on a touchscreen, and automatically delivers a food reward if the monkey performs the task correctly. Field tests of the prototype in the Taboga Forest Reserve of Costa Rica found that CapuchinAI identifies individual capuchins with 97% accuracy, following training on still images and videos. Wild capuchins rapidly habituated and learned touchscreen-reward associations, demonstrating that the system provides a scalable field method for cognitive testing, while also mapping individual differences across tasks. 'The minds behind the personalities' "This project builds on the legacy of Frans de Waal," says Federico Sánchez Vargas, first author of the paper and an Emory PhD student of anthropology. De Waal pioneered studies of animal cognition as director of Emory's Living Links Center for the Advanced Study of Ape and Human Evolution, while also writing best-selling books that helped popularize the field. He passed away in 2024. In addition to lab-based behavioral experiments, de Waal "gave us intimate, beautiful portraits of the lives of primates, treating them as individuals," Sánchez Vargas says. "Our AI method allows us to more deeply understand individuals that we already have data on through field observation. We can now automate cognitive testing of them and quantify the findings. It's a way of getting into the minds behind the personalities. Studying individuals in their natural environments, where there are tons of variations in their life experiences, lets us learn how environmental influences shaped them." Co-authors of the paper include Jacob Abernethy, Georgia Tech associate professor of computer science; and Sai Rakshith Potluri, a former Georgia Tech graduate research assistant who is now a software engineer at ExtraHop in Seattle. The open-source paper includes a guide to the computer coding developed for the system, along with a blueprint to build a low-tech, low-cost field-research platform and to integrate all the components into a closed-loop pipeline. The authors hope other scientists will adapt their AI method to generate cognitive data spanning different species of wild primates, living in a range of environments. Bridging lab and field Benítez' work lies at the intersection of anthropology, psychology and evolutionary biology. She studies cooperation and other social behaviors in monkeys, including a captive population of tufted capuchins in a laboratory and wild, white-faced capuchins in the Taboga Forest Reserve of northeastern Costa Rica. She is a co-director of Capuchinos de Taboga, a research project launched in 2017 in collaboration with the Universidad Nacional Técnica of Costa Rica. Experiments with animals in labs can be tightly controlled. The results, however, may be skewed since the animal is not interacting within its natural environment. Animal behavior experiments in the wild provide valid social and ecological contexts but they are challenging to design and to control. "I'm trying to bridge that gap," Benítez says. She decided to investigate the potential of AI to achieve this aim. A seed grant from Emory's AI.Humanities program launched a collaboration between Benítez and Abernethy to design an AI model for facial recognition of wild capuchins. Abernethy and Potluri used an open-source software known as YOLO (You Only Look Once) to develop a model to run on a laptop. The researchers trained the model on high-quality GoPro imagery of six wild capuchins interacting with testing platforms in Taboga. Emory and Georgia Tech undergraduates performed the labor-intensive task of digitally placing "bounding boxes" to frame the faces of the monkeys in thousands of still images and videos tagged with their identities. The result was a facial-recognition system that could identify these six capuchins with 97% accuracy from static images, video and live footage in the field. DIY ingenuity Sánchez Vargas, who joined Emory as a graduate student in 2023, took on the next phase of the AI project: figuring out how to integrate the facial-recognition model into a field-friendly, scalable computer interface that could present tasks to interacting capuchins and dispense food rewards. "The model was great at identifying six monkeys, but there are 100 capuchins at the Costa Rica field site," he says. "And we didn't have high quality video of all of these individuals, which is needed to train the model." Sánchez Vargas' undergraduate degrees are in evolutionary biology and psychology. He is not an expert computer coder, but he dove into the challenge, using Python programming language to simplify and change the parameters of the original facial-recognition model. "I essentially dumbed it down," he says, so that instead of classifying individual capuchin faces, the system recognized any capuchin monkey - and only capuchins. The idea, he explains, was to enable the system to trigger a webcam to record video whenever a capuchin approached a computer touchscreen, rapidly generating a larger, more up-to-date dataset of faces from the interacting monkeys. The resulting videos could then be used to keep training the facial-recognition model, expanding its face-recognition repertoire. A second Python script Sánchez Vargas developed uses a program called "pygame" to facilitate interactive stimuli for use in games or cognitive testing. The researchers created a simple stimulus to habituate the monkeys to the system: a blue-square covering the computer touchscreen that records when a capuchin touches it. At the monkey's touch, the script signals a motor circuit to dispense a food reward. The two Python scripts are integrated to run simultaneously on a Raspberry Pi, a tiny computer about half the size of an iPhone. The entire system can run for eight hours on a lightweight battery pack before it needs recharging. "It was important to us that our technology be both low cost and ecologically friendly," Sánchez Vargas says. Build it and they will come The next challenge was to create a wildlife-proof, weather-proof platform to house all the components of the system. "We built it in my garage," Benítez says. She and Sánchez Vargas bought planks of pine, deck sealant, rubber insulating strips and plastic piping from Home Depot. "A person helping us asked, 'What is the project you're working on?'" Benítez says. "We didn't go into it; it would have been a bit complicated to explain." Emory TechLab helped Sánchez Vargas create a 3D printed, plastic food dispenser. The two researchers put together a wooden box, only about 20 inches tall, to house the system's components: a webcam, a computer touchscreen, the Raspberry Pi, the food dispenser and a rotary motor to power the dispenser. "It was a lot of work but also a lot of fun," Sánchez Vargas says. "One of the best parts of working in animal cognition is getting creative, trying to put yourself into the mind of the animal you're studying, so you can develop a good way to engage them in experiments." Finally, it was time to test their creation in the field. In the field with dreams Upon arriving at the Capuchinos de Taboga research facility in Costa Rica, Sánchez Vargas remembers feeling a few last-minute jitters. Would the prototype function as envisioned? "We had to get up super early, at four in the morning," he says, "and drive a long way down this bumpy road to get to the research site." The team secured the CapuchinAI box to a platform, loaded its food dispenser with dried slices of forest banana, and waited nearby. "For the first couple of days that we did this, no capuchins visited," Sánchez Vargas says. "I was beginning to worry." The third day, however, a large male capuchin named Trompudo, which means "big snout," could not resist the scent of banana. "He climbs on the box and starts slapping the back of it," Sánchez Vargas recalls. "Finally, he slaps the touchscreen and a banana slice pops out." Trompudo gobbled up the food then put his hand on the screen again. Another banana slice popped out. "It was almost like you could see him realizing, 'Ah, that's what you have to do, touch the screen!'" Sánchez Vargas says. "It was amazing to watch an individual learn something so quickly." Different learning styles After Tompudo broke the ice, more monkeys began engaging with CapuchinAI. "Even I was surprised by their enthusiasm," Sánchez Vargas says. The researchers are already noting individual differences within the 16 monkeys who engaged with the CapuchinAI prototype during the pilot phase. Some capuchins are remarkably fast learners. Others take more time to figure out that they need to touch the screen to get a food reward. Individuals who investigated the box with their lips learned to kiss the screen to get a banana slice. Then there are the late adopters. They hang back and watch their friends interact with the box, seeing how the apparatus works before approaching it. The box stood up to the occasional aggressive moves of capuchins trying to bust it open. The facial-recognition software prevents the system from activating when other wildlife approach, but some animals still try to tinker with the box, including coatis. Members of the raccoon family, coatis are notorious for their ability to break into manmade containers and even houses. The box passed the coati test. The research team is now updating its facial-recognition model, training it on the recorded videos of the 16 capuchins from the pilot phase. They are developing cognitive experiments for four broad domains of cognition, including tests of individual capuchins' ability to learn, their level of impulse control, their cognitive flexibility, and their skill at both short- and long-term memory. If the model recognizes a capuchin it was trained on, it will present a specific cognitive test on the touchscreen, depending on what "level" an individual is at in the testing sequence. If the individual is unfamiliar, the model will assign the baseline habituation stimulus - touch the screen to get a reward. Machine vision allows the model to quickly shift tests from one identified capuchin to another, so multiple individuals can participate. The system is also programmed to limit the number of food rewards an individual can receive during a session, discouraging a dominant capuchin from monopolizing the platform. A powerful new tool The researchers can draw from decades of accumulated observational data on the life histories of individuals in the capuchin population of Taboga Forest Reserve. CapuchinAI allows them to learn how variations in the capuchins' lives may have influenced their cognition. "We can explore outstanding questions about how the environment, individual experiences and behaviors connect to cognitive abilities," Benítez says. "Why are some individuals better at some tasks than others? How do different individuals adapt to different situations? How do different cognitive strategies relate to fitness?" "Frans de Waal said that you cannot study cognition if you don't understand the animals," adds Sánchez Vargas. "Our AI methodology doesn't replace the need for human researchers in the field. It's essential to have rich, observational datasets gathered by people working on the ground." The unusually large brains of primates and their advanced cognition compared to other animals make them key models for the study of how brains and minds evolve and adapt. The researchers believe their AI model is adaptable to other species of wild primates living in the wild for which scientists have recorded individual life histories. "It's a powerful new tool in the primatology toolkit," Benítez says. In addition to seed funding from AI.Humanities, the project was supported by the National Institute on Drug Abuse of the U.S. National Institutes of Health (R34DA061925), the U.S. National Science Foundation (BCS-2127373), the Lewis and Clark Fund for Exploration and Field Research; and the Emory Center for Mind, Brain and Culture. Source: Emory University Journal reference: Vargas, F. S., et al. (2026) CapuchinAI 1.0: Development of a Machine Learning-Based Touchscreen Paradigm to Test Cognition in Wild Capuchins. American Journal of Primatology. DOI: 10.1002/ajp.70194. https://onlinelibrary.wiley.com/doi/10.1002/ajp.70194

Wild monkeys are taking AI-powered intelligence tests in the jungle

A wooden box bolted to a platform in a Costa Rican forest can spot a capuchin monkey's face and choose a puzzle suited to that individual. Get the answer right, and a slice of banana pops out. Nobody has to stand there and run the test. Wild primates are usually studied one of two ways: in controlled laboratory settings or watched loose in the trees with almost no experimental control. Researchers at Emory University and the Georgia Institute of Technology built a new system to close that gap. Marcela Benítez, an assistant professor of anthropology at Emory, led the project, which the team calls CapuchinAI. She has spent years studying monkey behavior both in captivity and in the wild, and she wanted a way to bring lab-grade precision into the forest itself. "The primate brain didn't evolve in a lab, it evolved in complex, competitive environments," said Benítez. "Yet primate cognition is rarely studied in the wild because the experimental control needed to measure cognition is difficult in unpredictable environments." Teaching AI to recognize monkeys The team first needed a computer that could tell one capuchin from another. They adapted an open-source facial-recognition program called YOLO. Then they trained it on thousands of close-up photos and videos of six adult male capuchins living at the Taboga Forest Reserve. Undergraduates at both universities spent hours drawing boxes around each monkey's face in old field footage and tagging every image with the correct name. The team tested the finished model on pictures it had never seen, some blurred or lit differently than the training photos. It detected a capuchin's face in the frame 98 percent of the time, and when it did, it identified the correct monkey 97 percent of the time. A lab in a wooden box Federico Sánchez Vargas, an Emory PhD student in anthropology and the paper's first author, took on the job of getting that software into the forest. His own degrees are in evolutionary biology and psychology, not computer science. Still, he taught himself enough Python to strip the recognition model down so it would flag any capuchin face, not only the six it already knew. "The model was great at identifying six monkeys, but there are 100 capuchins at the Costa Rica field site," noted Sánchez Vargas. "And we didn't have high-quality video of all of these individuals, which is needed to train the model." He and Benítez then built the housing themselves, in her garage, with pine planks, deck sealant, rubber insulating strips and plastic piping from Home Depot. "We built it in my garage," Benítez said. The finished box stands about 20 inches tall and weighs roughly 35 pounds (15.9 kilograms). A small Raspberry Pi computer inside runs a webcam, a touchscreen and a 3D-printed food dispenser for at least eight hours on ordinary travel battery packs. The monkey that figured it out The team waited three days in the field before any capuchin approached the box at all. Then a large male named Trompudo, Spanish for "big snout," couldn't resist the smell of dried banana packed inside. "He climbs on the box and starts slapping the back of it," Sánchez Vargas says. "Finally, he slaps the touchscreen and a banana slice pops out." Trompudo ate it, touched the screen again, and got another slice. "It was almost like you could see him realizing, 'Ah, that's what you have to do, touch the screen!'" Sánchez Vargas said. "It was amazing to watch an individual learn something so quickly." "Other capuchins followed his lead faster than the team expected. "Even I was surprised by their enthusiasm." Not every monkey caught on Over six sessions with two habituated groups, 16 capuchins interacted with the box while 14 more watched from a distance without touching it. Of those 16, ten learned to trigger a reward by touching the screen. Eight built a full habit of walking up, touching, and expecting banana, and five still remembered that habit a week after the box was removed. The monkeys didn't all learn the same way. Some caught on almost immediately. Some hung back and watched their groupmates first. Others investigated the box with their lips instead of their hands, learning to press their mouths to the screen for a reward. The touchscreen only pays out when its camera confirms a capuchin face is present. When coatis, tree-climbing relatives of raccoons known for breaking into houses, tried to get inside, they got nothing and lost interest. Learning more about monkey cognition The team is now training an upgraded model to recognize all 16 pilot monkeys individually. Around it, they are building four categories of cognitive tests: learning speed, impulse control, flexibility when rules change, and short- and long-term memory. A known monkey will get whichever task fits its progress. An unfamiliar monkey still gets the basic touch-for-banana lesson while a camera records its face to learn it. Nobody yet knows whether a monkey that learns fast on one task also learns fast on the others, or whether how it grew up made the difference. The next generation of field research "We can explore outstanding questions about how the environment, individual experiences and behaviors connect to cognitive abilities," Benítez said. "Why are some individuals better at some tasks than others? How do different individuals adapt to different situations? How do different cognitive strategies relate to fitness?" Sánchez Vargas ties the work back to Frans de Waal, the Emory primatologist who spent decades studying individual animal personalities before his death in 2024. "Frans de Waal said that you cannot study cognition if you don't understand the animals," he said. "Our AI methodology doesn't replace the need for human researchers in the field. It's essential to have rich, observational datasets gathered by people working on the ground." The full study was published in the journal American Journal of Primatology. -- - Like what you read? Subscribe to our newsletter for engaging articles, exclusive content, and the latest updates. Check us out on EarthSnap, a free app brought to you by Eric Ralls and Earth.com.

AI isn't just being forced on us. Even forest monkeys are now a test audience in the name of science

Researchers from Emory University have built an AI-powered touchscreen that rewards wild capuchin monkeys with banana slices, helping them study how they think, learn, and solve problems in their natural habitat. You didn't ask for an AI summary of your inbox or a chatbot in your camera app, but you got it anyway. The same thing is happening to the monkeys of Costa Rica's Taboga Forest Reserve. According to a New York Times report, researchers have handed them a touchscreen system that uses artificial intelligence to study how wild capuchins think and learn without ever taking them out of their natural habitat. Even monkeys can't escape AI now Unlike the AI features popping up across our apps, this one actually has a scientific purpose. The device, called CapuchinAI, is a portable testing station built by Emory University researchers. It combines a touchscreen, a webcam, and a 3D-printed food dispenser inside a wooden frame. Facial recognition software trained on images of Taboga's resident capuchins identifies an approaching monkey and presents a simple challenge: touch the screen to earn a piece of dried banana. According to the study, the researchers waited two days before any capuchin approached the device. On the third day, an alpha male the researchers named Papi showed up, and before long he learned the pattern. Eventually, ten of the sixteen capuchins who interacted with the station figured out the task, and at least eight remembered how it worked during later sessions. Recommended Videos Dr. Marcela Benítez, a primatologist at Emory University, said the goal is to observe how primates solve problems under the same pressures they experience in the wild, something a lab setting can't replicate. The AI upgrade is already on the way This summer, the team plans to test an upgraded model that identifies individual capuchins instead of simply recognizing the species. The new system will also run tests for impulse control and cognitive flexibility, tracking each monkey's progress the way a game tracks a player's stats. It's a more interactive step up from the facial recognition software previously used to identify individual chimpanzees in the wild, since CapuchinAI pairs recognition with a live reward loop. Most of us know AI as the thing that keeps appearing on our phones, browsers, and inboxes, whether we want it or not. Now it's showing up in the Costa Rican rainforest too. The difference here is that these new users are wild capuchins tapping a screen for banana slices, all in the name of science.