Children Read Intent in Human Eyes but Not Robot Gaze, Study Reveals

Children Read Intent in Human Eyes but Not in Robots

Summary: A pioneering international study in developmental psychology and artificial intelligence has revealed that while children as young as 3 years old can instinctively read intentions and personal preferences in a human's eyes, they fail to recognize this same nonverbal communication in the gaze of a humanoid robot. By demonstrating that a robot simply imitating a human signal like gaze is insufficient to establish a true communicative bond, the findings redefine the engineering standards required for embodied AI and child-robot interaction. Key Facts * The Developmental Eye Test: Coordinated by Professor Antonella Marchetti, researchers evaluated Italian children aged 3 to 5 years old to explore how human and robotic gazes evoke different structural impressions in a child's mind. The test required children to observe either a person or a humanoid robot looking at a specific object to assess if they could deduce which item was "preferred" by the agent. * Decoding Intent vs. Mechanical Staring: The results unmasked a distinct cognitive split. Children consistently interpreted the human gaze as a meaningful, intentional signal, assuming that if a person looks at an object, they must like it. However, when a humanoid robot stared at the exact same object, the gaze was not enough for the children to attribute a true psychological preference or desire to the machine. * The Sovereignty of Child Preference: The study highlighted that while a gaze, whether human or robotic -- helps a child decode what another agent likes, it does not exert a transformative effect on the child's own personal preferences or choices. * The Failure of Isolated Mimicry: Professor Marchetti noted that simply programming a robotic artifact to imitate an isolated human signal like eye movement is not enough to make it communicative to a child. To create a functional connection, intelligent technologies designed for children must incorporate richer, developmentally appropriate interactions consisting of words, physical gestures, reciprocity, context, and shared presence. * The Mandate for Embodied AI: In the broader debate on artificial intelligence, the study emphasizes that communication is not merely about verbal output or text-based responses. To help children attribute true mental states, such as intentions and beliefs, to technology, AI must be integrated into physical, interactive systems, a dimension known as embodied AI. * The ROBIN Project Launch: These findings carry massive clinical implications for supporting children on the autism spectrum, where shared attention and gaze interpretation are vulnerable dimensions of development. To apply this data, the Don Carlo Gnocchi Foundation and Università Cattolica will launch the ROBIN project (ROBot-based Neuropsychomotor INtervention) in June 2026, using humanoid robots to promote imitation skills and socio-communicative rehabilitation. Source: Universita Cattolica del Sacro Cuore Very young children (even as young as 3 years old) can read intention and preferences in the eyes of a person, but they do not recognize this type of nonverbal communication in the gaze of a humanoid robot. This is the finding of a study published in the International Journal of Child-Computer Interaction, coordinated by Antonella Marchetti, Director of the Department of Psychology of Università Cattolica and CERITOM (Research Center on Theory of Mind and Social Competences Across the Lifespan), in collaboration with scholars from Tokyo and Osaka, and colleagues Davide Massaro, Cinzia Di Dio, and Federico Manzi of the Università Cattolica of Milan. THE STUDY The research involved Italian children aged 3 to 5 years old to explore how people and robots gaze can evoke different impressions in children's minds. The test consisted of showing to children a person and a humanoid robot while looking at an object, assessing whether they could understand which object was "preferred" by the agent looking at it. The results show that children interpret the human gaze as a meaningful signal: if an individual looks at an object, children tend to assume that the person likes that object. The same does not happen, however, when a humanoid robot is looking at the object. In that case, the gaze is not enough for children to attribute a true preference to the robot. In short, children use the human gaze to "read" desires and intentions, while they struggle to do the same with the robot. Furthermore, gaze -- human or robotic -- does not seem to change children's personal preferences: it helps them understand what the other person likes, but it does not necessarily change their own preferences. Professor Marchetti explains, "This does not mean robots cannot play an educational or social role. However, it suggests that simply imitating a single human signal, such as gaze, in a robotic artifact is not enough to make it truly communicative in a child's eyes. "Designing robots and intelligent technologies for children requires richer, more natural, and developmentally appropriate interactions: made up of words, gestures, reciprocity, context, and shared presence. This is reinforced by the fact that even human interactions alone are not sufficient to exert clear transformative effects on children's preferences. These data are particularly relevant in the debate on artificial intelligence," she continues. "Many AIs today speak, respond, and make suggestions, but our results highlight that, especially for children, communication is not just about words: presence and shared context also matter. From this perspective, an AI integrated into physical systems -- so-called embodied AI, one of the most complete expressions of which is humanoid social robotics -- represents a crucial dimension for understanding how children attribute mental states (e.g., intentions, beliefs, preferences) to technologies as well," she adds. These findings also have significant implications for applications, particularly in the field of autistic spectrum disorder, where gaze and shared attention represent crucial psychological dimensions of socio-communicative development and can be particularly vulnerable. In this context, humanoid robots are increasingly being studied as support tools for rehabilitation interventions focused on these skills. Understanding how a child interprets a robot's gaze as an intentional signal can therefore help design more targeted, natural, and developmentally sensitive interventions. The ROBIN (ROBot-based Neuropsychomotor INtervention to promote imitation skills in young children with autism spectrum disorder) project, funded by the Ministry of Health as part of the Finalized Research program, which will begin in June 2026, is also part of this research direction, the professor anticipates. The project is led by the Don Carlo Gnocchi Foundation and the CeRiToM of the Università Cattolica del Sacro Cuore, which is involved as a research group on the role of gaze and psychological processes in these forms of intervention. The project involves interventions with a humanoid robot to promote imitation skills, which also involve understanding the robot's gaze and its communicative meaning. 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 robotics and neurodevelopment research news Author: Nicola Cerbino Source: Universita Cattolica del Sacro Cuore Contact: Nicola Cerbino - Universita Cattolica del Sacro Cuore Image: The image is credited to Neuroscience News Original Research: Closed access. "Preschoolers attribute preferences in response to human but not robot gaze" by Federico Manzi, Mitsuhiko Ishikawa, Cinzia Di Dio, Shoji Itakura, Takayuki Kanda, Hiroshi Ishiguro, Davide Massaro, and Antonella Marchetti. International Journal of Child-Computer Interaction DOI:10.1016/j.ijcci.2026.100822 Abstract Preschoolers attribute preferences in response to human but not robot gaze With technological advancements, children increasingly interact with robots designed to mimic human-like behaviors for communication, among which gaze is particularly pivotal from early childhood. This study thus explores how children attribute and form preferences when exposed to human versus robotic gazes. The research involved 58 Italian children aged 3 to 5 years. They watched videos featuring a human and a robot each gazing at one of two objects. Subsequently, children were asked which object the gazer preferred (preference attribution) and to indicate their own preference (preference formation). Attribution of object preference was evaluated also as a function of children's Theory of Mind (i.e., false belief) and mental state attributions to human and robot agents. Results showed that children consistently attributed preferences based on human gaze, but not robot gaze, suggesting that they interpret human gaze as a meaningful communicative signal, likely associated with intentionality. Gaze had no significant effect on children's own preferences for either agent. Importantly, attribution of mental states to the human, but not to the robot, significantly predicted accurate preference attribution. No associations were found between performance on the false-belief task and gaze-based responses, indicating that explicit preference attribution may rely on socio-cognitive processes distinct from belief-based reasoning. These findings provide design-relevant insights for child-robot interaction, suggesting that gaze alone may not function as an effective communicative cue for young children and highlighting the importance of developmentally informed interaction strategies in robotic systems designed for early childhood.

Children trust human eyes, but not a robot's gaze

Babies stare at faces before they can do almost anything else. A parent's eyes flick toward a toy, and somehow the message lands. Look here - this is interesting. That tiny act of eye-reading is one of the first social skills a child develops. But a new study finds it has a strict limit - the eyes have to belong to a person, not a machine. Children recognize human intent Researchers worked with Italian children between three and five years old, showing them short videos of two different "watchers" looking at an object. One was a person. The other was a humanoid robot with a face and movable eyes. The setup was simple. A child saw two objects side by side, then watched either the person or the robot turn and look at just one of them. Afterward, the kids were asked which object the watcher liked best. When a person did the looking, children read it instantly as a clue. The gaze meant something. They assumed the person preferred whatever caught their eye. The robot got no such credit, even with eyes pointed at the exact same object in the exact same way. A signal that disappears The work was coordinated by Antonella Marchetti, who directs the psychology department at the Catholic University of the Sacred Heart (Università Cattolica) in Milan, alongside collaborators in Italy and Japan. Her team built on an earlier infant study showing that one-year-olds followed a robot's head turn but didn't use it to learn about objects the way they did with a person. Marchetti's group asked whether older, more verbal children would close that gap. They didn't. Even at five, kids treated the robot's stare as movement without meaning. Eyes turning toward a toy registered as a mechanical event - not a window into wanting. Reading minds, not faces The skill being tested here has a name - theory of mind, the growing ability to grasp that other people have thoughts, feelings, and desires of their own. It's why a four-year-old can tell that you want the cookie you're staring at. This is where the robot fell short. Children weren't failing to notice where it looked. Their eyes tracked the motion fine. What they didn't do was take the next step and assume a mind sat behind that motion, wanting something. A person's glance seems to carry an unspoken promise of intention. Behind those eyes, children appeared to assume, is someone who wants, likes, chooses. The robot's eyes carried no such assumption, so the look stayed empty - a pointer with nothing pointing back. Children hold onto their preferences One more result surfaced, and it applied to both watchers equally. Neither the person nor the robot managed to change what the children themselves preferred. Watching someone admire a toy helped kids understand that other person's taste. It didn't make them want the toy. Gaze worked as a reading tool, not a persuasion tool. A child could decode "she likes the red one" without suddenly wanting the red one too. Understanding and wanting stayed separate. That split is its own quiet finding. We often assume attention is contagious - that watching someone covet a thing makes us covet it. In these young children, at least with a single glance, that pull simply wasn't there. Why designers should care For anyone building robots meant to teach or comfort children, the takeaway is pointed. Giving a machine a pair of expressive eyes won't make it socially convincing. Marchetti argued that imitating a single human signal like gaze isn't enough to make a robot feel genuinely communicative to a child. What works instead, she argues, is fuller interaction - words, gestures, back-and-forth responses, the sense of a shared moment. A robot that only mimics looking is missing the thing that makes looking mean something. Presence has to come with it. Weight builds there as embodied AI moves into homes and classrooms. A growing body of research shows children read different robots in different ways, and the design choices aren't cosmetic. They decide whether a child sees a partner or a prop. What comes next Until now, it wasn't clear whether older preschoolers, with their sharper social skills, would extend mind-reading to a machine the way they do to people. This study answers that - they don't, at least not from gaze alone. Eyes need a person behind them. That finding opens a practical door, especially in therapy. Robots are being tested as tools to help children with autism spectrum disorder practice shared attention and eye contact, skills that can be genuinely hard to build. Knowing exactly how a child reads - or fails to read - a robot's gaze lets designers build interventions that actually fit how young minds work. One such effort, a robot-based program to help autistic children practice imitation, is set to begin in mid-2026. It leans directly on questions this research raises about what a robot's gaze can and can't say. The study is published in the International Journal of Child-Computer Interaction. -- - 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.