Praise is not only rewarding but also informative. It can provide children with information about their competence, especially when they are uncertain or unable to judge for themselves. Not all praise is equally meaningful, however: someone who praises only high-quality work is more informative than someone who praises indiscriminately. Across four experiments, we find that 4- to 5-year-old U.S. children-from both in-person preschool and online samples-can infer the informativeness of others' praise based on the statistical dependence between praise and the quality of work evaluated. Participants were more likely to endorse praise from a teacher whose previous praise covaried with the quality of work over a teacher who praised indiscriminately or a teacher who praised only lower quality work (Experiment 1). Although children did not show a preference between teachers when seeking out praise for themselves (Experiment 2), they sought out praise from different teachers on behalf of another learner depending on the learner's goal (Experiments 3-4). Collectively, these findings show that even young children understand that praise is more than just positive reinforcement. Rather, they can reason about a speaker's inferred informativeness and use this to guide whose praise to seek out and endorse. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

Gaze following - infants' orienting towards an object attended to by their social partner - has been linked to a range of socio-cognitive skills. Despite considerable research on when infants follow the gaze of their social partners, studies have typically examined infants' following of adults' gaze. Therefore, little is known about whether or how gaze following is modulated by the characteristics of the model, such as their age. The current study examined infants' following of the gaze of an actor that varied in age: an adult, a young child, and an infant. In an eyetracking study, 49 infants, aged 6-14 months, were presented with videos in which the actor (either an adult, a child or an infant) first looked down towards a neutral point on the table, then to the participant with a friendly facial expression, and then to one of two novel objects to the left and right of the table. The age of the actor did not predict participants' gaze following behaviour, with participants following the gaze of the adult, child and same-aged peer. Thus, gaze following is not constrained to interactions with an adult. Furthermore, participants showed high interest in the actors' faces which was the strongest for the infant actor followed by the child actor, and the adult actor. These results shed insight into the interaction between infants' gaze following behaviour and their attentional preferences for different social partners. We discuss the implications of these findings for theories of development: Beyond adults, other infants and children are also perceived as interesting social partners and, potentially, valuable sources of information.

How do we learn who is good at what? Building on the idea that humans draw rich inferences from others' emotional expressions, here we ask whether others' surprised reactions to performance outcomes can elicit inferences about competence. Across three experiments, participants were asked to choose "who is better" in scenarios where two students performed identically on the same task but their teacher expressed surprise to only one of them. In Experiment 1 (n = 60, adults) and Experiment 2 (n = 90, 6- to 8-year-old children), participants' responses were modulated by not only the students' performance outcomes (success or failure) but also the teacher's response to the outcomes (surprise or no surprise). Specifically, participants preferentially chose the student who did not elicit the teacher's surprise as more competent when both students succeeded, but chose the student who elicited surprise when both failed. Experiment 3a (n = 150, 4- to 8-year-olds) replicated this pattern in 6- to 8-year-olds as a group-but not in 4- to 5-year-olds-with increasing robustness with age. Finally, this pattern was significantly reduced in Experiment 3b where the teacher's surprise was directed at an irrelevant event rather than the student's performance (n = 90, 6- to 8-year-olds). Taken together, these results suggest that even non-valenced emotional reactions to performance outcomes-being surprised at someone's success or failure-can inform inferences about valenced qualities such as competence. More broadly, the current findings demonstrate that emotional expressions we observe in our daily lives can lead to nuanced yet consequential social judgments.

Human infants show systematic responses to events that violate their expectations. Can they also revise these expectations based on others’ expressions of surprise? Here we ask whether infants (N = 156, mean = 15.2 months, range: 12.0–18.0 months) can use an experimenter’s expression of surprise to revise their own expectations about statistically probable vs. improbable events. An experimenter sampled a ball from a box of red and white balls and briefly displayed either a surprised or an unsurprised expression at the outcome before revealing it to the infant. Following an unsurprised expression, the results were consistent with prior work; infants looked longer at a statistically improbable outcome than a probable outcome. Following a surprised expression, however, this standard pattern disappeared or was even reversed. These results suggest that even before infants can observe the unexpected events themselves, they can use others’ surprise to expect the unexpected. Starting early in life, human learners can leverage social information that signals others’ prediction error to update their own predictions.

Theory of mind (ToM) reasoning refers to the process by which we reason about the mental states (beliefs, desires, emotions) of others. Here, we describe an open dataset of responses from children who completed a story booklet task for assessing ToM reasoning (n = 321 3–12-year-old children, including 64 (neurotypical) children assessed longitudinally and 68 autistic children). Children completed one of two versions of the story booklet task (Booklet 1 or 2). Both versions include two-alternative forced choice and free response questions that tap ToM concepts ranging in difficulty from reasoning about desires and beliefs to reasoning about moral blameworthiness and mistaken referents. Booklet 2 additionally includes items that assess understanding of sarcasm, lies, and second-order belief-desire reasoning. Compared to other ToM tasks, the booklet task provides relatively dense sampling of ToM reasoning within each child (Booklet 1: 41 items; Booklet 2: 65 items). Experimental sessions were video recorded and data were coded offline; the open dataset consists of children's accuracy (binary) on each item and, for many children (n = 171), transcriptions of free responses. The dataset also includes children's scores on standardized tests of receptive language and non-verbal IQ, as well as other demographic information. As such, this dataset is a valuable resource for investigating the development of ToM reasoning in early and middle childhood.

This research investigated children's and adults' understanding of the mind by assessing beliefs about the temporal features of mental states. English-speaking North American participants, varying in socioeconomic status (Study 1: N = 50 adults; Study 2: N = 112, 8- to 10-year-olds and adults; and Study 3: N = 116, 5- to 7-year-olds and adults; tested 2017-2022), estimated the duration (seconds to a lifetime) of emotions, desires (wanting), preferences (liking), and control trials (e.g., napping and having eyes). Participants were 56% female and 44% male; 32% Asian, 1% Black, 13% Hispanic/Latino, 38% White (non-Hispanic/Latino), and 16% multiracial or another race/ethnicity. Children and adults judged that preferences last longer than emotions and desires, with age differences in distinguishing specific emotions by duration ( η p 2 s > .03 ). By 5 to 7 years, ideas about the mind include consideration of time.

Capacities to understand and evaluate others’ actions are fundamental to human social life. Infants and toddlers are sensitive to the costs of others’ actions, infer others’ values from the costs of the actions they take, and prefer those who help others to those who hinder them, but it is largely unknown whether and how cost considerations inform early understanding of third-party prosocial actions. In three experiments (N = 94), we asked whether 16-month-old toddlers value agents who selectively help those who need it most. Presented with two agents who attempted two tasks, toddlers preferentially looked to and touched someone who helped the agent in greater need, both when one agent’s task required more effort and when the tasks were the same but one agent was weaker. These results provide evidence that toddlers engage in need-based evaluations of helping, applying their understanding of action utilities to their social evaluations.

Humans have an intuitive sense of what others know and how they learned it. These expectations are often latent, but violating them can elicit surprise and curiosity (e.g., a stranger knowing a lot about you). Here we investigate the development of epistemic expectations by measuring young children’s sensitivity to such violations. First, parents reported that children typically respond to violations of epistemic expectations by age 4 (Exp.1). In naturalistic dialogue experiments with 4and 5-year-olds, children were more likely to display surprised expressions and report being surprised when the experimenter’s parent knew personal information about them than when their own parent did (Exp.2). However, children showed an opposite pattern when these people knew information about the experimenter’s sibling (Exp.3). Together, these results suggest preschool-aged children are sensitive to others’ access to information and readily detect violations of their epistemic expectations in casual conversation.

A hallmark of human intelligence is the ability to understand and influence other minds. Humans engage in inferential social learning (ISL) by using commonsense psychology to learn from others and help others learn. Recent advances in artificial intelligence (AI) are raising new questions about the feasibility of human–machine interactions that support such powerful modes of social learning. Here, we envision what it means to develop socially intelligent machines that can learn, teach, and communicate in ways that are characteristic of ISL. Rather than machines that simply predict human behaviours or recapitulate superficial aspects of human sociality (e.g. smiling, imitating), we should aim to build machines that can learn from human inputs and generate outputs for humans by proactively considering human values, intentions and beliefs. While such machines can inspire next-generation AI systems that learn more effectively from humans (as learners) and even help humans acquire new knowledge (as teachers), achieving these goals will also require scientific studies of its counterpart: how humans reason about machine minds and behaviours. We close by discussing the need for closer collaborations between the AI/ML and cognitive science communities to advance a science of both natural and artificial intelligence.This article is part of a discussion meeting issue ‘Cognitive artificial intelligence’.

Do children consider how others learned when seeking help? Across three experiments, German children (N = 536 3-to-8 year olds, 49% female, majority White, tested 2017-2019) preferred to learn from successful active learners selectively by context: They sought help solving a problem from a learner who had independently discovered the solution to a previous problem over those who had learned through instruction or observation, but only when the current problem was novel, yet related, to the learners' problem (Experiment 1). Older, but not younger, children preferred the active learner even when she was offered help (Experiment 2), though only when her discovery was deliberate (Experiment 3). Although a preference to learn from successful active learners emerges early, a genuine appreciation for process beyond outcome increases across childhood.

Prior work demonstrates an early-emerging understanding of how speakers can alter listeners’ minds and actions. Yet, an abstract understanding of communication entails more than forward inferences about its influence on the listener; it also supports inverse inferences about the speaker based on its causal influence over the listener. Can children reason about the minds of speakers based on their causal influence over listeners? Across three studies, children viewed two communicative exchanges where a listener attempted to activate a toy; we manipulated when speakers communicated (Exp.1), how listeners’ subsequent actions changed (Exp.2), and whether speakers spoke or sneezed (Exp.3). By 5 years of age, children inferred the speaker who appeared to cause the listener to succeed was more knowledgeable, but only when they produced speech. These results suggest children can reason causally about the sources of communication, identifying knowledgeable speakers based on their influence over a listener’s actions and their outcomes.

Conversational AI devices are increasingly present in our lives and even used by children to ask questions, play, and learn. These entities not only blur the line between objects and agents—they are speakers (objects) that respond to speech and engage in conversations (agents)—but also operate differently from humans. Here we use a variant of a classic false-belief task to explore adults’ and children’s attributions of mental states to conversational AI versus human agents. While adults understood that two conversational AI devices, unlike two human agents, may share the same “beliefs” (Exp.1), 3to 8year-old children treated two conversational AI devices just like human agents (Exp.2); by 5 years of age, they expected the two devices to maintain separate beliefs rather than share the same belief, with hints of developmental change. Our results suggest that children initially rely on their understanding of agents to make sense of conversational AI.

Learning about the self is one of the most challenging goals that young children face. Yet, much of the prior work on early learning and curiosity has focused on children’s tendency to attend to and explore the external world. Are children actually curious about themselves? The current study examines this question by investigating whether children actively seek information about what others think of their performance. Threeto five-year-old children participated in a task where an experimenter evaluated the quality of their drawing and of another child’s drawing. Children were then left alone with a folder that contained one of these drawings (Self or Other). Children were more likely to peek inside the folder when it contained their drawing than when it contained the other child’s drawing. These preliminary findings suggest that children’s curiosity about what others think of them may emerge early in life and manifest as active information-seeking behaviors.

We present BEHAVIOR-1K, a comprehensive simulation benchmark for human-centered robotics. BEHAVIOR-1K includes two components, guided and motivated by the results of an extensive survey on "what do you want robots to do for you?". The first is the definition of 1,000 everyday activities, grounded in 50 scenes (houses, gardens, restaurants, offices, etc.) with more than 5,000 objects annotated with rich physical and semantic properties. The second is OmniGibson, a novel simulation environment that supports these activities via realistic physics simulation and rendering of rigid bodies, deformable bodies, and liquids. Our experiments indicate that the activities in BEHAVIOR-1K are long-horizon and dependent on complex manipulation skills, both of which remain a challenge for even state-of-the-art robot learning solutions. To calibrate the simulation-to-reality gap of BEHAVIOR-1K, we provide an initial study on transferring solutions learned with a mobile manipulator in a simulated apartment to its real-world counterpart. We hope that BEHAVIOR-1K's human-grounded nature, diversity, and realism make it valuable for embodied AI and robot learning research. Project website: https://behavior.stanford.edu.

Human adults can figure out what happened by combining evidence from different sensory modalities, such as vision and sound. How does the ability to integrate multi-modal information develop in early childhood? Inspired by prior computational work and behavioral studies with adults, we examined 3- to 8-year-old children's ability to reason about the physical trajectory of a ball that was dropped into an occluded Plinko box. Children had to infer in which one of three holes the ball was dropped based on visual information (i.e., where the ball landed) and auditory information (i.e., the sounds of the ball colliding with parts of the box). We compare children's responses to the predictions of four computational models. The results suggest that although even the youngest children make systematic judgments rather than randomly guessing, children's ability to integrate visual and auditory evidence continues to develop into late childhood.

Prior work suggests children understand how speech conveys information and influences others’ minds. Although these studies have focused on communication under ideal condi- tions, auditory noise plagues the real world, often corrupting the transmission of information. The current study examines how children reason about the impact of auditory noise on communication. Children (N=72, Age:3;0-5;11) watched sce- narios where a teacher tells a learner about two toys, but loud auditory noise masks one of the explanations. When asked which toy the learner wants to hear about again, children were more likely to select the noise-masked toy when the learner knew about neither toy (No Knowledge) than when he already knew about the masked toy (Partial Knowledge). However, their preference for the masked toy also increased with age in both conditions. Overall, these results demonstrate children’s developing understanding of when and how communication affects listeners’ knowledge and information-seeking behaviors.

Young children care what others think of them, but are these concerns specific to interactions with humans? Here we ask whether 4-year-old children engage in self-presentational behaviors even with a puppet. After failing to activate a toy in the presence of a puppet, children selectively demonstrated their success on the toy when the puppet was absent during their final success. This pattern was found when the puppet was treated as an agent capable of holding mental states (Exp.1), but not when it was treated as an object (Exp.2); we further explore the role of indirect, linguistic cues to the puppet's agency (Exp.3). These results highlight the importance of social contexts, particularly how an entity is depicted by others, in eliciting self-presentational behaviors. We discuss how depiction of puppets may influence their effectiveness in developmental research, and the possibility of self-presentational concerns in children's interactions with social robots and AI agents.

We care about what others think of us and often try to present ourselves in a good light. What cognitive capacities underlie our ability to think (or even worry) about reputation, and how do these concerns manifest as strategic self-presentational behaviors? Even though the tendency to modify one’s behaviors in the presence of others emerges early in life, the degree to which these behaviors reflect a rich understanding of what others think about the self has remained an open question. Bridging prior work on reputation management, communication, and theory of mind development in early childhood, here we investigate young children’s ability to infer and revise others’ mental representation of the self. Across four experiments, we find that 3- and 4-y-old children’s decisions about to whom to communicate (Experiment 1), what to communicate (Experiments 2and3), and which joint activity to engage in with a partner (Experiment 4) are systematically influenced by the partner’s observations of the children’s own past performance. Children in these studies chose to present self-relevant information selectively and strategically when it could revise the partner’s outdated, negative representation of the self. Extending research on children’s ability to engage in informative communication, these results demonstrate the sophistication of early self-presentational behaviors: Even young children can draw rich inferences about what others think of them and communicate self-relevant information to revise these representations.