Research has established that children are “naive psychologists”, adept at understanding and navigating the social world from an early age. However, most of this work has focused on how children process information that they acquire incidentally, for example by passively observing others’ actions. Here, we draw on literature framing children as intuitive scientists, who actively seek information and test hypotheses, to propose a view of children as naive experimental psychologists. From this perspective, children play an active role in selecting and pursuing relevant social information (e.g., about agents’ goals, traits, or relationships), whereby their search strategies are influenced both by context and task demands, as well as their prior beliefs, concepts, and domain-specific naive theories. We argue that the particular challenges associated with learning and reasoning about other minds may necessitate that children leverage their active learning competences, and we outline how the social domain uniquely constrains and shapes the learning process. We review existing research on social-information seeking in children and adults, and identify directions for future research, emphasizing that children’s developing social cognition should be understood in terms of the active, exploratory role they take in learning about and participating in the social world.
Social-Information Seeking in Development: The Child as Experimental Psychologist
Research has established that children are “naive psychologists,” adept at understanding and navigating the social world from an early age. However, most of this work has focused on how children process information that they acquire incidentally, for example, by passively observing others’ actions. Here, we draw on literature framing children as intuitive scientists who actively seek information and test hypotheses to propose a view of children as naive experimental psychologists. From this perspective, children play an active role in selecting and pursuing relevant social information (e.g., about agents’ goals, traits, or relationships), whereby their search strategies are influenced both by context and task demands, as well as their prior beliefs, concepts, and domain-specific naive theories. We argue that the particular challenges associated with learning and reasoning about other minds may necessitate that children leverage their active learning competences, and we outline how the social domain uniquely constrains and shapes the learning process. We review existing research on social-information seeking in children and adults and identify directions for future research, emphasizing that children’s developing social cognition should be understood in terms of the active, exploratory role they take in learning about and participating in the social world.
Mechanisms of social cognition
Social animals including humans share a range of social mechanisms that are automatic and implicit and enable learning by observation. Learning from others includes imitation of actions and mirroring of emotions. Learning about others, such as their group membership and reputation, is crucial for social interactions that depend on trust. For accurate prediction of others' changeable dispositions, mentalizing is required, i.e., tracking of intentions, desires, and beliefs. Implicit mentalizing is present in infants less than one year old as well as in some nonhuman species. Explicit mentalizing is a meta-cognitive process and enhances the ability to learn about the world through self-monitoring and reflection, and may be uniquely human. Meta-cognitive processes can also exert control over automatic behavior, for instance, when short-term gains oppose long-term aims or when selfish and prosocial interests collide. We suggest that they also underlie the ability to explicitly share experiences with other agents, as in reflective discussion and teaching. These are key in increasing the accuracy of the models of the world that we construct.
Social learning preserves both useful and useless theories by canalizing learners’ exploration
In many domains, learning from others is crucial for leveraging cumulative cultural knowledge, which encapsulates the efforts of successive generations of innovators. However, anecdotal and experimental evidence suggests that reliance on social information can reduce the exploration of the problem space. Here, we experimentally investigate the extent to which cultural transmission fosters the persistence of arbitrary solutions in a context where participants are incentivized to improve a physical system across multiple trials. Participants were exposed to various theories about the system, ranging from accurate to misleading. Our findings indicate that even under conditions conducive to exploration, the transmission of cultural knowledge canalizes learners’ focus, limiting their consideration of alternative solutions. This effect was observed in both the theories produced and the solutions attempted by participants, irrespective of the accuracy of the provided theories. These results challenge the notion that arbitrary solutions persist only when they are efficient or intuitive and underscore the significant role of cultural transmission in shaping human knowledge and technologies.
Talking with strangers is surprisingly informative
A meaningful amount of people’s knowledge comes from their conversations with others. The amount people expect to learn predicts their interest in having a conversation (pretests 1 and 2), suggesting that the presumed information value of conversations guides decisions of whom to talk with. The results of seven experiments, however, suggest that people may systematically underestimate the informational benefit of conversation, creating a barrier to talking with—and hence learning from—others in daily life. Participants who were asked to talk with another person expected to learn significantly less from the conversation than they actually reported learning afterward, regardless of whether they had conversation prompts and whether they had the goal to learn (experiments 1 and 2). Undervaluing conversation does not stem from having systematically poor opinions of how much others know (experiment 3) but is instead related to the inherent uncertainty involved in conversation itself. Consequently, people underestimate learning to a lesser extent when uncertainty is reduced, as in a nonsocial context (surfing the web, experiment 4); when talking to an acquainted conversation partner (experiment 5); and after knowing the content of the conversation (experiment 6). Underestimating learning in conversation is distinct from underestimating other positive qualities in conversation, such as enjoyment (experiment 7). Misunderstanding how much can be learned in conversation could keep people from learning from others in daily life.
Children use algorithm induction to discover patterns in data
Humans are unique in our ability to acquire diverse skills and inhabit myriad environments, but the cognitive mechanisms underlying such fast, flexible learning remain unresolved. Inspired by theories of artificial intelligence, here we show evidence for one such learning mechanism - program induction - in US American and indigenous Tsimane’ children in the Bolivian Amazon. Participants viewed novel patterns and were asked to generalize them to new stimuli, alphabets, and lengths, without feedback. Given very limited data, participants across ages, cultures, and conditions constructed response patterns that shared abstract structure with the sample patterns. Computational modeling shows that responses likely reflect discovery of latent rules, rather than simple heuristics or associations, even among children without formal schooling. The results suggest program induction serves as a domain-general learning mechanism from early in life, allowing children across cultures to rapidly infer the algorithmic structure of their natural and cultural environment, whatever it might be.
Learning by Chatting? Investigating the Impact of Generative AI on Information Seeking and Learning
Generative AI (GenAI) tools offer increasing opportunities for augmenting human cognitive tasks. Among these tasks, information seeking is being rapidly reshaped by GenAI tools, with potentially profound implications for learning and knowledge acquisition. To investigate these implications, we conducted a between-subjects field experiment in which participants pursued informal learning by seeking information through either ChatGPT or Google Search over a span of 8 days. Using a daily diary protocol, we gathered in-situ data on their information-seeking processes. Our findings show that participants in the ChatGPT group experienced diminished agency in their information-seeking processes, as they offloaded much of the information selection to AI, and consequently experienced greater meta-cognitive load arising from this reduced sense of control. We further highlight two sources of distortion in information access when using ChatGPT: biases in ChatGPT outputs, particularly towards providing solution-oriented artifacts over principled knowledge; and systematic shifts in users' information-seeking behaviors, whereby the conversational and socially-oriented interaction paradigm of current GenAI tools may inadvertently reduce exploration of the broader knowledge space. As a result, on average, participants in the ChatGPT group had worse learning outcomes than those using Google, especially for higher-order critical learning. Our work suggests inherent tensions between offloading information seeking to AI and meaningful learning, and provides broader implications for understanding AI's risks to human cognition.
Learning by Chatting? Investigating the Impact of Generative AI on Information Seeking and Learning
Generative AI (GenAI) tools offer increasing opportunities for augmenting human cognitive tasks. Among these tasks, information seeking is being rapidly reshaped by GenAI tools, with potentially profound implications for learning and knowledge acquisition. To investigate these implications, we conducted a between-subjects field experiment in which participants pursued informal learning by seeking information through either ChatGPT or Google Search over a span of 8 days. Using a daily diary protocol, we gathered in-situ data on their information-seeking processes. Our findings show that participants in the ChatGPT group experienced diminished agency in their information-seeking processes, as they offloaded much of the information selection to AI, and consequently experienced greater meta-cognitive load arising from this reduced sense of control. We further highlight two sources of distortion in information access when using ChatGPT: biases in ChatGPT outputs, particularly towards providing solution-oriented artifacts over principled knowledge; and systematic shifts in users' information-seeking behaviors, whereby the conversational and socially-oriented interaction paradigm of current GenAI tools may inadvertently reduce exploration of the broader knowledge space. As a result, on average, participants in the ChatGPT group had worse learning outcomes than those using Google, especially for higher-order critical learning. Our work suggests inherent tensions between offloading information seeking to AI and meaningful learning, and provides broader implications for understanding AI's risks to human cognition.
Not Learning from Others
We study social learning using experiments where two people independently learn relevant information and can share it to make accurate private decisions. Across three experiments, people are substantially less sensitive to information others discover than to equally-relevant information they discovered themselves. This holds when they must learn information from others through discussion; when the experimenter perfectly communicates the information; and even when participants observe others’ information with their own eyes. Our results therefore stem not from a failure to elicit information from others but a systematic tendency to underweight it relative to one’s own information. Our findings illustrate a powerful barrier to social learning that might underlie many documented cases of failure to learn from others.
Development Beyond Information: Human Becoming in the Age of AI
Abstract. Discussions of artificial intelligence in education increasingly equate development with the acquisition and deployment of information. This article argues that such accounts rest on false assumptions. Development is better understood not as an informational state but as a relational, historical process of becoming through which persons emerge and continue to change. Drawing on Vygotsky, on Ilyenkov's distinction between living and petrified ideality, and on the enactive approach to cognition, we characterise development through four features: its historicity, its dependence on reciprocal sense-making, its mediation by others who orient the learner, and its cultivation within relations of care. Large language models operate over the sedimented products of these processes without participating in them. As objectifications of human purposes, such systems may mediate human development or foreclose it. We conclude by considering what this account implies for education.
Against theory-motivated experimentation: Can random experimental choice lead to better theories?
Scientists must choose which among many experiments to perform. We study the epistemic success of experimental choice strategies proposed by philosophers of science or executed by scientists themselves. We develop a multi-agent model of the scientific process that jointly formalizes its core aspects: active experimentation, theorizing, and social learning. We find that agents who choose new experiments at random develop the most informative and predictive theories of the world. The agents aiming to confirm, falsify theories, or resolve theoretical disagreements end up with an illusion of epistemic success: they develop promising accounts for the data they collected, while misrepresenting the ground truth that they intended to learn about. Agents experimenting in these theory-motivated ways acquire less diverse or less representative samples from the ground truth that also turn out to be easier to account for. Random data collection, on the other hand, combines virtues of diverse and representative sampling from a target scientific domain which enables cumulative development of the successful theoretical accounts of it. We suggest that randomization, already a gold standard within experiments, is also beneficial at the level of experiments themselves.
Language and Experience: A Computational Model of Social Learning in Complex Tasks
The ability to combine linguistic guidance from others with direct experience is central to human development, enabling safe and rapid learning in new environments. How do people integrate these two sources of knowledge, and how might AI systems? We present a computational framework that models social learning as joint probabilistic inference over structured, executable world models given sensorimotor and linguistic data. We make this possible by turning a pretrained language model into a probabilistic model of how humans share advice conditioned on their beliefs, allowing our agents both to generate advice for others and to interpret linguistic input as evidence during Bayesian inference. Using behavioral experiments and simulations across 10 video games, we show how linguistic guidance can shape exploration and accelerate learning by reducing risky interactions and speeding up key discoveries in both humans and models. We further explore how knowledge can accumulate across generations through iterated learning experiments and demonstrate successful knowledge transfer between humans and models -- revealing how structured, language-compatible representations might enable human-machine collaborative learning.
Agentic AI Needs a Systems Theory
The endowment of AI with reasoning capabilities and some degree of agency is widely viewed as a path toward more capable and generalizable systems. Our position is that the current development of agentic AI requires a more holistic, systems-theoretic perspective in order to fully understand their capabilities and mitigate any emergent risks. The primary motivation for our position is that AI development is currently overly focused on individual model capabilities, often ignoring broader emergent behavior, leading to a significant underestimation in the true capabilities and associated risks of agentic AI. We describe some fundamental mechanisms by which advanced capabilities can emerge from (comparably simpler) agents simply due to their interaction with the environment and other agents. Informed by an extensive amount of existing literature from various fields, we outline mechanisms for enhanced agent cognition, emergent causal reasoning ability, and metacognitive awareness. We conclude by presenting some key open challenges and guidance for the development of agentic AI. We emphasize that a systems-level perspective is essential for better understanding, and purposefully shaping, agentic AI systems.
Thinking through other minds: A variational approach to cognition and culture
The processes underwriting the acquisition of culture remain unclear. How are shared habits, norms, and expectations learned and maintained with precision and reliability across large-scale sociocultural ensembles? Is there a unifying account of the mechanisms involved in the acquisition of culture? Notions such as “shared expectations,” the “selective patterning of attention and behaviour,” “cultural evolution,” “cultural inheritance,” and “implicit learning” are the main candidates to underpin a unifying account of cognition and the acquisition of culture; however, their interactions require greater specification and clarification. In this article, we integrate these candidates using the variational (free-energy) approach to human cognition and culture in theoretical neuroscience. We describe the construction by humans of social niches that afford epistemic resources called cultural affordances. We argue that human agents learn the shared habits, norms, and expectations of their culture through immersive participation in patterned cultural practices that selectively pattern attention and behaviour. We call this process “thinking through other minds” (TTOM) – in effect, the process of inferring other agents’ expectations about the world and how to behave in social context. We argue that for humans, information from and about other people's expectations constitutes the primary domain of statistical regularities that humans leverage to predict and organize behaviour. The integrative model we offer has implications that can advance theories of cognition, enculturation, adaptation, and psychopathology. Crucially, this formal (variational) treatment seeks to resolve key debates in current cognitive science, such as the distinction between internalist and externalist accounts of theory of mind abilities and the more fundamental distinction between dynamical and representational accounts of enactivism.
Attention and the Relevance Problem
ABSTRACT The relevance problem is the problem of how cognitive systems can be sensitive to what is relevant despite relevance being holistic, open‐ended, and context‐sensitive. The problem has typically been applied to higher‐order cognitive processes. In this paper, I argue that there is a hitherto unrecognized relevance problem for perceptual attention. This problem becomes apparent when considering the functions of attention: attention must not only support ongoing task performance but also remain sensitive to opportunities for task switching, latent learning, and context checking. Existing treatments of the relevance problem have largely focused on its task‐relative form: given a task, what is relevant? I argue that attention faces a more general problem that includes sensitivity to factors relevant to other interests and to cues indicating that another activity may be worth pursuing. For such features of the environment to stand out as salient, salience cannot be determined solely by bottom‐up factors and a limited set of occurrent top‐down factors, such as current goals and intentions. Rather, it must reflect the wider (if not the entire) range of an agent's latent interests in a context‐sensitive way. This motivational unencapsulation of attention poses a challenge for both contemporary accounts of attention and suggests that passive attention is much more intelligent than commonly thought.
Attention and the Relevance Problem
ABSTRACT The relevance problem is the problem of how cognitive systems can be sensitive to what is relevant despite relevance being holistic, open‐ended, and context‐sensitive. The problem has typically been applied to higher‐order cognitive processes. In this paper, I argue that there is a hitherto unrecognized relevance problem for perceptual attention. This problem becomes apparent when considering the functions of attention: attention must not only support ongoing task performance but also remain sensitive to opportunities for task switching, latent learning, and context checking. Existing treatments of the relevance problem have largely focused on its task‐relative form: given a task, what is relevant? I argue that attention faces a more general problem that includes sensitivity to factors relevant to other interests and to cues indicating that another activity may be worth pursuing. For such features of the environment to stand out as salient, salience cannot be determined solely by bottom‐up factors and a limited set of occurrent top‐down factors, such as current goals and intentions. Rather, it must reflect the wider (if not the entire) range of an agent's latent interests in a context‐sensitive way. This motivational unencapsulation of attention poses a challenge for both contemporary accounts of attention and suggests that passive attention is much more intelligent than commonly thought.
Children's Acquisition and Application of Norms
All human societies are permeated by collectively shared entities that govern daily social interactions and promote coordination and cooperation: norms. While the study of norm development is not new to developmental psychology, it has only recently been the target of an interdisciplinary wave of research using new methodologies and (often) complementary theoretical accounts to describe and explain the origins and potentially species-unique aspects of human norm psychology. Here we review recent developmental research showing that young children swiftly acquire and infer norms in a variety of social contexts. Moreover, children actively enforce these norms, even as unaffected bystanders, when third parties do things the wrong way. This research suggests that the foundations of human norm psychology can be found in early childhood. Deeper insights into the ontogenetic roots of norm psychology may contribute to understanding the evolutionary emergence of human cooperation and its maintenance in the contemporary world.