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Abstract Human social life unfolds within richly structured networks of overlapping relationships, including friendships, hierarchies, and collaborations. Yet the observable interactions that reveal these networks are often sparse and noisy, making it unclear how people could infer the latent structure of their social environments from such limited evidence. We propose that humans integrate domain-general statistical learning with domain-specific models of social structures to rapidly construct causal representations that support explanation, prediction, and planning. Across three behavioral experiments, we show that participants can infer underlying social structures (Experiment 1), predict social behavior (Experiment 2), and reason about the spread of social influence (Experiment 3), based on brief, abstract videos of social interactions. These judgments were closely captured by a computational model grounded in our account and could not be explained by simpler cue-based accounts. Statistical learning and causal reasoning operate in concert to support rapid, flexible understanding of social structures. 

Abstract As adults, we do not expect ignorant agents to behave randomly or always get things wrong. Instead, we expect them to act reasonably, guided by past experiences. We test whether 4-to-6-year-olds share this intuition and use it to infer others' knowledge, or whether they rely on a simple “ignorance = error” heuristic identified in past work. Across three pre-registered experiments (n = 264 4-to-6-year-olds recruited in the US between 2018-2022; demographic data not collected), we find that 4-year-olds expect agents to draw on past experiences when acting in new situations. However, only 6-year-olds reliably use this expectation to infer others' knowledge from behavior. These findings suggest that by age 6, children use a causal model of how ignorance shapes behavior, and not just a cue-based understanding of epistemic states. 

Abstract When determining what others know, we intuitively consider not only whether they succeed but also their probability of success in the absence of knowledge (e.g., random guessing). Across three experiments (n = 240 North American 4–6-year-olds, data collected between 2020–2023) we find that 4-year-olds understand that tasks with a lower probability of chance success are harder. However, it is not until age 6 that children use this understanding to gauge (Experiment 1) and infer (Experiments 2–3) what others know. These results suggest that, although basic probabilistic reasoning and representations of knowledge are well in place by age 4, children do not integrate the two to make mental-state inferences until much later, pointing to an area of important developmental change in Theory of Mind. 

This article reviews how humans come to understand other minds from a computational perspective. We propose that social development is structured around three abilities: (a) building representations of agents and minds from a small set of abstract primitives, (b) embedding these representations into a probabilistic causal model of rational action, and (c) using this model to interpret everyday behavior. For this third ability, we argue that using a full model of other minds is too computationally demanding. To manage this, people learn how to build simplified context-specific models that balance computational efficiency with explanatory power. Learning how to build these restricted scope models may be a central but understudied aspect of development, shaped in part through everyday conversation. All together, our framework offers a formal account of social development and highlights open questions about how this capacity emerges and develops. 

How do humans build and navigate their complex social world? Standard theoretical frameworks often attribute this success to a foundational capacity to analyze other people’s appearance and behavior to make inferences about their unobservable mental states. Here we argue that this picture is incomplete. Human behavior leaves traces in our physical environment that reveal our presence, our goals, and even our beliefs and knowledge. A new body of research shows that, from early in life, humans easily detect these traces—sometimes spontaneously—and readily extract social information from the physical world. From the features and placement of inanimate objects, people make inferences about past events and how people have shaped the physical world. This capacity develops early and helps explain how people have such a rich understanding of others: by drawing not only on how others act but also on the environments they have shaped. Overall, social cognition is crucial not only to our reasoning about people and actions but also to our everyday reasoning about the inanimate world.