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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. 

The ability to predict and understand other people’s actions is critical for real-world social behavior. Here we hypothesized that representations of social roles (e.g., cashier, mechanic, doctor) enable people to build rapid expectations about what others know and how they might act. Using a self-paced read- ing paradigm, we show that role representations support real time expectations about how other people might act (Study 1) and the knowledge they might possess (Study 2). Moreover, people reported more surprisal when the events deviated from role expectations, and they were more likely to misremember what happened. Our results suggest that roles are a powerful route for social understanding that has been previously under- studied in social cognition. 

People’s mental states constantly change as they navigate and interact with their environment. Accordingly, social reasoning requires us not only to represent mental states but also to understand the ways in which mental states tend to change. Despite their importance, relatively little is known about children’s understanding of the dynamics of mental states. To explore this question, we studied a common type of mental state change: knowledge gain. Specifically, we studied whether five- and six-year-olds distinguish between agents who gain knowledge from those who lose knowledge. In one condition, children saw an agent answer a two-alternative choice question incorrectly, followed by an identical-looking agent who answered the same question correctly (i.e., gaining knowledge). In another condition, children saw the reverse pattern (i.e., losing knowledge). Children were more likely to infer they had seen two different agents in the knowledge loss condition relative to the knowledge gain condition. These results suggest that children have intuitions about how epistemic states change and open new questions about children’s naive theories of mental state dynamics. 

Theory of Mind enables us to represent and reason about other people's mental states like beliefs and knowledge. By considering what other people know, this allows us to strategically construct believable lies. Previous work has shown that people construct lies to be consistent with others' beliefs even when those beliefs differ from their own. However, in most real world cases, we don't know everything that the other person knows. We propose that to produce believable lies, the sender considers what private information the receiver may have. Here, we develop our theory into a computational model and test it in a novel paradigm that allows us to distinguish between knowledge shared between the lie sender and receiver and knowledge private to the receiver. Our main model successfully captures how people lie in this paradigm over alternative models. Overall, our work furthers our understanding of human social cognition in adversarial situations. 

We readily get intuitions about a problem's complexity, how much thinking it will require to solve, and how long it should take, both for ourselves, and for others. These intuitions allow us to make inferences about other people's mental processing---like whether they are thinking hard, remembering, or merely mind-wandering. But where do these intuitions come from? Prior work suggests that people try solving problems themselves so as to draw inferences about another person's thinking. If we use our own thinking to build up expectations about other people, does this introduce biases into our judgments? We present a behavioral experiment testing for effects of first-person thinking speed on judgments about another person's thinking in the puzzle game Rush Hour. Although people overwhelmingly reported solving the puzzles themselves, we found no evidence for participants' thinking speeds influencing their judgments about the other person's thinking, suggesting that people can correct for first-person biases. 

Abstract Why have core knowledge? Standard answers typically emphasize the difficulty of learning core knowledge from experience, or the benefits it confers for learning about the world. Here, we suggest a complementary reason: Core knowledge is critical for learning not just about the external world, but about the mind itself. 

Abstract The discovery of a new kind of experience can teach an agent what that kind of experience is like. Such a discovery can be epistemically transformative, teaching an agent something they could not have learned without having that kind of experience. However, learning something new does not always require new experience. In some cases, an agent can merely expand their existing knowledge using, e.g., inference or imagination that draws on prior knowledge. We present a computational framework, grounded in the language of partially observable Markov Decision Processes (POMDPs), to formalize this distinction. We propose that epistemically transformative experiences leave a measurable “signature” distinguishing them from experiences that are not epistemically transformative. For epistemically transformative experiences, learning in a new environment may be comparable to “learning from scratch” (since prior knowledge has become obsolete). In contrast, for experiences that are not transformative, learning in a new environment can be facilitated by prior knowledge of that same kind (since new knowledge can be built upon the old). We demonstrate this in a synthetic experiment inspired by Edwin Abbott’s Flatland, where an agent learns to navigate a 2D world and is subsequently transferred either to a 3D world (epistemically transformative change) or to an expanded 2D world (epistemically non-transformative change). Beyond the contribution to understanding epistemic change, our work shows how tools in computational cognitive science can formalize and evaluate philosophical intuitions in new ways.