Inferential Social Learning: How humans learn from others and help others learn

A widespread view of social learning is that humans, especially children, learn by observing, copying, and trusting others. This learner-centric perspective, however, fails to capture what is distinctive about human social learning: Even young children draw rich inferences from others’ behaviors, and communicate information to help others learn. Recent work suggests that these early-emerging abilities as learners and as teachers have common cognitive roots: Domaingeneral probabilistic inferences guided by an intuitive understanding of how people think, plan, and act. Rather than studying social learning and teaching as two distinct capacities, inferential social learning explains these processes as interpretation and generation of evidence in social contexts, painting an integrated picture of how human cognition supports acquisition and communication of abstract knowledge. (120 words) What makes human social learning so powerful and distinctive? Humans are hardly the only species that learns from others. Chimpanzees, crows, fish, and even bumblebees engage in various forms of social learning [1–5]. Yet, only humans learn from others in ways that cultivate rich repertoires of knowledge, and only humans have developed cultural institutions to promote social learning at varying scales, from parenting practices to educational systems. What makes human social learning so powerful, smart, and distinctive, and what cognitive capacities underlie this feat? While social learning---learning from others---has long been a topic of research in cognitive science and beyond, its focus has largely remained on “what learners do”: What are the behaviors that humans (especially young children) exhibit that make them successful social learners? This line of inquiry has offered several potential answers. Even infants show sensitivity to cues that signal adults’ pedagogical intent (e.g., pointing, gaze, infant-directed speech), and in the presence of such cues, they interpret information from them as generalizable knowledge (natural pedagogy [6]); young children imitate others so faithfully that they even copy actions that are causally unnecessary (overimitation [7]); children readily trust what they are told, unless the speaker had been blatantly wrong in the past (epistemic trust [8]). To explain how young children know when to copy, whom to trust, and what to generalize, existing theories have often appealed to associative processes [9] or specialized mechanisms that evolved specifically for the purpose of social learning [Box 1]. Such learner-centric perspective, however, is missing what is truly special about human social learning: We learn from those who help us learn, and we also help others learn. By focusing on the learners without regard for those who enable, facilitate, and promote others’ learning, existing research has often underappreciated the bidirectional nature of human social learning. Instead, social learning has often been characterized as one-way transmission of information, reduced to a collection of transmission biases, heuristics, or cue-sensitivities, and studied separately from the mechanisms that underlie how humans communicate, inform, and teach. This tradition has also shaped the way we think about how young children learn from others; in stark contrast to the image of “children as scientists” who are curious, driven to explore, and motivated to learn [10,11], the image of children as social learners has been more passive, credulous, and deferential [12]. Here, I present a rather different picture of social learning. This picture is not only about how we learn from others but also about how we help others learn, framed within a broader perspective on how humans think, learn, and communicate in the social context. By studying both social learning and teaching through the same theoretical lens, we can begin to understand how the two processes work together towards a common, shared epistemic goal [13,14], giving rise to a form of learning and communication that is powerful, smart, and distinctively human. Human social learning: Acquisition and communication of abstract knowledge To understand what it means to learn from others, it is useful to first consider the nature of what we know. Human knowledge is far more than a collection of adaptive behaviors or isolated facts about the world; our minds construct abstract, structured, and theory-like beliefs that reflect our understanding of the world and how to survive in it. These beliefs manifest not only as formal, scientific theories but also as intuitive (folk, naïve) theories [15–17] that are baked into every aspect of our lives---old wisdoms, new technologies, cultural norms, social conventions, and even artistic creations. Critically, their abstract contents cannot be copied or transferred directly from one mind to another [18], presenting a serious challenge for accounts that emphasize selective copying as the main vehicle of cultural transmission. They also raise questions about how social learning enables acquisition of abstract knowledge well before children can benefit from verbal instruction, or in environments where adult-to-child teaching is rare [19]. In the past decade, collaborative efforts at the intersection of developmental and computational cognitive science have made significant progress in explaining how humans learn from observed evidence. Characterizing human intelligence as a powerful inference engine, Bayesian hierarchical models have formalized learning as probabilistic inferences that operate over abstract, structured representations of the world [20]. Consistent with this idea, developmental research has identified early-emerging signatures of these representations and inferential processes; infants exhibit an abstract, theory-like knowledge in core domains---e.g., objects [21], agents [22,23], number [24], and geometry [25]---that allows them to predict future events [26], explore events that violate these predictions [27], perform actions to learn from selfgenerated evidence [28,29], and even infer the cause of their failures when such actions fail [30]. This literature has thus painted a picture of young children as curious, active learners; just like scientists, children build, test, and revise their (intuitive) theories of the how the world works by actively acting on the world and drawing inferences from self-generated evidence [10,11]. Learning, however, does not occur in isolation. Humans, especially early in life, are rarely left all by themselves. Although learning from exploration and social learning are often considered as fundamentally different ways of learning, young children’s “exploration” often unfolds in the presence of (or are even scaffolded by) their caregivers, siblings, and peers. While prior developmental studies on children’s causal inferences have rarely acknowledged the role of experimenters who present the evidence to young participants, many of them, in fact, can be reconstrued as studies on how children learn from evidence provided by adults. This insight has deep implications for what it means to have a comprehensive theory of human learning. Rather than focusing on how humans learn as individual learners, it must explain how learning occurs in a range of contexts that vary in the degree to which others are involved, from self-guided exploration (low) to explicit instruction (high). Only then can we study learning as a whole, and the ways in which humans seamlessly transition between learning (as observers, explorers, scientists, students) and facilitating others’ learning (as onlookers, communicators, demonstrators, teachers). Inferential social learning: Learning from evidence generated by others Inferential social learning (Figure 1, Key Figure) is a framework that characterizes social learning as inferential at its core; humans learn by drawing inferences from evidence generated by others (i.e., observations of and interactions with others) just like how they learn by drawing inferences from observed physical events or self-generated evidence from exploration. If “social” and “individual” learning rely on the same inferential machinery, then what differentiates the two? One key difference is the learner’s understanding of how the evidence is generated, by whom, for whom, and why (i.e., sampling process [31], see Box 2, Figure 1A). People’s goal-directed behaviors are caused by, and thus reflect, their abstract, structured, intuitive theories about the world. While naïve learners often produce noisy data due to their lack of prior knowledge and expertise, knowledgeable agents, who already have an intuitive understanding of how things work, can perform more targeted causal interventions that reflect their knowledge about the world. Thus, learning from evidence generated by these agents can be more accurate, effective, and efficient than learning from self-generated evidence. Critically, simply copying the observed behaviors would be insufficient for learning what resides in others’ minds; to acquire the abstract, structured knowledge that led others to do what they did, learners must use the observed evidence—utterances, actions, demonstrations---as information to infer the contents of others’ mental states that gave rise to those behaviors. Drawing these inferences, however, is a nontrivial feat. To learn from others’ behaviors, the learner needs a causal understanding of how and why those behaviors came to be, that is, a generative model of other minds. This model reflects our intuitive theories about how unobservable states of an agent give rise to its observable behaviors. Critically, these internal states not only incorporate the agent’s subjective mental states (i.e., goals, beliefs, desires) but also its utilities (i.e., costs and rewards). While decades of developmental research has examined children’s reasoning about others’ mental states---often referred to as Theory of Mind--more rece