We propose that humans are adapted to transfer knowledge to, and receive knowledge from, conspecifics by teaching. This adaptation, which we call 'pedagogy', involves the emergence of a special communication system that does not presuppose either language or high-level theory of mind, but could itself provide a basis facilitating the development of these human-specific abilities both in phylogenetic and ontogenetic terms. We speculate that tool manufacturing and mediated tool use made the evolution of such a new social learning mechanism necessary. However, the main body of evidence supporting this hypothesis comes from developmental psychology. We argue that many central phenomena of human infant social cognition that may seem puzzling in the light of their standard functional explanation can be more coherently and plausibly interpreted as reflecting the adaptations to receive knowledge from social partners through teaching.

We propose a view of gaze following in which infants act as Bayesian learners actively attempting to identify the operating characteristics of the systems with which they interact. We present results of an experiment in which 28 infants (average age 10 months) interacted for a 3 minute period with a non-humanoid robot. For half the infants the robot simulated contingency structure typically produced by human beings. In particular it provided causal information about the existence of a line of regard. For the other 14 infants, the robot behaved in a manner which was not contingent with the environment. We found that a few minutes of interaction with the contingent robot was sufficient to elicit statistically detectable gaze following. There were clear signs that some of these infants were actively attempting to identify whether or not the robot was responsive to them. We propose that the infant brain is equipped to learn and analyze the contingency structure of real-time social interactions. Contingency is a fundamental perceptual dimension used by infants to recognize the operational properties of humans and to generalize existing behaviors to new social partners.

The capacity for shared attention is a cornerstone of human social intelligence. Recent accounts attribute the emergence of shared attention to multiple cognitive mechanisms. Current behavioral data support an alternative dynamic systems model, but many questions remain. To answer these questions and test alternative theories, robotic models will play a critical role. Robotic models reduce the scope of the modeling task, permit comparison of empirically supported theories, and encourage parsimonious models of complex behaviors. Current efforts to model the emergence of shared attention are described. 1.

Abstract: Research on human infants, adult nonhuman primates, and children and adults in diverse cultures provides converging evidence for four systems at the foundations of human knowledge. These systems are domain specific and serve to represent both entities in the perceptible world (inanimate manipulable objects and animate agents) and entities that are more abstract (numbers and geometrical forms). Human cognition may be based, as well, on a fifth system for representing social partners and for categorizing the social world into groups. Research on infants and children may contribute both to understanding of these systems and to attempts to overcome misconceptions that they may foster.

People commonly anthropomorphize nonhuman agents, imbuing everything from computers to pets to gods with humanlike capacities and mental experiences. Although widely observed, the determinants of anthropomorphism are poorly understood and rarely investigated. We propose that people anthropomorphize, in part, to satisfy effectance motivation—the basic and chronic motivation to attain mastery of one’s environment. Five studies demonstrated that increasing effectance motivation by manipulating the perceived unpredictability of a nonhuman agent or by increasing the incentives for mastery increases anthropomorphism. Neuroimaging data demonstrated that the neural correlates of this process are similar to those engaged when mentalizing other humans. A final study demonstrated that anthropomorphizing a stimulus makes it appear more predictable and understandable, suggesting that anthropomorphism satisfies effectance motivation. Anthropomorphizing nonhuman agents seems to satisfy the basic motivation to make sense of an otherwise uncertain environment.

The capacity for shared attention is a cornerstone of human social intelligence. Recent accounts attribute the emergence of shared attention to multiple cognitive mechanisms. Current behavioral data support an alternative dynamic systems model, but many questions remain. To answer these questions and test alternative theories, robotic models will play a critical role. Robotic models reduce the scope of the modeling task, permit comparison of empirically supported theories, and encourage parsimonious models of complex behaviors. Current efforts to model the emergence of shared attention are described.

The capacity for shared attention is a cornerstone of human social intelligence. We propose that the development of shared attention depends on a proper interaction of motivational biases and contingency learning mechanisms operating in an appropriately structured environment. Atypical contingency learning leads to deficits in shared attention as seen in children with autism. To test this theory, we describe a unique research effort that combines theoretically rigorous modeling techniques using both simulated and robotic learning systems with novel empirical investigations of social learning and development in infants and toddlers with and without developmental disabilities. We believe that studying embodied learning models, whose input data (from a real or virtual caregiver) is modeled after real infant-caregiver interactions, will lead to a better understanding of the development and dysfunction of shared attention.

The capacity for shared attention is a cornerstone of human social intelligence. We propose that the development of shared attention depends on a proper interaction of motivational biases and contingency learning mechanisms operating in an appropriately structured environment. Atypical contingency learning leads to deficits in shared attention as seen in children with autism. To test this theory, we describe a unique research effort that combines theoretically rigorous modeling techniques using both simulated and robotic learning systems with novel empirical investigations of social learning and development in infants and toddlers with and without developmental disabilities. We believe that studying embodied learning models, whose input data (from a real or virtual caregiver) is modeled after real infant-caregiver interactions, will lead to a better understanding of the development and dysfunction of shared attention. 1.

ABSTRACT—In two experiments, we investigated whether 13-month-old infants expect agents to behave in a way that is consistent with information to which they have been exposed. Infants watched animations in which an animal was either provided information or prevented from gathering information about the actual location of an object. The animal then searched successfully or failed to retrieve the object. Infants ’ looking times suggest that they expected searches to be effective when—and only when—the agent had had access to the relevant information. This result supports the view that infants possess an incipient metarepresentational ability that permits them to attribute beliefs to agents. We discuss the viability of more conservative explanations and the relation between this early ability and later forms of theory of mind that appear only after children have become experienced verbal communicators. Current accounts of the conceptual competence underlying infants’ understanding of agents have emphasized infants ’ ability to represent the goal of an action (Gergely & Csibra, 2003), as well as agents ’ internal source of energy (Leslie, 1994; Luo & Baillargeon, 2005). By their first birthday, infants distinguish agents from inanimate objects (Mandler, 2004), interpret behaviors as goal directed (Baldwin & Baird, 2001; Gergely, Nádasdy,

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