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Learning What is Where from Social Observations

Learning What is Where from Social Observations Julian Jara-Ettinger (jjara@mit.edu) Chris L. Baker (clbaker@mit.edu) Joshua B. Tenenbaum (jbt@mit.edu) Department of Brain and Cognitive Sciences, MIT Cambridge, MA 02139 Abstract Gopnik & Meltzoff, 1997). Implicitly you assume basic as- pects of rationality in the person you see crossing the street: that they want to cross safely, that they update their beliefs about the presence of oncoming cars based on what they can see, and that they plan their actions appropriately to achieve their goals given their beliefs. If they are clearly paying at- tention to the side of the street you cannot see, and they are walking across unhurriedly and unworriedly, it is then a good bet that no traffic is headed imminently toward them; if they are jumping or dashing out of the way, that is another story. Accounts of distinctively human cognition often empha- size the sophisticated representational power of people’s ToM, as in the capacity to represent arbitrary belief states, false beliefs as well as true ones, and predict how people will act accordingly. But just as or more important is the sophis- ticated inferential power of ToM: how we can learn about the contents of other agents’ mental states, or even the structure of the world, by reasoning backwards to the best explanations of agents’ observed behaviors. This kind of inverse reasoning underlies not only the traffic example above, but many other situations of practical importance for everyday cognition. For example, if you see people filing out of a new restaurant with contented looks, it is a good bet the food inside is satisfying. If you see someone enter the restaurant with an expression of eager anticipation, then exit a moment later and start looking for a different place to eat, you might guess that the restau- rant is unexpectedly closed – or perhaps he mistook it for a different place. If your friend the foodie goes far out of his way while visiting a new city to visit a particular restaurant, you can bet that place is one of the city’s best. In this paper, we present a computational model of this social-learning capacity – inferring the world’s state from ob- serving other agents’ behavior, guided by ToM. Similar in- ferential abilities have been studied in infants (Csibra, Bir´o, Ko´os, & Gergely, 2003) and adults (Goodman, Baker, & Tenenbaum, 2009), and the latter paper presented a compu- tational model similar to ours in key respects (but focused on causal learning). Our work is the first to test people’s social learning against rational model predictions in a large- scale quantitative experiment, showing that people can form surprisingly accurate fine-grained beliefs about the relative probabilities of different possible worlds from sparse social observations – just a single agent moving along a single goal- directed path of intentional action. We contrast our model with a non-intentional, non-ToM account based on low-level features of the agent’s motion. Even when we introduce many free parameters in the form of variable feature weights, Observing the actions of other people allows us to learn not only about their mental states, but also about hidden aspects of a shared environmental situation – things we cannot see, but they can, and that influence their behavior in predictable ways. This paper presents a computational model of how peo- ple can learn about the world through these social inferences, supported by the same Theory of Mind (ToM) that enables rep- resenting and reasoning about an agent’s mental states such as beliefs, desires, and intentions. The model is an extension of the Bayesian Theory of Mind (BToM) model of Baker et al. (2011), which treats observed intentional actions as the output of an approximately rational planning process and then rea- sons backwards to infer the most likely inputs to the agent’s planner – in this case, the locations and states of utility sources (potential goal objects) in the environment. We conducted a large-scale experiment comparing the world-state inferences of the BToM model and those of human subjects, given observa- tions of agents moving along various trajectories in simple spa- tial environments. The model quantitatively predicts subjects’ graded beliefs about possible world states with high accuracy – and substantially better than a non-mentalistic feature-based model with many more free parameters. These results show the power of social learning for acquiring surprisingly fine-grained knowledge about the world. Keywords: Social Cognition; Theory of Mind; Social Learn- ing; Reinforcement Learning Introduction The most obvious way to learn about the world is by direct observation. You may believe there is a Starbucks across the street from your office because you have passed it many times, and believe it is open at this moment because you just passed by a few minutes ago and saw a number of people go- ing in and out. But many aspects of the world are unobserv- able and must be inferred indirectly, often based on observing the actions of other people who know or perceive what you do not. Consider the situation of driving or biking and need- ing to turn left at an intersection onto a busy street, across oncoming traffic. Of course before turning you will check to see whether there are any cars coming down the busy street from the left, but suppose there is a large truck parked on the street, blocking your view so that you cannot see whether there is any oncoming traffic. You may inch out slowly until you can see, but you may also observe what other drivers or pedestrians are doing. If they are in a position to see the on- coming cars that you cannot, and if they are crossing the busy street at the same point you wish to turn, then it is a good bet that your turn would also be safe. Making such a judgment is literally betting your life on a mental model of another person’s cognitive processes – a Theory of Mind (ToM) (e.g. Dennett, 1987; Wellman, 1990;