@article {3513, title = {Learning physical parameters from dynamic scenes.}, journal = {Cognitive Psychology}, volume = {104}, year = {2018}, month = {8/2018}, pages = {57-82}, abstract = {

Humans acquire their most basic physical concepts early in development, and continue to enrich and expand their intuitive physics throughout life as they are exposed to more and varied dynamical environments. We introduce a hierarchical Bayesian framework to explain how people can learn physical parameters at multiple levels. In contrast to previous Bayesian models of theory acquisition (Tenenbaum et al., 2011), we work with more ex- pressive probabilistic program representations suitable for learning the forces and properties that govern how objects interact in dynamic scenes unfolding over time. We compare our model to human learners on a challenging task of estimating multiple physical parameters in novel microworlds given short movies. This task requires people to reason simultane- ously about multiple interacting physical laws and properties. People are generally able to learn in this setting and are consistent in their judgments. Yet they also make systematic errors indicative of the approximations people might make in solving this computationally demanding problem with limited computational resources. We propose two approximations that complement the top-down Bayesian approach. One approximation model relies on a more bottom-up feature-based inference scheme. The second approximation combines the strengths of the bottom-up and top-down approaches, by taking the feature-based inference as its point of departure for a search in physical-parameter space.

}, keywords = {intuitive physics, intuitive theory, learning, physical reasoning, probabilistic inference}, doi = {10.1016/j.cogpsych.2017.05.006}, url = {https://www-sciencedirect-com.libproxy.mit.edu/science/article/pii/S0010028517301822}, author = {Ullman, Tomer D. and Stuhlm{\"u}ller, Andreas and Noah D. Goodman and Joshua B. Tenenbaum} } @article {2736, title = {Changing minds: Children{\textquoteright}s inferences about third party belief revision}, journal = {Developmental Science}, year = {2017}, month = {05/2017}, pages = {e12553}, abstract = {

By the age of five, children explicitly represent that agents can have both true and false beliefs

based on epistemic access to information (e.g., Wellman, Cross, \& Watson, 2001). Children also begin to understand that agents can view identical evidence and draw different inferences from it (e.g., Carpenter \& Chandler, 1996). However, much less is known about when, and under what conditions, children expect other agents to change their minds. Here, inspired by formal ideal observer models of learning, we investigate children{\textquoteright}s expectations of the dynamics that underlie third parties{\textquoteright} belief revision. We introduce an agent who has prior beliefs about the location of a population of toys and then observes evidence that, from an ideal observer perspective, either does, or does not justify revising those beliefs. We show that children{\textquoteright}s inferences on behalf of third parties are consistent with the ideal observer perspective, but not with a number of alternative possibilities, including that children expect other agents to be influenced only by their prior beliefs, only by the sampling process, or only by the observed data. Rather, children integrate all three factors in determining how and when agents will update their beliefs from evidence.\ 

}, keywords = {learning, rational action, theory of mind}, doi = {10.1111/desc.12553}, author = {Rachel Magid and Phyllis Yan and Max Siegel and Joshua B. Tenenbaum and Laura Schulz} } @article {2653, title = {Monkeys head-gaze following is fast, precise and not fully suppressible.}, journal = {Proc Biol Sci}, volume = {282}, year = {2015}, month = {2015 Oct 07}, pages = {20151020}, abstract = {

Human eye-gaze is a powerful stimulus, drawing the observer{\textquoteright}s attention to places and objects of interest to someone else ({\textquoteright}eye-gaze following{\textquoteright}). The largely homogeneous eyes of monkeys, compromising the assessment of eye-gaze by conspecifics from larger distances, explain the absence of comparable eye-gaze following in these animals. Yet, monkeys are able to use peer head orientation to shift attention ({\textquoteright}head-gaze following{\textquoteright}). How similar are monkeys{\textquoteright} head-gaze and human eye-gaze following? To address this question, we trained rhesus monkeys to make saccades to targets, either identified by the head-gaze of demonstrator monkeys or, alternatively, identified by learned associations between the demonstrators{\textquoteright} facial identities and the targets (gaze versus identity following). In a variant of this task that occurred at random, the instruction to follow head-gaze or identity was replaced in the course of a trial by the new rule to detect a change of luminance of one of the saccade targets. Although this change-of-rule rendered the demonstrator portraits irrelevant, they nevertheless influenced performance, reflecting a precise redistribution of spatial attention. The specific features depended on whether the initial rule was head-gaze or identity following: head-gaze caused an insuppressible shift of attention to the target gazed at by the demonstrator, whereas identity matching prompted much later shifts of attention, however, only if the initial rule had been identity following. Furthermore, shifts of attention prompted by head-gaze were spatially precise. Automaticity and swiftness, spatial precision and limited executive control characterizing monkeys{\textquoteright} head-gaze following are key features of human eye-gaze following. This similarity supports the notion that both may rely on the same conserved neural circuitry.

}, keywords = {Animals, Attention, Face, Head, learning, Macaca mulatta, Saccades, Vision, Ocular}, issn = {1471-2954}, doi = {10.1098/rspb.2015.1020}, author = {Marciniak, Karolina and Dicke, Peter W and Thier, Peter} } @conference {1298, title = {Preschoolers expect others to learn rationally from evidence}, booktitle = {Annual Conference of the Cognitive Science Society}, year = {2014}, abstract = {

Even infants expect agents to act rationally in pursuit of their\  goals. However, little research has looked at whether young\  children expect other agents to learn rationally. In the\  current study, we investigated 4.5- to 6-year-olds{\textquoteright} reasoning\  about another agent{\textquoteright}s beliefs after the agent observed a\  sample drawn randomly or selectively from a population.\  We found that those children who could correctly track both\  the true state of the world and the other agent{\textquoteright}s initial beliefs\  expected the other agent to learn rationally from the data.\  Critically, this inference depended upon but could not be\  reduced to either the child{\textquoteright}s own understanding of the world,\  or the child{\textquoteright}s own inferences from the sampling process,\  suggesting that the ability to integrate these component\  processes underlies a developing understanding of the way\  in which evidence informs others{\textquoteright} beliefs.

}, keywords = {learning, rational action, theory of mind}, author = {Phyllis Yan and Rachel Magid and Laura Schulz} }