The Computation and Cognitive Development Lab studies how we make sense of the social world and the physical world using a combination of computational models with behavioral studies that span across ages and cultures.
Computational modeling and behavioral studies with adults
The lab's research is driven by an engineering philosophy: if we really understand a cognitive process, we should be able to implement it on a machine. Thus, much of our research involves formalizing cognitive theories as working computational models. This allows us to ensure that our theories are precise, it enables us to understand the scope and limitations of our theories, and it lets us generate precise predictions which we can then compare to human reasoning. To date most of our computational work has focused on Theory of Mind -our ability to represent and reason about other people's thoughts-. In recent work we've built models that can infer a person's competence and motivation by watching their behavior (Jara-Ettinger, Schulz, & Tenenbaum, under review), models that can infer and express complex desires with temporal and logical structure (Velez-Ginorio, Siegel, Tenenbaum, & Jara-Ettinger, 2017), and models that can do one-shot joint inferences on subjective and objective properties of the world (Jara-Ettinger & Gweon, 2017).
Our greatest cognitive achievements -understanding minds, numbers, and langauges, to name a few- all happen during early childhood. The lab runs behavioral studies with young children to understand the building blocks of our ability to make sense of and navigate the social and physical world. Although our computational models are usually evaluated against adult participants, the inspiration behind them often stems from our developmental studies. In recent work we've shown that preschoolers can interpret ambiguous utterances by thinking about the speakers intentions (Jara-Ettinger, Floyd, Huey, Tenenbaum, & Schulz, under review), that preschoolers understand that other people sometimes don't know what they like (Jara-Ettinger, Floyd, Tenenbaum, & Schulz, 2017), and that even youger children are sensitive to the helper's competence when making moral judgments (Jara-Ettinger, Tenenbaum, & Schulz, 2015).
To better understand the extent to which our findings reveal universal properties of human cognition, we conduct behavioral studies with Tsimane' children and adults. The Tsimane' are a non-industrialized farming-foraging group living in the Bolivian Amazon. In recent work we have found that children's intuitions about fairness interact with the cultural construction of number (Jara-Ettinger, Gibson, Kidd, & Piantadosi, 2015), and we have made progress in understanding how children learn the meaning of abstract words, focusing on number words (Jara-Ettinger, Piantadosi, Spelke, Levy, & Gibson, 2016).