Imagine that you just watched a woman give a dollar to a man, get an apple in return, and walk away. Clearly, the woman just bought an apple. But to conclude that this happened, we implicitly rely on a large set of abstract concepts. You have to know what ownership is, and that it can be transferred. You have to understand that the piece of paper the man received is currency with an exact value. And you have to understand that, in contrast to the apple, the man and the woman have minds with thoughts and desires. Although you cannot see what either of them were thinking, you can infer a lot based on their brief interaction: the man intended to sell the apple and the woman intended to buy it. The woman wanted an apple and she believes that it now belongs to her (she also believes that the man also believes that the apple now belongs to her). The man believes that the paper he received is real money, and the woman believes that the exchange is fair. Moreover, this interpretation can change drastically if minor details of the story were different. If the woman had walked away without paying, she probably got the apple as a gift; if she had run away, she probably stole it.

Many of the concepts that we use to make sense of what happened –ownership, money, minds, thoughts, beliefs, preferences, intentions, fairness, and theft- are abstract. You cannot point to the atoms that make up a belief in the same way that you can point to the atoms that make up an apple. How does our mind represent concepts like these? How do we learn them? And how do we transform physical events into abstract representations that let us infer things we cannot see, guess what may have happened in the past, and predict what might happen in the future?

The Computation and Development lab studies how our experience with the world drives our construction of abstract concepts and how, in turn, our understanding of complex events is structured through these concepts. Our research spans across ages and cultures, but our primary focus is on early childhood. Human beings’ greatest cognitive achievements – understanding minds, numbers, and languages, to name only a few— all happen in the first years of life. By studying cognition in early childhood we can gain insight into the most fundamental concepts that support human thought.

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 incorporates computational modeling, which allows us to ensure the precision of our theories, to understand their scope and limitations, and to generate testable quantitative predictions. To learn more, check out the lab's publications!