How do kids manage to figure out that the word “dog” applies to a whole category of animals, not just one creature? Joshua Tenenbaum wants to understand how children and adults manage to solve such classic problems of induction. Throughout cognition, wherever you look, he says “we see places where we know more than we have a reasonable right to know about the world, places where we come to abstractions, generalizations, models of the world that go beyond our sparse, noisy, limited experience.” Tenenbaum’s goal is to come up with “general purpose computational tools for understanding how people solve these problems so successfully.”

He’s creating a set of hierarchical, probabilistic models that will help explain how humans make inductive leaps – how abstract knowledge that “guides and constrains our inferences” helps us acquire language from our earliest days. While his models can apply to many areas of cognition, Tenenbaum focuses on recent work with syntax. From very simple data, children manage to turn a complex declarative like “The girl who is sleeping is happy,” to a complex interrogative: “Is the girl who is sleeping happy?” They don’t say, “Is the girl who sleeping is happy?” Tenenbaum suggests that humans somehow identify the hierarchical phrase structure of language, and use this as an “inductive constraint to guide acquisition of a particular piece of syntax.”

Tenenbaum and his colleagues have built representative grammars using data from child-directed speech --2300 sentences that correspond to 20 thousand-plus utterances. He deconstructs these sentences so that each word is replaced by a syntactic category. “The baby bear discovers Goldilocks in his bed” becomes “det adj n v prop pre adj n.” He’s explored these grammars for their capacity to balance complexity, generalize appropriately, and ability to fit the data. His results indicate that “by having the right kind of inductive bias, the idea of hierarchical phrase structure, you can make generalizations which you have no evidence for…”