Learning to predict or predicting to learn?

摘要

Humans complete complex commonplace tasks, such as understanding sentences, with striking speed and accuracy. This expertise is dependent on anticipation: predicting upcoming words gets us ahead of the game. But how do we master the game in the first place? To make accurate predictions, children must first learn their language. One possibility is that prediction serves double duty, enabling rapid language learning as well as understanding. Children could master the structures of their language by predicting how speakers will behave and, when those guesses are wrong, revising their linguistic representations. A number of prominent computational models assume that children learn in this way. But is that assumption correct? Here, we lay out the requirements for showing that children use “predictive learning”, and review the current evidence for this position. We argue that, despite widespread enthusiasm for the idea, we cannot yet conclude that children “predict to learn”.