Learning reference biases from language input: a cognitive modelling approach

In order to gain insight into how people acquire certain reference biases in language and how those biases eventually influence online language processing, we constructed a cognitive model and presented it with a dataset containing reference asymmetries. Via prediction and reinforcement learning the model was able to pick up on the asymmetries in the input. The model predictions have implications for various accounts of reference processing and demonstrate that seemingly complex behavior can be explained by simple learning mechanisms.