Topic: reading times

Artificial neural networks (ANN) have emerged as computationally plausible models of human language processing. A major criticism of these models is that the amount of training data they receive far exceeds that of humans. Here, we use two complementary approaches to ask how the models’ ability to capture human neural and behavioral responses to language is affected by the amount of training data. First, we evaluate GPT-2 models trained on 1 million, 10 million, 100 million, or 1 billion tokens against a neural (fMRI) and a behavioral (reading times) benchmark. Because children are exposed to