10 - Benchmarking n-grams, Topic Models and Recurrent Neural Networks by Cloze Completions, EEGs and Eye Movements

Abstract

In neurocognitive psychology, manually collected cloze completion probabilities (CCPs) are the standard approach to quantifying a word’s predictability from sentence context. Here, we test a series of language models in accounting for CCPs, as well as the data they typically account for, i.e. electroencephalographic (EEG) and eye movement (EM) data. With this, we hope to render time-consuming CCP procedures unnecessary. We test a statistical n-gram language model, a Latent Dirichlet Allocation (LDA) topic model, as well as a recurrent neural network (RNN) language model for correlation with the neurocognitive data.