This work presents a linguistic analysis into why larger Transformer-based pre-trained
language models with more parameters and lower perplexity nonetheless yield surprisal …

During real-time language comprehension, our minds rapidly decode complex meanings
from sequences of words. The difficulty of doing so is known to be related to words' …

Much research in cognitive neuroscience supports prediction as a canonical computation of
cognition across domains. Is such predictive coding implemented by feedback from higher …

To understand language, we must infer structured meanings from real-time auditory or visual
signals. Researchers have long focused on word-by-word structure building in working …

Expectation-based theories of sentence processing posit that processing difficulty is
determined by predictability in context. While predictability quantified via surprisal has …

Surprisal theory (Hale, 2001; Levy, 2008) posits that a word's reading time is proportional to
its surprisal (ie, to its negative log probability given the proceeding context). Since we are …

Abstract Words that are more surprising given context take longer to process. However, no
incremental parsing algorithm has been shown to directly predict this phenomenon. In this …

What role do domain-general executive functions play in human language comprehension?
To address this question, we examine the relationship between behavioral measures of …

We investigate the statistical power and Type I error rate of the two most common
approaches to reading time (RT) analyses: assuming normality of residuals and …

We present a synchronized multimodal neuroimaging dataset for studying brain language
processing (SMN4Lang) that contains functional magnetic resonance imaging (fMRI) and …