Computational Psycholinguistics

Computational psycholinguistics builds models of how people learn, produce, and understand language, and uses them to explain measurable behavior. Approaches include Bayesian noisy-channel and rational-speaker models, surprisal-based prediction, neural language models, and explicit accounts of syntactic parsing and memory. Researchers test these models against reading times, eye movements, cloze judgments, speech, and EEG or brain-imaging data, across languages and developmental settings. A central tension is that predictive fit—especially from language-model surprisal—does not by itself establish a human-like mechanism, and models often miss integration costs or vary across tasks, languages, and measures.