Resource-Rational Lossy-Context Surprisal (Model Predictions)

Resource-Rational Lossy-Context Surprisal is a computationally implemented model of how humans process language, predicting at what points in complex sentences they experience comprehension difficulty. It unifies the memory-based and expectation-based paradigms in psycholinguistics, and provides a more refined account of when hierarchical structure is difficult to comprehend for humans. This repository contains output of the model on a battery of test sentences exhibiting iterated recursive structure, described in associated publications on Resource-Rational Lossy-Context Surprisal. The filenames are referred to in the source code, to be published together with a forthcoming journal publication on the model. The model was first described in the following publication: Lexical Effects in Structural Forgetting: Evidence for Experience-Based Accounts and a Neural Network Model (Michael Hahn, Richard Futrell, Edward Gibson), 33rd Annual CUNY Human Sentence Processing Conference, 2020