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: <em>Lexical Effects in Structural Forgetting: Evidence for Experience-Based Accounts and a Neural Network Model</em> (Michael Hahn, Richard Futrell, Edward Gibson), 33rd Annual CUNY Human Sentence Processing Conference, 2020

资源理性损失语境惊讶度（Resource-Rational Lossy-Context Surprisal）是一款计算实现的人类语言处理模型，可精准预测复杂语句中引发理解困难的节点位置。该模型整合了心理语言学领域基于记忆与基于预期的两大研究范式，针对人类理解层级结构时的难点场景提供了更为精细的阐释。本代码仓库收录了该模型在一组展现迭代递归结构的测试句集上的输出结果，相关细节已在与本模型相关的已发表研究中详述。代码中提及的文件名，将随本模型相关的一篇即将刊出的期刊论文一同公开。该模型首次在以下研究中被提出：《结构遗忘中的词汇效应：基于经验的解释与神经网络模型》(*Lexical Effects in Structural Forgetting: Evidence for Experience-Based Accounts and a Neural Network Model*)（Michael Hahn、Richard Futrell、Edward Gibson），第33届CUNY人类语句处理年会，2020年