Gigley H M
Comput Methods Programs Biomed. 1986 Mar;22(1):43-50. doi: 10.1016/0169-2607(86)90092-1.
Computational neurolinguistics, an integrated approach to cognitive modelling of neural processes which may subserve natural language performance, attempts to build computational models that model behavior on two levels: at the neural process level and at the human performance level in its normal state and under pathological conditions. HOPE is one example of such a model. It demonstrates how the design and implementation of such models can provide insights into how a brain-like architecture can produce a behavior as complex as natural language. This paper will briefly describe the neurally motivated or 'natural computation' processes which produce the model's observable and verifiable behavioral results. Experiments with artificially induced aphasia on HOPE will then be described, showing that the results of simulation produce hypothesized patient profiles that are unique. These profiles illustrate the suggested contribution of the computational neurolinguistics research approach as a tool for investigating the breakdown of language performance and its potential contribution to understanding brain function.
计算神经语言学是一种对可能支持自然语言表现的神经过程进行认知建模的综合方法,它试图构建在两个层面上对行为进行建模的计算模型:神经过程层面以及正常状态和病理条件下的人类表现层面。HOPE就是这样一个模型的例子。它展示了此类模型的设计与实现如何能够深入了解类似大脑的架构如何产生像自然语言这样复杂的行为。本文将简要描述产生该模型可观察和可验证行为结果的神经驱动或“自然计算”过程。然后将描述对HOPE进行人工诱导失语的实验,结果表明模拟结果产生了独特的假设患者概况。这些概况说明了计算神经语言学研究方法作为一种工具在研究语言表现障碍及其对理解脑功能的潜在贡献方面的作用。