The Simulative Role of Neural Language Models in Brain Language Processing

Author:

Angius Nicola1ORCID,Perconti Pietro1,Plebe Alessio1,Acciai Alessandro1ORCID

Affiliation:

1. Department of Cognitive Science, University of Messina, Via Concezione 8, 98121 Messina, Italy

Abstract

This paper provides an epistemological and methodological analysis of the recent practice of using neural language models to simulate brain language processing. It is argued that, on the one hand, this practice can be understood as an instance of the traditional simulative method in artificial intelligence, following a mechanistic understanding of the mind; on the other hand, that it modifies the simulative method significantly. Firstly, neural language models are introduced; a study case showing how neural language models are being applied in cognitive neuroscience for simulative purposes is then presented; after recalling the main epistemological features of the simulative method in artificial intelligence, it is finally highlighted how the epistemic opacity of neural language models is tackled by using the brain itself to simulate the neural language model and to test hypotheses about it, in what is called here a co-simulation.

Funder

Italian Ministry of University and Research

Publisher

MDPI AG

Reference65 articles.

1. Wiener, N. (1948). Cybernetics or Control and Communication in the Animal and the Machine, MIT Press.

2. Bergson, H. (1911). Creative Evolution, Dover.

3. Simon, H.A. (1996). The Sciences of the Artificial, MIT Press. [3rd ed.].

4. The artificial intelligence renaissance: Deep learning and the road to human-Level machine intelligence;Tan;APSIPA Trans. Signal Inf. Process.,2018

5. Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3–6). ImageNet Classification with Deep Convolutional Neural Networks. Proceedings of the Advances in Neural Information Processing Systems, Lake Tahoe, NV, USA.

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3