Williams Daniel
Faculty of Philosophy, Trinity Hall, University of Cambridge, Cambridge, UK.
Minds Mach (Dordr). 2018;28(1):141-172. doi: 10.1007/s11023-017-9441-6. Epub 2017 Jun 26.
Clark has recently suggested that predictive processing advances a theory of neural function with the resources to put an ecumenical end to the "representation wars" of recent cognitive science. In this paper I defend and develop this suggestion. First, I broaden the representation wars to include three foundational challenges to representational cognitive science. Second, I articulate three features of predictive processing's account of internal representation that distinguish it from more orthodox representationalist frameworks. Specifically, I argue that it posits a resemblance-based representational architecture with organism-relative contents that functions in the service of pragmatic success, not veridical representation. Finally, I argue that internal representation so understood is either impervious to the three anti-representationalist challenges I outline or can actively embrace them.
克拉克最近提出，预测处理推进了一种神经功能理论，该理论有资源以一种普适的方式终结近期认知科学中的“表征之争”。在本文中，我捍卫并发展这一观点。首先，我将表征之争的范围扩大，以纳入对表征认知科学的三个基础性挑战。其次，我阐述了预测处理对内部表征的解释所具有的三个特征，这些特征使其有别于更为正统的表征主义框架。具体而言，我认为它假定了一种基于相似性的表征架构，其内容与有机体相关，其功能是为了实现实用上的成功，而非如实表征。最后，我认为如此理解的内部表征要么不受我所概述的三个反表征主义挑战的影响，要么能够积极接纳它们。
