深度学习认知架构的反表征主义转向

The Anti-Representationism Turn in Deep Learning Cognitive Architectures

当代认知研究发展出了符号主义和联结主义两种不同的范式。符号主义的计算-表征是以思想语言假说为基础的“句法图像”,具有内容与载体相分离、符号语境无关性等表征特征。深度学习是对联结主义技术的创新和深化,其认知架构是具有分布式加工和叠加存储、语境敏感和原型提取学习等特点的亚符号计算,表现出一系列的反表征特征,反映在深度网络中并不以明确的概念表征为对象的操作,推动了认知哲学中反表征主义的兴起。在充分理解符号主义和深度学习认知架构表征方式的基础上,探索二者在某种程度上的统一,也许是值得努力的目标。

Two different paradigms,symbolism and connectionism,have evolved from contemporary cognitive research.The computational-representation of symbolism is a"syntactic image"based on the language-of-thought hypothesis,which is featured by the separation between content and its carriers and by the context-independence of symbols.Deep learning has been an innovation and depth to connectionism technique,whose architectures are characterized as a kind of sub-symbol computation with such features as distributed processing and superposition storage,context-sensitivity,and prototype extraction,has demonstrated a series of anti-representational features which have been reflected by operations in deep networks that do not target explicit conceptual representations,and has driven the rise of anti-representationism in cognitive philosophy.Exploring unity between symbolism and deep learning architectures in some degree based on fully understanding their representational approaches may be a worthwhile goal.