Dodig-Crnkovic Gordana
Department of Computer Science and Engineering, Chalmers University of Technology, 41296 Gothenburg, Sweden.
Division of Computer Science and Software Engineering, School of Innovation, Design and Engineering, Mälardalen University, 722 20 Västerås, Sweden.
Entropy (Basel). 2022 Oct 31;24(11):1576. doi: 10.3390/e24111576.
Cognition, historically considered uniquely human capacity, has been recently found to be the ability of all living organisms, from single cells and up. This study approaches cognition from an info-computational stance, in which structures in nature are seen as information, and processes (information dynamics) are seen as computation, from the perspective of a cognizing agent. Cognition is understood as a network of concurrent morphological/morphogenetic computations unfolding as a result of self-assembly, self-organization, and autopoiesis of physical, chemical, and biological agents. The present-day human-centric view of cognition still prevailing in major encyclopedias has a variety of open problems. This article considers recent research about morphological computation, morphogenesis, agency, basal cognition, extended evolutionary synthesis, free energy principle, cognition as Bayesian learning, active inference, and related topics, offering new theoretical and practical perspectives on problems inherent to the old computationalist cognitive models which were based on abstract symbol processing, and unaware of actual physical constraints and affordances of the embodiment of cognizing agents. A better understanding of cognition is centrally important for future artificial intelligence, robotics, medicine, and related fields.
认知,历史上被认为是人类独有的能力,最近却被发现是所有生物(从单细胞生物到更高级生物)都具备的能力。本研究从信息计算的角度探讨认知,在这个视角下,从认知主体的角度看,自然界中的结构被视为信息,而过程(信息动态)被视为计算。认知被理解为一个由并发的形态学/形态发生学计算组成的网络,这些计算是由于物理、化学和生物主体的自组装、自组织和自创生而展开的。当今在主要百科全书中仍然盛行的以人类为中心的认知观点存在各种未解决的问题。本文探讨了关于形态计算、形态发生、智能体、基础认知、扩展进化综合理论、自由能原理、作为贝叶斯学习的认知、主动推理以及相关主题的最新研究,为基于抽象符号处理、忽视认知主体实际物理限制和可供性的旧计算主义认知模型所固有的问题提供了新的理论和实践视角。更好地理解认知对于未来的人工智能、机器人技术、医学及相关领域至关重要。