Institute for Neural Computation, Ruhr-University Bochum.
Cogn Sci. 2024 Sep;48(9):e13491. doi: 10.1111/cogs.13491.
How situated embodied agents may achieve goals using knowledge is the classical question of natural and artificial intelligence. How organisms achieve this with their nervous systems is a central challenge for a neural theory of embodied cognition. To structure this challenge, we borrow terms from Searle's analysis of intentionality in its two directions of fit and six psychological modes (perception, memory, belief, intention-in-action, prior intention, desire). We postulate that intentional states are instantiated by neural activation patterns that are stabilized by neural interaction. Dynamic instabilities provide the neural mechanism for initiating and terminating intentional states and are critical to organizing sequences of intentional states. Beliefs represented by networks of concept nodes are autonomously learned and activated in response to desired outcomes. The neural dynamic principles of an intentional agent are demonstrated in a toy scenario in which a robotic agent explores an environment and paints objects in desired colors based on learned color transformation rules.
情境化的具身主体如何利用知识来实现目标，这是自然和人工智能的经典问题。生物体如何通过其神经系统实现这一点，是具身认知的神经理论的核心挑战。为了构建这一挑战，我们借鉴了塞尔（Searle）对意向性的分析中的两个契合方向和六个心理模式（感知、记忆、信念、意向行动、先前意向、欲望）的术语。我们假设，意向状态是由神经相互作用稳定的神经激活模式来实现的。动态不稳定性为启动和终止意向状态提供了神经机制，对于组织意向状态的序列至关重要。由概念节点网络表示的信念是自主学习和激活的，以响应期望的结果。在一个玩具场景中，展示了一个意向主体的神经动力学原理，其中一个机器人主体在环境中探索，并根据学习到的颜色转换规则将物体涂成所需的颜色。
