Acquiring Rules for Rules: Neuro-Dynamical Systems Account for Meta-Cognition
Acquiring Rules for Rules: Neuro-Dynamical Systems Account for Meta-Cognition
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DOI：
10.1177/1059712308101739
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发表时间：
2009-02
影响因子：
1.6
通讯作者：
M. Maniadakis;J. Tani
中科院分区：
文献类型：
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作者：
M. Maniadakis;J. Tani
动物和人类在日常生活中都使用元规则，以使其行为策略适应不断变化的环境状况。通常，术语元规则包括应用于规则本身的那些规则。在认知科学中，设计元规则的传统方法遵循人类硬连线架构。与以前的方法相比，本研究采用进化过程来探索元水平规则转换的神经机制。特别是，我们进行了一系列的实验，模拟机器人必须学会在不同的行为规则之间切换，以完成给定的任务。连续时间递归神经网络（CTRNN）控制器的全连接或瓶颈结构进行了研究。结果表明，不同的规则由不同的自组织吸引子表示，而规则切换是通过吸引子之间的转换实现的。此外，结果表明，神经网络划分为较低的感觉运动水平和较高的认知水平，提高了机器人在给定任务中的性能。此外，元认知规则处理显着支持的控制器的实施例和较低层次的感觉运动的环境相互作用的属性。
Both animals and humans use meta-rules in their daily life, in order to adapt their behavioral strategies to changing environmental situations. Typically, the term meta-rule encompasses those rules that are applied to rules themselves. In cognitive science, conventional approaches for designing meta-rules follow human hardwired architectures. In contrast to previous approaches, this study employs evolutionary processes to explore neuronal mechanisms accounting for meta-level rule switching. In particular, we performed a series of experiments with a simulated robot that has to learn to switch between different behavioral rules in order to accomplish given tasks. Continuous time recurrent neural networks (CTRNN) controllers with either a fully connected or a bottleneck architecture were examined. The results showed that different rules are represented by separate self-organized attractors, while rule switching is enabled by the transitions among attractors. Furthermore, the results showed that neural network division into a lower sensorimotor level and a higher cognitive level enhances the performance of the robot in the given tasks. Additionally, meta-cognitive rule processing is significantly supported by the embodiment of the controller and the lower level sensorimotor properties of environmental interaction.