Automatic Generation of Cognitive Theories using Genetic Programming
Automatic Generation of Cognitive Theories using Genetic Programming
复制标题
使用遗传编程自动生成认知理论
DOI：
10.1007/s11023-007-9070-6
复制
发表时间：
2007
影响因子：
7.4
通讯作者：
F. Gobet
中科院分区：
文献类型：
--
作者：
E. Frías;F. Gobet
认知神经科学是神经科学的分支，研究支撑认知的神经机制并发展解释它们的理论。在认知神经科学中，计算神经科学专注于建模行为，使用表达为计算机程序的理论。到目前为止，神经科学家已经制定了计算理论。在本文中，我们提出了一种新的方法，在神经科学的理论发展：使用遗传编程（GP）的认知理论的自动生成和测试。我们的方法是从实验数据发展而来的认知理论，解释了受试者用来解决特定任务的“心理程序”。作为一个例子，我们专注于一个典型的神经科学实验，延迟匹配样本（DMTS）任务。我们方法的主要目标是开发一种工具，神经科学家可以用它来开发更好的认知理论。
Cognitive neuroscience is the branch of neuroscience that studies the neural mechanisms underpinning cognition and develops theories explaining them. Within cognitive neuroscience, computational neuroscience focuses on modeling behavior, using theories expressed as computer programs. Up to now, computational theories have been formulated by neuroscientists. In this paper, we present a new approach to theory development in neuroscience: the automatic generation and testing of cognitive theories using genetic programming (GP). Our approach evolves from experimental data cognitive theories that explain “the mental program” that subjects use to solve a specific task. As an example, we have focused on a typical neuroscience experiment, the delayed-match-to-sample (DMTS) task. The main goal of our approach is to develop a tool that neuroscientists can use to develop better cognitive theories.