The emergence of executive functions by the evolution of second-order learning
The emergence of executive functions by the evolution of second-order learning
复制标题
二阶学习的进化导致执行功能的出现
DOI：
10.1007/s10015-017-0389-7
复制
发表时间：
2017
影响因子：
0.9
通讯作者：
Arita Takaya
中科院分区：
文献类型：
--
作者：
Daimon Keisuke;Arnold Solvi;Suzuki Reiji;Arita Takaya
心理表征被认为是复杂认知过程的一部分。有观点认为，在二阶学习（学习如何学习）的选择压力下，一阶学习通过捕捉作为mr的不断变化的环境的固有结构，从而在一生中进化为促进二阶学习。这一理论提出了两个假设：(1)解决mr依赖任务应该涉及神经层面的二阶可塑性。(2)解决核磁共振依赖任务需要将环境的结构特征内化为认知系统的相应特征。本文采取了建设性的方法，并从这两个假设的角度对结果进行了分析。执行功能是复杂任务中良好表现所必需的认知过程的集合，是我们模型的主题。它们被认为与心理理论有关，这是mr的一个典型例子。我们进行了一个进化模拟，其中具有循环神经网络的代理处理威斯康星卡片分类测试（WCST），这是一个广泛使用的任务，用于衡量执行功能的能力。结果表明，一些代理成功地在WCST中获得了理想的分数，从而出现了执行功能。此外，我们还讨论了基于一种进化的神经网络的假设。
Mental representation (MR) is regarded as part of sophisticated cognitive processes. It has been argued that under selection pressure for second-order learning (learning how to learn), first-order learning evolves to facilitate second-order learning within lifetime by capturing inherent structures of changing environment as MR. Two hypotheses derive from this theory: (1) solving MR-dependent tasks should involve second-order plasticity at the neural level. (2) Solving MR-dependent tasks should involve internalization of structural features of environment into corresponding features of the cognitive system. In this paper, constructive approach was taken and the result was analyzed from the viewpoint of these two hypotheses. Executive functions, a collection of cognitive processes necessary for good performance on complex tasks, are the theme of our model. They are considered to be related to theory of mind, which is a typical example of MR. We conducted an evolutionary simulation where agents with recurrent neural networks tackled the Wisconsin card sorting test (WCST), a widely used task to measure abilities of executive functions. The results showed some agents were successfully able to achieve ideal scores in the WCST, hence the emergence of executive functions. In addition, we also discussed the hypotheses based on one of the evolved neural networks.