Beyond Single-Level Accounts: The Role of Cognitive Architectures in Cognitive Scientific Explanation
Beyond Single-Level Accounts: The Role of Cognitive Architectures in Cognitive Scientific Explanation
复制标题
DOI：
10.1111/tops.12132
复制
发表时间：
2015-04-01
影响因子：
3
通讯作者：
Peebles, David
中科院分区：
文献类型：
--
作者：
Cooper, Richard P.;Peebles, David
我们考虑认知科学中的解释方法，这些方法开始于马尔的计算水平（例如，认知现象的纯贝叶斯解释）或马尔的实现水平（例如，还原主义者对认知现象的解释仅基于神经层面的证据），并认为每一种解释都受到基本限制，这损害了他们对认知现象提供充分解释的能力。出于这个原因，有人认为，解释不能在任何一个级别没有紧密耦合的算法和代表性的水平。然而，即使在这个层面上，我们认为，与认知系统分解为一组相互作用的子功能（即，认知体系结构）。集成的认知架构，允许抽象规范的组件的功能，并与神经水平的联系提供了一个强大的桥梁连接的算法和代表性的水平，以计算水平和实施水平。
We consider approaches to explanation within the cognitive sciences that begin with Marr's computational level (e.g., purely Bayesian accounts of cognitive phenomena) or Marr's implementational level (e.g., reductionist accounts of cognitive phenomena based only on neural-level evidence) and argue that each is subject to fundamental limitations which impair their ability to provide adequate explanations of cognitive phenomena. For this reason, it is argued, explanation cannot proceed at either level without tight coupling to the algorithmic and representation level. Even at this level, however, we argue that additional constraints relating to the decomposition of the cognitive system into a set of interacting subfunctions (i.e., a cognitive architecture) are required. Integrated cognitive architectures that permit abstract specification of the functions of components and that make contact with the neural level provide a powerful bridge for linking the algorithmic and representational level to both the computational level and the implementational level.