.png){kind=icon}
返回
A systematic methodology for cognitive modelling
.png){kind=icon}
OA
AI
DOI：10.1016/0004-3702(95)00112-3.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The development and testing of computational models of cognition is typically ad hoc: few generally agreed methodological principles guide the process. Consequently computational models often conflate empirically justified mechanisms with pragmatic implementation details, and essential theoretical aspects of theories are frequently hard to identify. We argue that attempts to construct cognitive theories would be considerably assisted by the availability of appropriate languages for specifying cognitive models. Such languages should: (1) be syntactically clear and succinct; (2) be operationally well defined; (3) be executable; and (4) explicitly support the division between theory and implementation detail. In support of our arguments we introduce Sceptic, an executable specification language which goes some way towards satisfying these requirements, Sceptic has been successfully used to implement a number of cognitive models including Soar, and details of the Sceptic specification of Soar are included in a technical appendix. The simplicity of Sceptic Soar permits the essentials of the underlying cognitive theory to be seen, and aids investigation of alternative theoretical assumptions. We demonstrate this by reporting three computational experiments involving modifications to the functioning of working memory within Soar, Although our focus is on Soar, the thrust of the work is more concerned with general methodological issues in cognitive modelling.
Keyword:
GENERAL INTELLIGENCE
SOAR
ARCHITECTURE
DYSLEXIA
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
13.9
论文数:
6.1K
被引数:
1.9W
机构
暂无机构信息
引用论文
Forest vegetation of the Colorado Front Range: Patterns of species diversity科罗拉多前线山脉的森林植被: 物种多样性的模式.png)
Vegetatio
VegetatioIF0
PREAI
分享收藏
分享收藏
查看更多内容