Acquisition of a Mini-Super Computer for Modeling Normal and Discordered Cognition.

购买一台微型超级计算机，用于模拟正常和紊乱的认知。

• 批准号: 9102196
• 负责人: James McClelland
• 金额: $ 16.23万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1991
• 资助国家: 美国
• 起止时间: 1991-07-15 至 1993-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9102196&HistoricalAwards=false
• 关键词: Acquisition; Mini; Super; Computer; Modeling

This proposal seeks funds for instrumentation needed for the further development and assessment of parallel distributed processing (PDP) models of a number of different aspects of human cognitive processes. We seek to study a class of PDP models in which processing occurs through the interaction of a large number of intrinsically variable processing units whose activation is based on a time-average of their input from other units. The knowledge that governs skilled processing is stored in the strengths of the connections among the units, and the acquisition of the knowledge that underlies skilled processing occurs through gradual adjustments in the strengths of the connections triggered by processing experience. Previous work has shown the potential of the approach for a number of applications, but continued progress requires an increase in computational resources. In some cases, it has not been possible to explore a realistically diverse range of training examples, thus limiting the opportunity to test the adequacy of the principles to model human performance data. In other cases, simplified assumptions about processing have been made, and it is now clear that these simplifications can result in inadequate fits to experimental data. In both kinds of cases, the ability to assess the degree to which the models can account for experimental data has been compromised. We therefore seek an upgrade to our existing 5-year old vectorizing 'minisupercomputer' to a newer, more powerful model.

这项建议寻求资金，用于 并行分布式的进一步发展和评估 处理（PDP）模型的一些不同方面的人类 认知过程 我们试图研究一类PDP模型， 这种处理是通过大量的 的本质可变的处理单元，其激活是 基于他们从其他单位输入的时间平均值。 的 管理熟练处理的知识存储在 单位之间的联系的强度，以及收购 作为熟练加工基础的知识， 连接强度的逐渐调整引发了 处理经验。 以前的研究表明， 一些应用程序的方法，但继续 进步需要增加计算资源。 在 在某些情况下，不可能现实地探讨 培训实例的多样性，从而限制了 来测试人类行为模型的原则是否充分 数据 在其他情况下，关于处理的简化假设 现在很清楚，这些简化 可能导致与实验数据的拟合不充分。 无论是 的能力，以评估在何种程度上， 模型能解释的实验数据已经被破坏了。 因此，我们寻求升级到我们现有的5岁 将“迷你超级计算机”矢量化为更新、更强大的模型。