The ability to focus on relevant information and ignore irrelevant information is a fundamental part of intelligent behavior. It not only allows faster acquisition of new tasks by reducing the size of the problem space but also allows for generalizations to novel stimuli. Task-switching, task-sets, and rule-set learning are all intertwined with this ability. There are many models that attempt to individually describe these cognitive abilities. However, there are few models that try to capture the breadth of these topics in a unified model and fewer still that do it while adhering to the biological constraints imposed by the findings from the field of neuroscience. Presented here is a comprehensive model of rule-set learning and selection that can capture the learning curve results, error-type data, and transfer effects found in rule-learning studies while also replicating the reaction time data and various related effects of task-set and task-switching experiments. The model also factors in many disparate neurological findings, several of which are often disregarded by similar models.

中文翻译：

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规则集学习和选择的统一模型。

专注于相关信息而忽略无关信息的能力是智能行为的基本组成部分。它不仅可以通过减少问题空间的大小来更快地获取新任务，而且还可以推广到新颖的刺激。任务切换，任务集和规则集学习都与此功能交织在一起。有许多模型试图单独描述这些认知能力。但是，很少有模型会尝试在统一模型中捕获这些主题的广度，而在遵循神经科学领域的发现所施加的生物学限制的同时，仍然很少有模型能够做到。这里展示的是规则集学习和选择的综合模型，可以捕获学习曲线结果，错误类型数据，规则学习研究中发现的转移效应，同时还复制反应时间数据以及任务集和任务转换实验的各种相关效应。该模型还考虑了许多不同的神经系统发现，其中一些经常被相似的模型所忽略。