Cognitive and Sub-regular Complexity
Cognitive and Sub-regular Complexity
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认知和次规则复杂性
DOI：
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发表时间：
2013
期刊：
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通讯作者：
Sean Wibel
中科院分区：
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作者：
J. Rogers;Jeffrey Heinz;M. Fero;J. Hurst;D. Lambert;Sean Wibel
我们提出了一种基于模型理论复杂性而不是特定语法或自动机类的描述长度的常规语言子类的认知复杂性度量。与描述长度方法不同，这种复杂性度量与认知机制的实现细节无关。因此，它提供了一个基础，使推理的认知机制是有效的，不管这些机制是如何实现的。
We present a measure of cognitive complexity for subclasses of the regular languages that is based on model-theoretic complexity rather than on description length of particular classes of grammars or automata. Unlike description length approaches, this complexity measure is independent of the implementation details of the cognitive mechanism. Hence, it provides a basis for making inferences about cognitive mechanisms that are valid regardless of how those mechanisms are actually realized.