COGS/Informatics, University of Sussex.
Cogn Sci. 2009 Nov;33(8):1383-412. doi: 10.1111/j.1551-6709.2009.01066.x. Epub 2009 Sep 21.
Early agreement within cognitive science on the topic of representation has now given way to a combination of positions. Some question the significance of representation in cognition. Others continue to argue in favor, but the case has not been demonstrated in any formal way. The present paper sets out a framework in which the value of representation use can be mathematically measured, albeit in a broadly sensory context rather than a specifically cognitive one. Key to the approach is the use of Bayesian networks for modeling the distal dimension of sensory processes. More relevant to cognitive science is the theoretical result obtained, which is that a certain type of representational architecture is necessary for achievement of sensory efficiency. While exhibiting few of the characteristics of traditional, symbolic encoding, this architecture corresponds quite closely to the forms of embedded representation now being explored in some embedded/embodied approaches. It becomes meaningful to view that type of representation use as a form of information recovery. A formal basis then exists for viewing representation not so much as the substrate of reasoning and thought, but rather as a general medium for efficient, interpretive processing.
早期认知科学在表示论这个主题上达成的共识，现在已经让位于各种立场的结合。一些人质疑表示在认知中的重要性。另一些人则继续支持表示论，但还没有以任何正式的方式证明这一点。本文提出了一个框架，在这个框架中，可以以数学方式衡量表示使用的价值，尽管是在广泛的感官背景下，而不是在特定的认知背景下。该方法的关键是使用贝叶斯网络来对感官过程的远端维度进行建模。与认知科学更相关的是所获得的理论结果，即对于实现感官效率来说，某种类型的表示架构是必要的。虽然这种架构几乎没有表现出传统的符号编码的特征，但它与现在在一些嵌入式/具身方法中探索的嵌入式表示形式非常相似。将这种类型的表示使用视为一种信息恢复的形式就变得有意义了。然后，就存在了一个正式的基础，可以将表示视为推理和思维的基础，而不是高效、解释性处理的一般媒介。
