Statistical learning of parts and wholes: A neural network approach.
Statistical learning of parts and wholes: A neural network approach.
复制标题
部分和整体的统计学习：神经网络方法。
DOI：
10.1037/xge0000262
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发表时间：
2017
期刊：
影响因子：
--
通讯作者：
Anna K Vande Velde
中科院分区：
文献类型：
--
作者：
D. Plaut;Anna K Vande Velde
统计学习通常被认为是一种发现感知单位的方法，例如单词和对象，并将它们表示为明确的“块”。然而，实体并不是无差别的整体，而往往包含系统地对其意义做出贡献的部分。对附带的听觉或视觉统计学习的研究表明，随着参与者对整体的了解，他们对嵌入其中的部分变得不敏感，但这似乎很难与广泛的发现相一致，在这些发现中，部分和整体共同作用，对行为做出贡献。贝叶斯方法提供了部分和整体如何同时对性能做出贡献的原则性描述，但通常不打算对实际导致这种性能的计算进行建模。在目前的工作中，我们开发了一个基于人工神经网络学习的帐户，其中部分和整体的表示是一个程度问题，它们的合作或竞争的程度是通过附带学习自然产生的。我们表明，该方法解释了关于听觉和视觉统计学习中部分和整体之间的关系的广泛发现，包括一些以前被认为是神经网络方法的问题的发现。(SqucINFO数据库记录
Statistical learning is often considered to be a means of discovering the units of perception, such as words and objects, and representing them as explicit "chunks." However, entities are not undifferentiated wholes but often contain parts that contribute systematically to their meanings. Studies of incidental auditory or visual statistical learning suggest that, as participants learn about wholes they become insensitive to parts embedded within them, but this seems difficult to reconcile with a broad range of findings in which parts and wholes work together to contribute to behavior. Bayesian approaches provide a principled description of how parts and wholes can contribute simultaneously to performance, but are generally not intended to model the computations that actually give rise to this performance. In the current work, we develop an account based on learning in artificial neural networks in which the representation of parts and wholes is a matter of degree, and the extent to which they cooperate or compete arises naturally through incidental learning. We show that the approach accounts for a wide range of findings concerning the relationship between parts and wholes in auditory and visual statistical learning, including some findings previously thought to be problematic for neural network approaches. (PsycINFO Database Record
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影响因子：
3.7
作者：
Kravitz, Dwight J.;Kriegeskorte, Nikolaus;Baker, Chris I.
通讯作者：
Baker, Chris I.
非相邻特征之间统计关系的视觉学习：结构编码的证据。
影响因子：
2
作者：
Barenholtz,Elan;Tarr,MichaelJ
通讯作者：
Tarr,MichaelJ
DOI：
10.1080/23273798.2015.1102299
发表时间：
2016
期刊：
Language, cognition and neuroscience
影响因子：
--
作者：
Kuperberg GR;Jaeger TF
通讯作者：
Jaeger TF
影响因子：
4.1
作者：
Gonnerman, Laura M.;Seidenberg, Mark S.;Andersen, Elaine S.
通讯作者：
Andersen, Elaine S.