Kelly Matthew A, Blostein Dorothea, Mewhort D J K
Institute of Cognitive Science, Carleton University, ON, Canada.
Can J Exp Psychol. 2013 Jun;67(2):79-93. doi: 10.1037/a0030301. Epub 2012 Dec 3.
Vector Symbolic Architectures (VSAs) such as Holographic Reduced Representations (HRRs) are computational associative memories used by cognitive psychologists to model behavioural and neurological aspects of human memory. We present a novel analysis of the mathematics of VSAs and a novel technique for representing data in HRRs. Encoding and decoding in VSAs can be characterised by Latin squares. Successful encoding requires the structure of the data to be orthogonal to the structure of the Latin squares. However, HRRs can successfully encode vectors of locally structured data if vectors are shuffled. Shuffling results are illustrated using images but are applicable to any nonrandom data. The ability to use locally structured vectors provides a technique for detailed modelling of stimuli in HRR models.
诸如全息压缩表征（HRRs）之类的向量符号架构（VSAs）是认知心理学家用于模拟人类记忆的行为和神经学方面的计算联想记忆。我们提出了一种对VSAs数学的新颖分析以及一种在HRRs中表示数据的新颖技术。VSAs中的编码和解码可以用拉丁方来表征。成功的编码要求数据的结构与拉丁方的结构正交。然而，如果向量被打乱，HRRs可以成功地编码局部结构化数据的向量。使用图像说明了打乱的结果，但该结果适用于任何非随机数据。使用局部结构化向量的能力为HRR模型中的刺激详细建模提供了一种技术。
