Department of Psychological and Brain Sciences, Indiana University, Bloomington, Indiana, USA.
Psychon Bull Rev. 2010 Oct;17(5):662-72. doi: 10.3758/PBR.17.5.662.
A common assumption implicit in cognitive models is that lexical semantics can be approximated by using randomly generated representations to stand in for word meaning. However, the use of random representations contains the hidden assumption that semantic similarity is symmetrically distributed across randomly selected words or between instances within a semantic category. We evaluated this assumption by computing similarity distributions for randomly selected words from a number of well-known semantic measures and comparing them with the distributions from random representations commonly used in cognitive models. The similarity distributions from all semantic measures were positively skewed compared with the symmetric normal distributions assumed by random representations. We discuss potential consequences that this false assumption may have for conclusions drawn from process models that use random representations.
认知模型中隐含的一个常见假设是，词汇语义可以通过使用随机生成的表示来代替单词的意思来近似。然而，使用随机表示隐含着一个假设，即语义相似性在随机选择的单词之间或语义类别中的实例之间是对称分布的。我们通过计算来自几个著名语义度量的随机选择的单词的相似性分布，并将其与认知模型中常用的随机表示的分布进行比较，来评估这个假设。与随机表示所假设的对称正态分布相比，所有语义度量的相似性分布都呈现正偏态。我们讨论了这个错误假设可能对使用随机表示的过程模型得出的结论产生的潜在影响。
