Semantic significance: a new measure of feature salience
Semantic significance: a new measure of feature salience
复制标题
DOI：
10.3758/s13421-013-0365-y
复制
发表时间：
2014-04-01
影响因子：
2.4
通讯作者：
Mammarella, Nicola
中科院分区：
文献类型：
--
作者：
Montefinese, Maria;Ambrosini, Ettore;Mammarella, Nicola
根据基于特征的语义记忆模型，概念是由一组语义特征来描述的，这些语义特征以不同的权重贡献于概念的含义。有趣的是，这个理论框架引入了许多维度来描述语义特征。最近，我们提出了一个新的参数来衡量语义特征对于概念表示的重要性——即语义重要性。在这里，通过快速验证任务，我们测试了指数的预测价值，并研究了概念维度和特征维度对参与者表现的相对作用。结果表明，语义重要性可以很好地预测参与者的验证延迟，并表明它可以有效地捕获特征的显着性，以计算给定概念的含义。因此，我们建议语义重要性可以被认为是给定概念表示中特征重要性的有效指标。此外，我们认为它可能对基于特征的语义记忆模型产生直接影响，作为理解概念表示的重要附加因素。
According to the feature-based model of semantic memory, concepts are described by a set of semantic features that contribute, with different weights, to the meaning of a concept. Interestingly, this theoretical framework has introduced numerous dimensions to describe semantic features. Recently, we proposed a new parameter to measure the importance of a semantic feature for the conceptual representation-that is, semantic significance. Here, with speeded verification tasks, we tested the predictive value of our index and investigated the relative roles of conceptual and featural dimensions on the participants' performance. The results showed that semantic significance is a good predictor of participants' verification latencies and suggested that it efficiently captures the salience of a feature for the computation of the meaning of a given concept. Therefore, we suggest that semantic significance can be considered an effective index of the importance of a feature in a given conceptual representation. Moreover, we propose that it may have straightforward implications for feature-based models of semantic memory, as an important additional factor for understanding conceptual representation.