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Emotional Valence Precedes Semantic Maturation of Words: A Longitudinal Computational Study of Early Verbal Emotional Anchoring
Cognitive Science ( IF 2.3 ) Pub Date : 2021-07-19 , DOI: 10.1111/cogs.13026
José Á Martínez-Huertas 1 , Guillermo Jorge-Botana 2 , Ricardo Olmos 1
Affiliation  

We present a longitudinal computational study on the connection between emotional and amodal word representations from a developmental perspective. In this study, children's and adult word representations were generated using the latent semantic analysis (LSA) vector space model and Word Maturity methodology. Some children's word representations were used to set a mapping function between amodal and emotional word representations with a neural network model using ratings from 9-year-old children. The neural network was trained and validated in the child semantic space. Then, the resulting neural network was tested with adult word representations using ratings from an adult data set. Samples of 1210 and 5315 words were used in the child and the adult semantic spaces, respectively. Results suggested that the emotional valence of words can be predicted from amodal vector representations even at the child stage, and accurate emotional propagation was found in the adult word vector representations. In this way, different propagative processes were observed in the adult semantic space. These findings highlight a potential mechanism for early verbal emotional anchoring. Moreover, different multiple linear regression and mixed-effect models revealed moderation effects for the performance of the longitudinal computational model. First, words with early maturation and subsequent semantic definition promoted emotional propagation. Second, an interaction effect between age of acquisition and abstractness was found to explain model performance. The theoretical and methodological implications are discussed.

中文翻译:

情感价先于词的语义成熟:早期言语情感锚定的纵向计算研究

我们从发展的角度对情感和非模态词表示之间的联系进行了纵向计算研究。在这项研究中,使用潜在语义分析 (LSA) 向量空间模型和词成熟度方法生成儿童和成人词表示。一些儿童的词表示被用来通过神经网络模型使用 9 岁儿童的评分来设置 amodal 和情绪词表示之间的映射函数。神经网络在子语义空间中进行了训练和验证。然后,使用来自成人数据集的评级,用成人词表示测试生成的神经网络。分别在儿童和成人语义空间中使用了 1210 个和 5315 个单词的样本。结果表明,即使在儿童阶段,也可以从非模态向量表示中预测单词的情感效价,并且在成人词向量表示中发现了准确的情感传播。通过这种方式,在成人语义空间中观察到了不同的传播过程。这些发现突出了早期语言情绪锚定的潜在机制。此外,不同的多元线性回归和混合效应模型揭示了纵向计算模型性能的调节效应。首先,早熟的词和随后的语义定义促进了情感的传播。其次,发现习得年龄和抽象性之间的交互作用可以解释模型性能。讨论了理论和方法的影响。
更新日期:2021-07-20
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