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An Abundance of Riches: Cross-Task Comparisons of Semantic Richness Effects in Visual Word Recognition

机译:丰富的财富:视觉单词识别中语义丰富度效果的跨任务比较

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摘要

There is considerable evidence (e.g., Pexman et al., ) that semantically rich words, which are associated with relatively more semantic information, are recognized faster across different lexical processing tasks. The present study extends this earlier work by providing the most comprehensive evaluation to date of semantic richness effects on visual word recognition performance. Specifically, using mixed effects analyses to control for the influence of correlated lexical variables, we considered the impact of number of features, number of senses, semantic neighborhood density, imageability, and body–object interaction across five visual word recognition tasks: standard lexical decision, goo-go lexical decision, speeded pronunciation, progressive demasking, and semantic classification. Semantic richness effects could be reliably detected in all tasks of lexical processing, indicating that semantic representations, particularly their imaginal and featural aspects, play a fundamental role in visual word recognition. However, there was also evidence that the strength of certain richness effects could be flexibly and adaptively modulated by task demands, consistent with an intriguing interplay between task-specific mechanisms and differentiated semantic processing.
机译:有相当多的证据(例如Pexman等人)表明,与相对更多的语义信息相关联的语义丰富的单词在不同的词法处理任务中被更快地识别。本研究通过提供迄今为止最丰富的评估来丰富语义对视觉单词识别性能的影响,从而扩展了此早期工作。具体来说,我们使用混合效应分析来控制相关词汇变量的影响,我们考虑了特征数量,感官数量,语义邻域密度,可成像性以及跨五个视觉单词识别任务的身体-对象交互的影响:标准词汇决策,进行/不进行词汇决策,加快发音速度,进行渐进式屏蔽以及语义分类。可以在所有词法处理任务中可靠地检测到语义丰富度效应,这表明语义表示,尤其是它们的虚构和特征方面,在视觉单词识别中起着基本作用。但是,也有证据表明,可以根据任务需求灵活而自适应地调节某些丰富度效果的强度,这与特定于任务的机制和差异化语义处理之间有趣的相互作用相一致。

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