Chen Qi, Mirman Daniel
Center for Studies of Psychological Application and School of Psychology, South China Normal University; Moss Rehabilitation Research Institute.
Cogn Sci. 2015 Apr;39(3):538-58. doi: 10.1111/cogs.12156. Epub 2014 Aug 23.
Computational modeling and eye-tracking were used to investigate how phonological and semantic information interact to influence the time course of spoken word recognition. We extended our recent models (Chen & Mirman, 2012; Mirman, Britt, & Chen, 2013) to account for new evidence that competition among phonological neighbors influences activation of semantically related concepts during spoken word recognition (Apfelbaum, Blumstein, & McMurray, 2011). The model made a novel prediction: Semantic input modulates the effect of phonological neighbors on target word processing, producing an approximately inverted-U-shaped pattern with a high phonological density advantage at an intermediate level of semantic input-in contrast to the typical disadvantage for high phonological density words in spoken word recognition. This prediction was confirmed with a new analysis of the Apfelbaum et al. data and in a visual world paradigm experiment with preview duration serving as a manipulation of strength of semantic input. These results are consistent with our previous claim that strongly active neighbors produce net inhibitory effects and weakly active neighbors produce net facilitative effects.
我们运用计算建模和眼动追踪技术,来探究语音和语义信息如何相互作用,从而影响口语单词识别的时间进程。我们扩展了近期的模型(Chen & Mirman,2012;Mirman、Britt & Chen,2013),以解释新的证据,即语音邻域之间的竞争会影响口语单词识别过程中语义相关概念的激活(Apfelbaum、Blumstein & McMurray,2011)。该模型做出了一个新颖的预测:语义输入会调节语音邻域对目标词加工的影响,在语义输入的中间水平产生近似倒U形模式,其中语音密度高具有优势——这与口语单词识别中语音密度高的单词通常具有劣势形成对比。通过对Apfelbaum等人的数据进行新的分析,并在一个视觉世界范式实验中得到了证实,该实验中预览持续时间作为语义输入强度的一种操纵手段。这些结果与我们之前的观点一致,即高度活跃的邻域会产生净抑制作用,而活跃度低的邻域会产生净促进作用。