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Effective Scheduling of Looking and Talking During Rapid Automatized Naming

机译：快速自动命名过程中的有效视听安排

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Rapid Automatized Naming (RAN) is strongly related to literacy gains in developing readers, reading disabilities and reading ability in children and adults. Because successful RAN performance depends on the close coordination of a number of abilities, it is unclear what specific skills drive this RAN-reading relationship. The current study used concurrent recordings of young adult participants’ vocalizations and eye movements during the RAN task to assess how individual variation in RAN performance depends on the coordination of visual and vocal processes. Results showed that fast RAN times are facilitated by having the eyes one or more items ahead of the current vocalization, as long as the eyes do not get so far ahead of the voice as to require a regressive eye movement to an earlier item. These data suggest that optimizing RAN performance is a problem of scheduling eye movements and vocalization given memory constraints and the efficiency of encoding and articulatory control. Both RAN completion time (conventionally used to indicate RAN performance) and eye-voice relations predicted some aspects of participants’ eye movements on a separate sentence reading task. However, eye-voice relations predicted additional features of first-pass reading that were not predicted by RAN completion time. This shows that measurement of eye-voice patterns can identify important aspects of individual variation in reading that are not identified by the standard measure of RAN performance. We argue that RAN performance predicts reading ability because both tasks entail challenges of scheduling cognitive and linguistic processes that operate simultaneously on multiple linguistic inputs.
机译：快速自动命名（RAN）与提高儿童，成年人的阅读障碍，阅读障碍和阅读能力方面的读写能力密切相关。由于成功的RAN表现取决于许多能力的密切配合，因此尚不清楚是什么特定技能推动了这种RAN阅读关系。当前的研究使用了RAN任务期间年轻参与者的发声和眼睛运动的并发记录，以评估RAN性能的个体差异如何取决于视觉和声音过程的协调。结果表明，只要眼睛不在声音之前那么远以至于需要对较早的项进行回归眼动，则使眼睛比当前发声提前一个或多个项可以加快RAN时间。这些数据表明，优化RAN性能是在给定内存限制以及编码和发音控制效率的情况下安排眼睛运动和发声的问题。 RAN完成时间（通常用于表示RAN的表现）和眼音关系都可以预测参与者在单独的句子阅读任务中眼动的某些方面。但是，眼音关系预测了RAN完成时间无法预测的首过读取的其他特征。这表明眼图模式的测量可以识别阅读中的个体差异的重要方面，而这是RAN性能的标准测量方法无法识别的。我们认为RAN的性能可预测阅读能力，因为这两项任务都需要安排同时在多个语言输入上运行的认知和语言过程的挑战。

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