ABSTRACT

To reveal the underlying cause of disfluency, several authors related the pattern of disfluencies to difficulties at specific levels of production, using a Network Task. Given that disfluencies are multifactorial, we combined this paradigm with eye-tracking to disentangle disfluency related to word preparation difficulties from others (e.g. stalling strategies). We manipulated lexical and grammatical selection difficulty. In Experiment 1, lines connecting the pictures varied in length, which led participants to use a strategy and inspect other areas than the upcoming picture when pictures were preceded by long lines. Experiment 2 only used short lines. In both experiments, lexical selection difficulty promoted self-corrections, pauses and longer fixation latency prior to naming. Multivariate Pattern Analyses demonstrated that disfluency and eye-movement data patterns can predict lexical selection difficulty. Eye-tracking could provide complementary information about network tasks, by disentangling disfluencies related to picture naming from disfluencies related to self-monitoring or stalling strategies.

摘要

为了揭示不流畅的根本原因，几位作者使用网络任务将不流畅的模式与特定生产水平的困难联系起来。鉴于不流畅是多因素的，我们将这种范式与眼动追踪相结合，以解决与其他人（例如拖延策略）有关的单词准备困难相关的不流畅。我们操纵了词汇和语法选择难度。在实验 1 中，连接图片的线条长度各不相同，这导致参与者使用一种策略并在图片之前有长线条时检查即将出现的图片以外的其他区域。实验 2 只使用了短线。在这两个实验中，词汇选择困难促进了命名前的自我修正、停顿和更长的注视潜伏期。多元模式分析表明，不流畅和眼动数据模式可以预测词汇选择难度。通过将与图片命名相关的不流畅与与自我监控或拖延策略相关的不流畅分开，眼动追踪可以提供有关网络任务的补充信息。