Eye-movements can help disentangle mechanisms underlying disfluency

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.

为揭示言语不流畅（disfluency）的潜在成因，已有多位研究者采用网络任务（Network Task）范式，将不流畅模式与语言产出特定阶段的困难建立关联。鉴于言语不流畅受多因素影响，本研究将该范式与眼动追踪（eye-tracking）技术相结合，以区分由词汇准备困难引发的不流畅与其他类型的不流畅（例如拖延策略相关不流畅）。我们对词汇选择与语法选择的难度进行了操纵。在实验1中，连接图片的线条长度各不相同：当图片前存在长线条时，被试会采取特定策略，转而注视待命名图片之外的其他区域。实验2仅使用短线条。在两项实验中，词汇选择难度的提升均会导致自我修正、言语停顿以及命名前注视潜伏期的延长。多变量模式分析（Multivariate Pattern Analyses）结果显示，不流畅数据与眼动数据的模式可用于预测词汇选择难度。通过区分图片命名相关的不流畅与自我监控或拖延策略相关的不流畅，眼动追踪技术可为网络任务研究提供补充性信息。