Despite the essential role of statistical learning in shaping human behavior, there are still controversies concerning its measurement. In this paper, we present a novel online target-detection task in an acoustic word segmentation paradigm, which is able to track the process of learning and does not build on deliberation and decision making. Beside testing the novel online task, we also examined its relationship with two offline measures: the traditional two-alternative forced choice (2AFC) task, and the statistically-induced chunking recall (SICR) task (Isbilen et al., 2017). Participants showed a significant learning effect on the online task, reflected in the decrease of reaction times during training and in the differences between reaction times to predictable versus unpredictable targets. Online learning scores correlated with the 2AFC scores, but this association was only present when participants did not have explicit knowledge about stimuli. SICR scores were not associated with any of the other measures. The internal consistency was higher for online learning measures than for the other two tasks. These findings show that the online target detection task is a good tool for assessing statistical learning, and invite further research on its psychometric properties.

尽管统计学习在塑造人类行为中起着至关重要的作用，但有关其测量方法仍存在争议。在本文中，我们提出了一种基于声学分词范例的新型在线目标检测任务，该任务能够跟踪学习过程，而无需进行深思熟虑和决策。除了测试新颖的在线任务外，我们还检查了它与两种离线度量的关系：传统的两选式强制选择（2AFC）任务和统计诱导的分块召回（SICR）任务（Isbilen等人，2017）。参与者对在线任务表现出显着的学习效果，这反映在训练过程中反应时间的减少以及对可预测目标与不可预测目标的反应时间之间的差异上。在线学习分数与2AFC分数相关，但是仅当参与者对刺激没有明确知识时才存在这种关联。SICR分数与其他任何指标均不相关。在线学习措施的内部一致性高于其他两项任务。这些发现表明，在线目标检测任务是评估统计学习的好工具，并邀请对其心理测量特性进行进一步研究。