Eye behavior is increasingly used as an indicator of internal versus external focus of attention both in research and application. However, available findings are partly inconsistent, which might be attributed to the different nature of the employed types of internal and external cognition tasks. The present study, therefore, investigated how consistently different eye parameters respond to internal versus external attentional focus across three task modalities: numerical, verbal, and visuo‐spatial. Three eye parameters robustly differentiated between internal and external attentional focus across all tasks. Blinks, pupil diameter variance, and fixation disparity variance were consistently increased during internally directed attention. We also observed substantial attentional focus effects on other parameters (pupil diameter, fixation disparity, saccades, and microsaccades), but they were moderated by task type. Single‐trial analysis of our data using machine learning techniques further confirmed our results: Classifying the focus of attention by means of eye tracking works well across participants, but generalizing across tasks proves to be challenging. Based on the effects of task type on eye parameters, we discuss what eye parameters are best suited as indicators of internal versus external attentional focus in different settings.

中文翻译：

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眼睛参数指示内部注意力与外部注意力焦点的可靠性如何？

在研究和应用中，眼睛行为越来越多地被用作内部与外部关注焦点的指标。然而，现有的研究结果部分不一致，这可能归因于所采用的内部和外部认知任务类型的不同性质。因此，本研究调查了不同的眼睛参数如何一致地响应三种任务模式中的内部和外部注意焦点：数字、语言和视觉空间。三个眼睛参数在所有任务中强烈区分内部和外部注意力。在内部定向注意期间，眨眼、瞳孔直径差异和注视差异差异持续增加。我们还观察到对其他参数（瞳孔直径、注视差异、扫视和微扫视），但它们按任务类型进行调节。使用机器学习技术对我们的数据进行的单次试验分析进一步证实了我们的结果：通过眼动追踪对注意力进行分类对参与者很有效，但跨任务泛化被证明具有挑战性。基于任务类型对眼睛参数的影响，我们讨论了哪些眼睛参数最适合作为不同环境下内部注意力和外部注意力的指标。