We know that reading involves coordination between textual characteristics and visual attention, but research linking eye movements during reading and comprehension assessed after reading is surprisingly limited, especially for reading long connected texts. We tested two competing possibilities: (a) the weak association hypothesis: Links between eye movements and comprehension are weak and short‐lived, versus (b) the strong association hypothesis: The two are robustly linked, even after a delay. Using a predictive modeling approach, we trained regression models to predict comprehension scores from global eye movement features, using participant‐level cross‐validation to ensure that the models generalize across participants. We used data from three studies in which readers (Ns = 104, 130, 147) answered multiple‐choice comprehension questions ~30 min after reading a 6,500‐word text, or after reading up to eight 1,000‐word texts. The models generated accurate predictions of participants' text comprehension scores (correlations between observed and predicted comprehension: 0.384, 0.362, 0.372, ps < .001), in line with the strong association hypothesis. We found that making more, but shorter fixations, consistently predicted comprehension across all studies. Furthermore, models trained on one study's data could successfully predict comprehension on the others, suggesting generalizability across studies. Collectively, these findings suggest that there is a robust link between eye movements and subsequent comprehension of a long connected text, thereby connecting theories of low‐level eye movements with those of higher order text processing during reading.

中文翻译：

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眼动揭示了后来对长连接文本的理解

我们知道阅读涉及文本特征和视觉注意力之间的协调，但将阅读过程中的眼球运动与阅读后评估的理解联系起来的研究却非常有限，尤其是对于阅读长篇大论的文本。我们测试了两种相互竞争的可能性：(a) 弱关联假设：眼球运动和理解力之间的联系是微弱且短暂的，而 (b) 强关联假设：即使在延迟之后，两者也有很强的联系。使用预测建模方法，我们训练回归模型来预测全局眼动特征的理解分数，使用参与者级别的交叉验证来确保模型在参与者之间泛化。我们使用了来自三项研究的数据，其中读者（Ns = 104, 130, 147) 在阅读 6,500 字的文本后约 30 分钟或阅读最多 8 篇 1,000 字的文本后回答多项选择题。该模型生成了参与者文本理解分数的准确预测（观察到的和预测的理解之间的相关性：0.384、0.362、0.372、ps < .001)，符合强关联假设。我们发现，在所有研究中，进行更多但更短的注视一致地预测了理解力。此外，对一项研究的数据进行训练的模型可以成功预测对其他研究的理解，这表明研究具有普遍性。总的来说，这些发现表明，眼球运动与随后对长连接文本的理解之间存在强大的联系，从而将低级眼球运动理论与阅读过程中的高阶文本处理理论联系起来。