Moments of inattention to our surroundings may be essential to optimal cognitive functioning. Here, we investigated the hypothesis that humans spontaneously switch between two opposing attentional states during wakefulness—one in which we attend to the external environment (an “online” state) and one in which we disengage from the sensory environment to focus our attention internally (an “offline” state). We created a data-driven model of this proposed alternation between “online” and “offline” attentional states in humans, on a seconds-level timescale. Participants (n = 34) completed a sustained attention to response task while undergoing simultaneous high-density EEG and pupillometry recording and intermittently reporting on their subjective experience. “Online” and “offline” attentional states were initially defined using a cluster analysis applied to multimodal measures of (1) EEG spectral power, (2) pupil diameter, (3) RT, and (4) self-reported subjective experience. We then developed a classifier that labeled trials as belonging to the online or offline cluster with >95% accuracy, without requiring subjective experience data. This allowed us to classify all 5-sec trials in this manner, despite the fact that subjective experience was probed on only a small minority of trials. We report evidence of statistically discriminable “online” and “offline” states matching the hypothesized characteristics. Furthermore, the offline state strongly predicted memory retention for one of two verbal learning tasks encoded immediately prior. Together, these observations suggest that seconds-timescale alternation between online and offline states is a fundamental feature of wakefulness and that this may serve a memory processing function.

对周围环境不注意的时刻可能对最佳认知功能至关重要。在这里，我们研究了这样一种假设，即人类在清醒期间会自发地在两种相反的注意力状态之间切换——一种是我们关注外部环境（一种“在线”状态），另一种是我们脱离感官环境而将注意力集中在内部（ “离线”状态）。我们在秒级时间尺度上创建了一个数据驱动模型，用于描述人类“在线”和“离线”注意力状态之间的这种拟议交替。参与者 ( n= 34）完成了对反应任务的持续关注，同时进行高密度脑电图和瞳孔测量记录并间歇性地报告他们的主观体验。“在线”和“离线”注意力状态最初是使用聚类分析来定义的，该聚类分析应用于 (1) EEG 频谱功率、(2) 瞳孔直径、(3) RT 和 (4) 自我报告的主观体验的多模态测量。然后，我们开发了一个分类器，将试验标记为属于在线或离线集群，准确率 >95%，不需要主观经验数据。这使我们能够以这种方式对所有 5 秒试验进行分类，尽管主观体验仅在少数试验中进行了探讨。我们报告了与假设特征匹配的统计上可区分的“在线”和“离线”状态的证据。此外，离线状态强烈预测了之前编码的两个语言学习任务之一的记忆保留。总之，这些观察表明在线和离线状态之间的秒级时间尺度交替是清醒的基本特征，这可能起到记忆处理功能。