Abstract The Dream Catcher test defines the criteria for a genuine discovery of the neural constituents of phenomenal consciousness. Passing the test implies that some patterns of purely brain-based data directly correspond to the subjective features of phenomenal experience, which would help to bridge the explanatory gap between consciousness and brain. Here, we conducted the Dream Catcher test for the first time in a step-wise and simplified form, capturing its core idea. The Dream Catcher experiment involved a Data Team, which measured participants’ brain activity during sleep and collected dream reports, and a blinded Analysis Team, which was challenged to predict, based solely on brain measurements, whether or not a participant had a dream experience. Using a serial-awakening paradigm, the Data Team prepared 54 1-min polysomnograms of non-rapid eye movement sleep—27 of dreamful sleep and 27 of dreamless sleep (three of each condition from each of the nine participants)—redacting from them all associated participant and dream information. The Analysis Team attempted to classify each recording as either dreamless or dreamful using an unsupervised machine learning classifier, based on hypothesis-driven, extracted features of electroencephalography (EEG) spectral power and electrode location. The procedure was repeated over five iterations with a gradual removal of blindness. At no level of blindness did the Analysis Team perform significantly better than chance, suggesting that EEG spectral power could not be utilized to detect signatures specific to phenomenal consciousness in these data. This study marks the first step towards realizing the Dream Catcher test in practice.

中文翻译：

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捕梦网实验：盲目的分析未能检测出脑电波频谱能量中梦意识的标记

摘要Dream Catcher测试定义了真正发现现象意识的神经成分的标准。通过测试意味着某些纯粹基于大脑的数据模式直接对应于现象体验的主观特征，这将有助于弥合意识与大脑之间的解释性鸿沟。在这里，我们首次以逐步和简化的形式进行了Dream Catcher测试，以捕捉其核心思想。Dream Catcher实验包括一个数据团队，该团队测量参与者在睡眠期间的大脑活动并收集梦境报告，以及一个盲目的分析团队，该团队被挑战以仅根据大脑测量来预测参与者是否有梦境经历。使用串行唤醒范例 数据团队准备了54个1分钟的非快速眼动睡眠多导睡眠图-27个梦幻梦和27个梦幻梦（9位参与者中的每一种情况中的3种）-从他们中删除了所有相关的参与者和梦想信息。分析小组尝试基于假设驱动的提取的脑电图（EEG）频谱功率和电极位置特征，使用无监督的机器学习分类器将每条记录分类为无梦或有梦。该过程重复了五次迭代，并逐渐消除了盲目性。在没有盲目性的情况下，分析团队的表现明显好于偶然性，这表明无法利用EEG频谱功率来检测这些数据中特定于现象意识的特征。