Rahnuma Tarannum, Jothiraj Sairamya Nanjappan, Kuvar Vishal, Faber Myrthe, Knight Robert T, Kam Julia W Y
Department of Psychology, University of Calgary, Calgary, AB T2N 1N4, Canada.
Hotchkiss Brain Institute, University of Calgary, Calgary, AB T2N 4N1, Canada.
Bioengineering (Basel). 2024 Jul 27;11(8):760. doi: 10.3390/bioengineering11080760.
One key aspect of the human experience is our ongoing stream of thoughts. These thoughts can be broadly categorized into various dimensions, which are associated with different impacts on mood, well-being, and productivity. While the past literature has often identified eye movements associated with a specific thought dimension (task-relatedness) during experimental tasks, few studies have determined if these various thought dimensions can be classified by oculomotor activity during naturalistic tasks. Employing thought sampling, eye tracking, and machine learning, we assessed the classification of nine thought dimensions (task-relatedness, freely moving, stickiness, goal-directedness, internal-external orientation, self-orientation, others orientation, visual modality, and auditory modality) across seven multi-day recordings of seven participants during self-selected computer tasks. Our analyses were based on a total of 1715 thought probes across 63 h of recordings. Automated binary-class classification of the thought dimensions was based on statistical features extracted from eye movement measures, including fixation and saccades. These features all served as input into a random forest (RF) classifier, which was then improved with particle swarm optimization (PSO)-based selection of the best subset of features for classifier performance. The mean Matthews correlation coefficient (MCC) values from the PSO-based RF classifier across the thought dimensions ranged from 0.25 to 0.54, indicating above-chance level performance in all nine thought dimensions across participants and improved performance compared to the RF classifier without feature selection. Our findings highlight the potential of machine learning approaches combined with eye movement measures for the real-time prediction of naturalistic ongoing thoughts, particularly in ecologically valid contexts.
人类体验的一个关键方面是我们持续不断的思维流。这些思维可以大致分为不同的维度，这些维度对情绪、幸福感和生产力有着不同的影响。虽然过去的文献经常在实验任务中确定与特定思维维度（任务相关性）相关的眼动，但很少有研究确定在自然任务中这些不同的思维维度是否可以通过眼动活动来分类。我们采用思维抽样、眼动追踪和机器学习，在七名参与者进行自选计算机任务的七天多记录中，评估了九个思维维度（任务相关性、自由移动、粘性、目标导向性、内外定向、自我定向、他人定向、视觉模态和听觉模态）的分类。我们的分析基于63小时记录中的总共1715个思维探测。思维维度的自动二分类是基于从眼动测量中提取的统计特征，包括注视和扫视。这些特征都作为随机森林（RF）分类器的输入，然后通过基于粒子群优化（PSO）的最佳特征子集选择来提高分类器性能。基于PSO的RF分类器在各个思维维度上的平均马修斯相关系数（MCC）值在0.25至0.54之间，表明在所有九个思维维度上参与者的表现高于随机水平，并且与未进行特征选择的RF分类器相比性能有所提高。我们的研究结果突出了机器学习方法与眼动测量相结合用于实时预测自然状态下持续思维的潜力，特别是在生态有效环境中。
