Department of Psychology & Department of Communication, Stanford University.
Graduate School of Education, Stanford University.
Multivariate Behav Res. 2024 Nov-Dec;59(6):1211-1219. doi: 10.1080/00273171.2023.2229305. Epub 2023 Jul 13.
Advances in ability to comprehensively record individuals' digital lives and in AI modeling of those data facilitate new possibilities for describing, predicting, and generating a wide variety of behavioral processes. In this paper, we consider these advances from a person-specific perspective, including whether the pervasive concerns about of results might be productively reframed with respect to of models, and how self-supervision and new deep neural network architectures that facilitate transfer learning can be applied in a person-specific way to the super-intensive longitudinal data arriving in the Human Screenome Project. In developing the possibilities, we suggest Molenaar add a statement to the person-specific Manifesto - "In short, the concerns about commonly leveled at the person-specific paradigm are unfounded and can be fully and completely replaced with discussion and demonstrations of ."
能力的进步能够全面记录个人的数字生活，并对这些数据进行人工智能建模，为描述、预测和生成各种行为过程提供了新的可能性。在本文中，我们从个体的角度考虑这些进步，包括关于结果的普遍关注是否可以从模型的角度进行有益的重新构建，以及自我监督和促进迁移学习的新的深度神经网络架构如何可以以特定于个体的方式应用于人类屏幕组学计划中出现的超密集纵向数据。在探讨这些可能性时，我们建议 Molenaar 在特定于个体的宣言中添加一句话：“简而言之，通常针对特定于个体的范式提出的对结果的担忧是没有根据的，可以完全用关于模型的讨论和演示来替代。”
