Max Planck Institute for Human Cognitive and Brain Sciences.
Faculty of Education and Psychology, Freie Universität Berlin.
Perspect Psychol Sci. 2020 May;15(3):562-571. doi: 10.1177/1745691619895071. Epub 2020 Mar 13.
For human infants, the first years after birth are a period of intense exploration-getting to understand their own competencies in interaction with a complex physical and social environment. In contemporary neuroscience, the predictive-processing framework has been proposed as a general working principle of the human brain, the optimization of predictions about the consequences of one's own actions, and sensory inputs from the environment. However, the predictive-processing framework has rarely been applied to infancy research. We argue that a predictive-processing framework may provide a unifying perspective on several phenomena of infant development and learning that may seem unrelated at first sight. These phenomena include statistical learning principles, infants' motor and proprioceptive learning, and infants' basic understanding of their physical and social environment. We discuss how a predictive-processing perspective can advance the understanding of infants' early learning processes in theory, research, and application.
对于人类婴儿来说，出生后的头几年是一个强烈探索的时期——在与复杂的物理和社会环境相互作用中，逐渐了解自己的能力。在当代神经科学中，预测加工框架被提出来作为人类大脑的一般工作原理，即优化对自身行为后果和来自环境的感官输入的预测。然而，预测加工框架在婴儿研究中很少被应用。我们认为，预测加工框架可以为婴儿发展和学习的几个看似无关的现象提供一个统一的视角。这些现象包括统计学习原则、婴儿的运动和本体感觉学习，以及婴儿对其物理和社会环境的基本理解。我们讨论了预测加工视角如何在理论、研究和应用方面促进对婴儿早期学习过程的理解。
