Department of Psychology, Carnegie Mellon University.
Psychol Bull. 2014 Jan;140(1):224-55. doi: 10.1037/a0032150. Epub 2013 Mar 11.
Connectionist models have been applied to many phenomena in infant development including perseveration, language learning, categorization, and causal perception. In this article, we discuss the benefits of connectionist networks for the advancement of theories of early development. In particular, connectionist models contribute novel testable predictions, instantiate the theorized mechanism of change, and create a unifying framework for understanding infant learning and development. We relate these benefits to the 2 primary approaches used in connectionist models of infant development. The first approach employs changes in neural processing as the basis for developmental changes, and the second employs changes in infants' experiences. The review sheds light on the unique hurdles faced by each approach as well as the challenges and solutions related to both, particularly with respect to the identification of critical model components, parameter specification, availability of empirical data, and model comparison. Finally, we discuss the future of modeling work as it relates to the study of development. We propose that connectionist networks stand to make a powerful contribution to the generation and revision of theories of early child development. Furthermore, insights from connectionist models of early development can improve the understanding of developmental changes throughout the life span.
连接主义模型已被应用于婴儿发展中的许多现象，包括坚持、语言学习、分类和因果感知。在本文中，我们讨论了连接主义网络对早期发展理论进步的益处。特别是，连接主义模型提供了新颖的可测试预测，实例化了变化的理论机制，并为理解婴儿学习和发展创建了一个统一的框架。我们将这些益处与用于婴儿发展的连接主义模型的 2 种主要方法联系起来。第一种方法将神经处理的变化作为发展变化的基础，第二种方法则利用婴儿经验的变化。这篇综述揭示了每种方法所面临的独特障碍，以及与这两种方法相关的挑战和解决方案，特别是与关键模型组件的识别、参数指定、经验数据的可用性以及模型比较有关。最后，我们讨论了与发展研究相关的建模工作的未来。我们提出，连接主义网络有可能为早期儿童发展理论的产生和修正做出有力贡献。此外，早期发展的连接主义模型的见解可以提高对整个生命周期发展变化的理解。
