Second-Order Correlation Learning of Dynamic Stimuli: Evidence from Infants and Computational Modeling

作     者：Rakison, David H. Benton, Deon T. 

作者机构：Carnegie Mellon Univ Dept Psychol 5000 Forbes Ave Pittsburgh PA 15213 USA 

出 版 物：《INFANCY》 (婴儿期)

年 卷 期：2019年第24卷第1期

页      面：57-78页

核心收录：

学科分类：0402[教育学-心理学(可授教育学、理学学位）] 1002[医学-临床医学] 100202[医学-儿科学] 10[医学] 

基　　金：National Science Foundation [BCS-1228322] 

主　　题：COMPUTER simulation LEARNING ARTIFICIAL neural networks SENSORY stimulation SENSORY stimulation in newborn infants MEDICAL coding 

摘      要：We present two habituation experiments that examined 20- and 26-month-olds ability to engage in second-order correlation learning for static and dynamic features, whereby learned associations between two pairs of features (e.g., P and Q, P and R) are generalized to the features that were not presented together (e.g., Q and R). We also present results from an associative learning mechanism that was implemented as an autoencoder parallel distributed processing (PDP) network in which second-order correlation learning is shown to be an emergent property of the dynamics of the network. The experiments and simulation demonstrate that 20- and 26-month-olds as well as neural networks are capable of second-order correlation learning in a category context for internal features of dynamic objects. However, the model predicts-and Experiment 3 demonstrates-that 20- and 26-month-olds are unable to encode second-order correlations in a noncategory context for dynamic objects with internal features. It is proposed that the ability to learn second-order correlations represents a powerful but as yet unexplored process for generalization in the first years of life.