Psychological perspectives on implicit regularization: A model of retrieval-induced forgetting (RIF)

Abstract

While learning theory suggests different forms of explicit regularization to guarantee small generalization error, deep learning models may introduce some sort of implicit regularization that tries to find a solution with small complexity, but neither does not include a penalty term nor does not directly modifies the optimization method. Recent research in deep learning pointed to the importance of proper understanding the underlying mechanisms of implicit regularization to elucidate the nature of generalization ability. This study is aimed at looking at implicit regularization from a psychological perspective with regard to the phenomena of retrieval-induced forgetting (RIF). The findings of this study may greatly assist in solving the major problems of proper understanding the deep learning procedure, improving the generalization ability, and the capacity control.