Division of Human Communication, Development and Hearing, School of Health Sciences, University of Manchester, Manchester, UK.
Department of Psychology, University of Lancaster, Lancaster, UK.
Dev Sci. 2018 Jul;21(4):e12629. doi: 10.1111/desc.12629. Epub 2017 Oct 26.
Infants are curious learners who drive their own cognitive development by imposing structure on their learning environment as they explore. Understanding the mechanisms by which infants structure their own learning is therefore critical to our understanding of development. Here we propose an explicit mechanism for intrinsically motivated information selection that maximizes learning. We first present a neurocomputational model of infant visual category learning, capturing existing empirical data on the role of environmental complexity on learning. Next we "set the model free", allowing it to select its own stimuli based on a formalization of curiosity and three alternative selection mechanisms. We demonstrate that maximal learning emerges when the model is able to maximize stimulus novelty relative to its internal states, depending on the interaction across learning between the structure of the environment and the plasticity in the learner itself. We discuss the implications of this new curiosity mechanism for both existing computational models of reinforcement learning and for our understanding of this fundamental mechanism in early development.
婴儿是好奇的学习者，他们在探索时通过对学习环境施加结构来推动自己的认知发展。因此，了解婴儿构建自己学习的机制对于我们理解发展至关重要。在这里，我们提出了一种用于内在动机信息选择的明确机制，该机制可以最大限度地提高学习效果。我们首先提出了一个婴儿视觉类别学习的神经计算模型，该模型捕捉了关于环境复杂性对学习影响的现有实证数据。接下来，我们“让模型自由发挥”，允许它根据好奇心的形式化和三种替代选择机制来选择自己的刺激。我们证明，当模型能够根据其内部状态最大化刺激新颖性时，最大程度的学习就会出现，这取决于学习过程中环境结构和学习者自身可塑性之间的相互作用。我们讨论了这种新的好奇心机制对强化学习的现有计算模型以及我们对早期发展中这一基本机制的理解的影响。
