As children grow older, they develop an intuitive understanding of the physical processes around them. They move along developmental trajectories, which have been mapped out extensively in previous empirical research. We investigate how children's developmental trajectories compare to the learning trajectories of artificial systems. Specifically, we examine the idea that cognitive development results from some form of stochastic optimization procedure. For this purpose, we train a modern generative neural network model using stochastic gradient descent. We then use methods from the developmental psychology literature to probe the physical understanding of this model at different degrees of optimization. We find that the model's learning trajectory captures the developmental trajectories of children, thereby providing support to the idea of development as stochastic optimization.
翻译:随着儿童长大,他们可以直觉地了解周围的物理过程。他们沿着发展轨迹前进,这些轨迹在以前的实证研究中已广泛绘制出来。我们调查儿童的发展轨迹如何与人工系统的学习轨迹相比较。具体地说,我们研究认知发展来自某种形式的随机优化程序。为此目的,我们用随机梯度梯度下降来培训现代基因神经网络模型。我们随后使用发展心理学文献的方法在不同程度上探索这一模式的物理理解。我们发现该模型的学习轨迹捕捉了儿童的发育轨迹,从而为发展作为随机优化的理念提供支持。