论文标题
随机梯度下降捕获孩子如何学习物理学
Stochastic Gradient Descent Captures How Children Learn About Physics
论文作者
论文摘要
随着孩子的年龄增长，他们对周围的身体过程有了直观的理解。它们沿着发育轨迹移动，在先前的实证研究中，这些轨迹已被广泛绘制出来。我们研究儿童的发育轨迹与人工系统的学习轨迹相比。具体而言，我们研究了一种观念，即认知发展是由某种形式的随机优化程序导致的。为此，我们使用随机梯度下降训练现代生成神经网络模型。然后，我们使用发展心理学文献的方法来探究该模型的物理理解，以不同的优化程度。我们发现该模型的学习轨迹捕捉了儿童的发展轨迹，从而为发展的想法提供了支持，作为随机优化。
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.
