Abstract
Is cognition a collection of loosely connected functions tuned to different tasks, or can there be a general learning algorithm? If such an hypothetical general algorithm did exist, tuned to our world, could it adapt seamlessly to a world with different laws of nature? We consider the theory that predictive coding is such a general rule, and falsify it for one specific neural architecture known for high-performance predictions on natural videos and replication of human visual illusions: PredNet. Our results show that PredNet's high performance generalizes without retraining on a completely different natural video dataset. Yet PredNet cannot be trained to reach even mediocre accuracy on an artificial video dataset created with the rules of the Game of Life (GoL). We also find that a submodule of PredNet, a Convolutional Neural Network trained alone, reaches perfect accuracy on the GoL while being mediocre for natural videos, showing that PredNet's architecture itself is responsible for both the high performance on natural videos and the loss of performance on the GoL. Just as humans cannot predict the dynamics of the GoL, our results suggest that there might be a trade-off between high performance on sensory inputs with different sets of rules.
Abstract (translated)
认知是一个松散连接的函数集合，适合不同的任务，还是有一个通用的学习算法？如果这样一个假设的通用算法确实存在，并与我们的世界相适应，它是否能够无缝地适应具有不同自然规律的世界？我们认为预测编码是一个普遍规律的理论，并为一种特定的神经系统结构所证伪，这种结构以自然视频的高性能预测和人类视觉错觉的复制而闻名：prednet。我们的结果表明，prednet的高性能概括了一个完全不同的自然视频数据集，没有再培训。然而，prednet无法在一个用生命游戏规则（gol）创建的人工视频数据集上达到甚至平庸的精度。我们还发现prednet的一个子模块，一个单独训练的卷积神经网络，在自然视频中表现平平的同时，在GOL上达到了完美的精度，这表明prednet的架构本身对自然视频的高性能和GOL的性能损失都负有责任。正如人类无法预测GOL的动力学一样，我们的研究结果表明，不同规则集的感官输入的高性能之间可能存在权衡。
URL
https://arxiv.org/abs/1904.07969
