论文标题
神经网络进入实验室：使用深网作为人类行为的模型
A neural network walks into a lab: towards using deep nets as models for human behavior
论文作者
论文摘要
听起来像是开玩笑的开始，这对许多认知科学家来说已经成为一个有吸引力的前景：在感知和认知任务中，深层神经网络模型（DNN）用作人类行为的模型。尽管DNN已经接管了机器学习，但尝试将其用作人类行为模型仍处于早期阶段。它们可以成为认知科学家工具箱中的多功能模型类吗？我们首先争论为什么DNN有可能成为人类行为的有趣模型。然后，我们讨论如何更充分地实现这种潜力。一方面，我们认为，需要通过认知科学家的目标来重新审视训练，测试和修订的DNN的周期。具体而言，我们认为评估DNN模型与人类行为之间拟合良好的方法迄今已贫穷。另一方面，认知科学可能必须开始使用更复杂的任务（包括较丰富的刺激空间），但是出于独立的原因，这样做可能也是有益的。最后，我们重点介绍了传统的认知过程模型和DNN可能表现出生产性协同作用的途径。
What might sound like the beginning of a joke has become an attractive prospect for many cognitive scientists: the use of deep neural network models (DNNs) as models of human behavior in perceptual and cognitive tasks. Although DNNs have taken over machine learning, attempts to use them as models of human behavior are still in the early stages. Can they become a versatile model class in the cognitive scientist's toolbox? We first argue why DNNs have the potential to be interesting models of human behavior. We then discuss how that potential can be more fully realized. On the one hand, we argue that the cycle of training, testing, and revising DNNs needs to be revisited through the lens of the cognitive scientist's goals. Specifically, we argue that methods for assessing the goodness of fit between DNN models and human behavior have to date been impoverished. On the other hand, cognitive science might have to start using more complex tasks (including richer stimulus spaces), but doing so might be beneficial for DNN-independent reasons as well. Finally, we highlight avenues where traditional cognitive process models and DNNs may show productive synergy.
