一个神经网络走进实验室：走向使用深度网络作为人类行为模型
A neural network walks into a lab: towards using deep nets as models for human behavior
摘要：
对许多认知科学家来说，这听起来像是一个笑话的开头，但现在已经成为一个有吸引力的前景：使用深度神经网络模型（DNN）作为感知和认知任务中人类行为的模型。尽管DNN已经接管了机器学习，但将其用作人类行为模型的尝试仍处于早期阶段。它们能成为认知科学家工具箱中的通用模型类吗？我们首先讨论为什么DNN有潜力成为有趣的人类行为模型。然后，我们讨论如何更充分地实现这一潜力。一方面，我们认为训练、测试和修改DNN的周期需要通过认知科学家的目标来重新审视。具体来说，我们认为，用于评估DNN模型和人类行为之间的拟合优度的方法到目前为止已经枯竭。另一方面，认知科学可能不得不开始使用更复杂的任务（包括更丰富的刺激空间），但这样做也可能有利于独立于DNN的原因。最后，我们强调了传统认知过程模型和DNN可能显示出生产协同作用的途径。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
