A neural network walks into a lab: towards using deep nets as models for human behavior
Wei Ji Ma and Benjamin Peters
Abstract
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.
中文速览
深度神经网络（deep neural networks, DNNs）在机器学习领域大获成功，但能否成为研究人类认知行为的有效模型，目前仍处于探索阶段。作者系统梳理了这一议题，指出要让DNNs真正成为认知科学工具箱的一员，需要重新审视训练与评估流程：首先应通过"生态预训练"让网络获得类似人类的认知先验，再在实验室诊断任务上进行针对性训练，并用反应时、学习曲线等更丰富的行为指标来衡量模型与人类行为的吻合程度，而非仅看准确率。此外，作者还倡导借助DNNs强大的计算能力开发更复杂的人类实验任务，并探索DNNs与传统认知过程模型相互融合的混合路径。这项工作的价值在于为认知科学家提供了一套系统性的方法论框架，使DNNs不再只是机器学习的工具，而是可以与传统模型相互补充、共同揭示人类行为机制的新型研究利器。
原文 arXiv:2005.02181;中英对照 + 大白话阅读 https://aha.fim.ai/paper/2005.02181v1