论文标题
用于预测人类行为的端到端深度原型和示例模型
End-to-end Deep Prototype and Exemplar Models for Predicting Human Behavior
论文作者
论文摘要
心理学中类别学习的传统模型集中在类别级别的代表性上，而不是刺激水平，即使两者可能相互作用。在这种模型中采用的刺激表示是由实验者手工设计的，从人类判断中推断出来，或者是从验证的深层神经网络中借来的，这些神经网络本身就是类别学习的竞争模型。在这项工作中，我们扩展了经典的原型和示例模型，以从原始输入中共同学习刺激和类别表示。这种新的模型可以通过深神网络（DNN）和经过训练的端到端进行参数化。遵循他们的名字，我们将它们称为深型原型模型，深度示例模型和深层混合模型。与典型的DNN相比，我们发现他们的认知启发的对应物都为人类行为提供了更好的内在拟合度并改善了地面实际分类。
Traditional models of category learning in psychology focus on representation at the category level as opposed to the stimulus level, even though the two are likely to interact. The stimulus representations employed in such models are either hand-designed by the experimenter, inferred circuitously from human judgments, or borrowed from pretrained deep neural networks that are themselves competing models of category learning. In this work, we extend classic prototype and exemplar models to learn both stimulus and category representations jointly from raw input. This new class of models can be parameterized by deep neural networks (DNN) and trained end-to-end. Following their namesakes, we refer to them as Deep Prototype Models, Deep Exemplar Models, and Deep Gaussian Mixture Models. Compared to typical DNNs, we find that their cognitively inspired counterparts both provide better intrinsic fit to human behavior and improve ground-truth classification.
