Despite the groundbreaking successes of neural networks, contemporary models require extensive training with massive datasets and exhibit poor out-of-sample generalization. One proposed solution is to build systematicity and domain-specific constraints into the model, echoing the tenets of classical, symbolic cognitive architectures. In this paper, we consider the limitations of this approach by examining human adults' ability to learn an abstract reasoning task from a brief instructional tutorial and explanatory feedback for incorrect responses, demonstrating that human learning dynamics and ability to generalize outside the range of the training examples differ drastically from those of a representative neural network model, and that the model is brittle to changes in features not anticipated by its authors. We present further evidence from human data that the ability to consistently solve the puzzles was associated with education, particularly basic mathematics education, and with the ability to provide a reliably identifiable, valid description of the strategy used. We propose that rapid learning and systematic generalization in humans may depend on a gradual, experience-dependent process of learning-to-learn using instructions and explanations to guide the construction of explicit abstract rules that support generalizable inferences.

中文翻译：

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人类快速学习和系统泛化的基础是什么

尽管神经网络取得了突破性的成功，但当代模型需要对大量数据集进行广泛的训练，并且表现出较差的样本外泛化能力。一个提议的解决方案是在模型中建立系统性和特定领域的约束，呼应经典、符号认知架构的原则。在本文中，我们通过检查人类成年人从简短的教学教程中学习抽象推理任务的能力和对错误反应的解释性反馈来考虑这种方法的局限性，证明了人类学习动态和在训练范围之外的泛化能力示例与代表性神经网络模型的示例截然不同，并且该模型对于其作者未预料到的特征变化很脆弱。我们从人类数据中提供了进一步的证据，表明始终如一地解决难题的能力与教育相关，尤其是基础数学教育，以及提供所用策略的可靠可识别、有效描述的能力。我们提出，人类的快速学习和系统泛化可能取决于一个循序渐进的、依赖于经验的学习过程，使用说明和解释来指导构建支持泛化推理的明确抽象规则。