We present a computational model capable of simulating aspects of human knowledge for thousands of real-world concepts. Our approach involves a pretrained transformer network that is further fine-tuned on large data sets of participant-generated feature norms. We show that such a model can successfully extrapolate from its training data, and predict human knowledge for new concepts and features. We apply our model to stimuli from 25 previous experiments in semantic cognition research and show that it reproduces many findings on semantic verification, concept typicality, feature distribution, and semantic similarity. We also compare our model against several variants, and by doing so, establish the model properties that are necessary for good prediction. The success of our approach shows how a combination of language data and (laboratory-based) psychological data can be used to build models with rich world knowledge. Such models can be used in the service of new psychological applications, such as the modeling of naturalistic semantic verification and knowledge retrieval, as well as the modeling of real-world categorization, decision-making, and reasoning.

中文翻译：

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人类概念知识的变压器网络。


我们提出了一种计算模型，能够模拟数千个现实世界概念的人类知识的各个方面。我们的方法涉及一个预训练的变压器网络，该网络在参与者生成的特征规范的大数据集上进一步进行微调。我们证明，这样的模型可以成功地从其训练数据中推断出来，并预测人类对新概念和特征的知识。我们将我们的模型应用于语义认知研究中 25 个先前实验的刺激，并表明它重现了语义验证、概念典型性、特征分布和语义相似性方面的许多发现。我们还将我们的模型与几个变体进行比较，并通过这样做，建立良好预测所需的模型属性。我们方法的成功表明，如何结合语言数据和（基于实验室的）心理数据来构建具有丰富世界知识的模型。这些模型可以用于新的心理学应用，例如自然语义验证和知识检索的建模，以及现实世界分类、决策和推理的建模。