野生范畴化：将认知模型推广到跨语言的自然主义数据
Categorization in the Wild: Generalizing Cognitive Models to Naturalistic Data across Languages
摘要：
动物或家具等类别是在很小的时候就获得的，在处理、组织和交流世界知识方面发挥着重要作用。类别存在于不同的文化中：它们允许有效地代表世界的复杂性，社区的成员强烈同意它们的性质，揭示了共同的心理表征。然而，类别学习和表征的模型通常是在小规模实验的数据上进行测试的，这些实验涉及具有人为限制特征的小组概念;实验主要涉及选定的文化和社会经济群体的参与者（通常涉及以英语为母语的西方人，如美国大学生）。这项工作研究了分类模型是否推广到（a）接近人类生活环境的丰富和嘈杂的数据;以及（b）跨语言和文化。我们提出了一个贝叶斯认知模型，旨在共同学习类别和它们的结构化表示从自然语言文本，使我们能够（a）大规模评估性能，（b）将我们的模型应用到一组不同的语言。我们表明，有意义的类别，包括数百个概念和丰富的结构化特征表示出现在不同的语言。我们的工作说明了最近在计算建模和大规模自然数据集的认知科学研究的进展的潜力。
英文摘要：
Categories such as animal or furniture are acquired at an early age and play an important role in processing, organizing, and communicating world knowledge. Categories exist across cultures: they allow to efficiently represent the complexity of the world, and members of a community strongly agree on their nature, revealing a shared mental representation. Models of category learning and representation, however, are typically tested on data from small-scale experiments involving small sets of concepts with artificially restricted features; and experiments predominantly involve participants of selected cultural and socio-economical groups (very often involving western native speakers of English such as U.S. college students) . This work investigates whether models of categorization generalize (a) to rich and noisy data approximating the environment humans live in; and (b) across languages and cultures. We present a Bayesian cognitive model designed to jointly learn categories and their structured representation from natural language text which allows us to (a) evaluate performance on a large scale, and (b) apply our model to a diverse set of languages. We show that meaningful categories comprising hundreds of concepts and richly structured featural representations emerge across languages. Our work illustrates the potential of recent advances in computational modeling and large scale naturalistic datasets for cognitive science research.
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。
