Recent research has shown that semantic category systems, such as color and kinship terms, find an optimal balance between simplicity and informativeness. We argue that this situation arises through pressure for simplicity from learning and pressure for informativeness from communicative interaction, two distinct pressures that often (but not always) pull in opposite directions. Another account argues that learning might also act as a pressure for informativeness, that learners might be biased toward inferring informative systems. This results in two competing hypotheses about the human inductive bias. We formalize these competing hypotheses in a Bayesian iterated learning model in order to simulate what kinds of languages are expected to emerge under each. We then test this model experimentally to investigate whether learners' biases, isolated from any communicative task, are better characterized as favoring simplicity or informativeness. We find strong evidence to support the simplicity account. Furthermore, we show how the application of a simplicity principle in learning can give the impression of a bias for informativeness, even when no such bias is present. Our findings suggest that semantic categories are learned through domain-general principles, negating the need to posit a domain-specific mechanism.

最近的研究表明，语义类别系统（例如颜色和亲属关系术语）在简单性和信息性之间找到了最佳的平衡。我们认为，这种情况是由于学习的简单性压力和交流互动的信息性压力而产生的，这两种不同的压力通常（但不总是）朝相反的方向发展。另一个说法认为，学习也可能成为信息性的压力，学习者可能偏向于推断信息系统。这导致了关于人类归纳偏差的两个相互竞争的假设。我们在贝叶斯迭代学习模型中将这些相互竞争的假设形式化，以模拟每种语言下预期会出现哪种语言。然后，我们通过实验测试该模型，以调查学习者的偏见，与任何沟通任务隔离开来，最好表现为偏向于简单或信息丰富。我们发现有力的证据支持简单性说明。此外，我们展示了简单性原理在学习中的应用如何给人以信息丰富性偏见的印象，即使没有这种偏见也是如此。我们的发现表明，语义类别是通过领域通用原则学习的，而无需提出特定领域的机制。