Being able to learn from small amounts of data is a key characteristic of human intelligence, but exactly {\em how} small? In this paper, we introduce a novel experimental paradigm that allows us to examine classification in an extremely data-scarce setting, asking whether humans can learn more categories than they have exemplars (i.e., can humans do "less-than-one shot" learning?). An experiment conducted using this paradigm reveals that people are capable of learning in such settings, and provides several insights into underlying mechanisms. First, people can accurately infer and represent high-dimensional feature spaces from very little data. Second, having inferred the relevant spaces, people use a form of prototype-based categorization (as opposed to exemplar-based) to make categorical inferences. Finally, systematic, machine-learnable patterns in responses indicate that people may have efficient inductive biases for dealing with this class of data-scarce problems.

翻译：能够从少量数据中学习是人类智慧的关键特征,但确切地说,数据是很小的。 在本文中,我们引入了一个新的实验范式,允许我们在极端的数据碎裂环境中审查分类,询问人类是否可以学习比其外观学更多的类别(即,人类能够做“无镜头”的学习吗? ) 。使用这一范式进行的实验表明,人们有能力在这种环境中学习,并提供了对基本机制的一些洞察力。首先,人们可以精确地从极小的数据中推断和代表高维特征空间。第二,在推断相关空间之后,人们使用一种原型分类(而不是基于外观的分类)来做出绝对的推断。最后,系统性的、机器可忽略的模式在反应中表明,人们在处理这类数据碎问题时可能具有有效的导力偏差。