Can Humans Do Less-Than-One-Shot Learning?

Understand

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.