# Testing a Bayesian measure of representativeness using a large image database

Published

Journal Article

How do people determine which elements of a set are most representative of that set? We extend an existing Bayesian measure of representativeness, which indicates the representativeness of a sample from a distribution, to define a measure of the representativeness of an item to a set. We show that this measure is formally related to a machine learning method known as Bayesian Sets. Building on this connection, we derive an analytic expression for the representativeness of objects described by a sparse vector of binary features. We then apply this measure to a large database of images, using it to determine which images are the most representative members of different sets. Comparing the resulting predictions to human judgments of representativeness provides a test of this measure with naturalistic stimuli, and illustrates how databases that are more commonly used in computer vision and machine learning can be used to evaluate psychological theories.

### Duke Authors

### Cited Authors

- Abbott, JT; Heller, KA; Ghahramani, Z; Griffiths, TL

### Published Date

- December 1, 2011

### Published In

- Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011, Nips 2011

### Citation Source

- Scopus