joshua abbott  

Computational Cognitive Science Lab
Department of Psychology
5429 Tolman Hall
University of California, Berkeley

me  |  research  |  papers

Bayesian Generalization and Representativeness
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.

J.T. Abbott, K.A. Heller, Z. Ghahramani, and T.L. Griffiths. Applying a Bayesian Measure of Representativeness to Sets of Images. 44th Annual Meeting of the Society for Mathematical Psychology. Boston, Massachusetts. July, 2011.

J.T. Abbott, K.A. Heller, Z. Ghahramani, and T.L. Griffiths. Testing a Bayesian measure of representativeness using a large image database. Advances in Neural Information Processing Systems 24, 2011.
[abstract] [paper] [poster]