TY - GEN

T1 - Testing a Bayesian measure of representativeness using a large image database

AU - Abbott, Joshua T.

AU - Heller, Katherine A.

AU - Ghahramani, Zoubin

AU - Griffiths, Thomas L.

PY - 2011

Y1 - 2011

N2 - 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.

AB - 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.

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M3 - Conference contribution

AN - SCOPUS:85162530478

SN - 9781618395993

T3 - Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011, NIPS 2011

BT - Advances in Neural Information Processing Systems 24

PB - Neural Information Processing Systems

T2 - 25th Annual Conference on Neural Information Processing Systems 2011, NIPS 2011

Y2 - 12 December 2011 through 14 December 2011

ER -