TY - GEN
T1 - Look-Ahead Monte Carlo with People
AU - Blundell, Charles
AU - Sanborn, Adam
AU - Griffiths, Thomas L.
N1 - Publisher Copyright:
© CogSci 2012.All rights reserved.
PY - 2012
Y1 - 2012
N2 - Investigating people’s representations of categories of complicated objects is a difficult challenge, not least because of the large number of ways in which such objects can vary. To make progress we need to take advantage of the structure of object categories – one compelling regularity is that object categories can be described by a small number of dimensions. We present Look-Ahead Monte Carlo with People, a method for exploring people’s representations of a category where there are many irrelevant dimensions. This method combines ideas from Markov chain Monte Carlo with People, an experimental paradigm derived from an algorithm for sampling complicated distributions, with hybrid Monte Carlo, a technique that uses directional information to construct efficient statistical sampling algorithms. We show that even in a simple example, our approach takes advantage of the structure of object categories to make experiments shorter and increase our ability to accurately estimate category representations.
AB - Investigating people’s representations of categories of complicated objects is a difficult challenge, not least because of the large number of ways in which such objects can vary. To make progress we need to take advantage of the structure of object categories – one compelling regularity is that object categories can be described by a small number of dimensions. We present Look-Ahead Monte Carlo with People, a method for exploring people’s representations of a category where there are many irrelevant dimensions. This method combines ideas from Markov chain Monte Carlo with People, an experimental paradigm derived from an algorithm for sampling complicated distributions, with hybrid Monte Carlo, a technique that uses directional information to construct efficient statistical sampling algorithms. We show that even in a simple example, our approach takes advantage of the structure of object categories to make experiments shorter and increase our ability to accurately estimate category representations.
KW - Markov chain Monte Carlo
KW - category representation
KW - directional judgements
UR - http://www.scopus.com/inward/record.url?scp=84941270230&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84941270230&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84941270230
T3 - Building Bridges Across Cognitive Sciences Around the World - Proceedings of the 34th Annual Meeting of the Cognitive Science Society, CogSci 2012
SP - 1356
EP - 1361
BT - Building Bridges Across Cognitive Sciences Around the World - Proceedings of the 34th Annual Meeting of the Cognitive Science Society, CogSci 2012
A2 - Miyake, Naomi
A2 - Peebles, David
A2 - Cooper, Richard P.
PB - The Cognitive Science Society
T2 - 34th Annual Meeting of the Cognitive Science Society: Building Bridges Across Cognitive Sciences Around the World, CogSci 2012
Y2 - 1 August 2012 through 4 August 2012
ER -