TY - GEN
T1 - Hierarchical Bayesian Modeling
T2 - 35th Annual Meeting of the Cognitive Science Society - Cooperative Minds: Social Interaction and Group Dynamics, CogSci 2013
AU - Scheibehenne, Benjamin
AU - Pachur, Thorsten
N1 - Publisher Copyright:
© CogSci 2013.All rights reserved.
PY - 2013
Y1 - 2013
N2 - Fitting multi-parameter models to the behavior of individual participants is a popular approach in cognitive science to measuring individual differences. This approach assumes that the model parameters capture psychologically meaningful and stable characteristics of a person. If so, the estimated parameters should show, to some extent, stability across time. Recently, it has been proposed that hierarchical procedures might provide more reliable parameter estimates than non-hierarchical procedures. Here, we examine the benefits of hierarchical parameter estimation for assessing parameter stability using Bayesian techniques. Using the transfer-of-attention-exchange model (TAX; Birnbaum & Chavez, 1997), a highly successful account of risky decision making, we compare parameter stability based on hierarchically versus non-hierarchically estimated parameters. Surprisingly, we find that parameter stability for TAX is not improved by using a hierarchical Bayesian as compared to a non-hierarchical Bayesian approach. Further analyses suggest that this is because the shrinkage induced by hierarchical estimation overcorrects for extreme yet reliable parameter values. We suggest that the benefits of hierarchical techniques may be limited to particular conditions, such as sparse data on the individual level or very homogenous samples.
AB - Fitting multi-parameter models to the behavior of individual participants is a popular approach in cognitive science to measuring individual differences. This approach assumes that the model parameters capture psychologically meaningful and stable characteristics of a person. If so, the estimated parameters should show, to some extent, stability across time. Recently, it has been proposed that hierarchical procedures might provide more reliable parameter estimates than non-hierarchical procedures. Here, we examine the benefits of hierarchical parameter estimation for assessing parameter stability using Bayesian techniques. Using the transfer-of-attention-exchange model (TAX; Birnbaum & Chavez, 1997), a highly successful account of risky decision making, we compare parameter stability based on hierarchically versus non-hierarchically estimated parameters. Surprisingly, we find that parameter stability for TAX is not improved by using a hierarchical Bayesian as compared to a non-hierarchical Bayesian approach. Further analyses suggest that this is because the shrinkage induced by hierarchical estimation overcorrects for extreme yet reliable parameter values. We suggest that the benefits of hierarchical techniques may be limited to particular conditions, such as sparse data on the individual level or very homogenous samples.
KW - cognitive modeling
KW - hierarchical Bayesian modeling
KW - parameter consistency
KW - risky choice
KW - transfer-of-attention-exchange model
UR - http://www.scopus.com/inward/record.url?scp=85041417381&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85041417381
T3 - Cooperative Minds: Social Interaction and Group Dynamics - Proceedings of the 35th Annual Meeting of the Cognitive Science Society, CogSci 2013
SP - 1277
EP - 1282
BT - Cooperative Minds
A2 - Knauff, Markus
A2 - Sebanz, Natalie
A2 - Pauen, Michael
A2 - Wachsmuth, Ipke
PB - The Cognitive Science Society
Y2 - 31 July 2013 through 3 August 2013
ER -