The purpose of this proposal is to extend our past work comparing performance of brain damaged, drug abusing, or psychopathological individuals and with non abusing or normal individuals on standard laboratory decision making tasks. Performance on these tasks is an interaction and synthesis of three different underlying components, including motivational, learning, and choice processes. Cognitive models of these complex decision tasks are used to break performance down into these three components. The parameters associated with these components are then used to understand the source of the decision making deficits exhibited by these clinical populations. Two critical assumptions underlying this past work are the assumptions of model generalization and parameter consistency. A model generalizes if one can fit the parameters of the model to one task for an individual, and then use these same parameters to predict performance on other closely related tasks for the same individual. Parameters are consistent if the parameters estimated from one task for an individual correlate with the parameters estimated from another closely related task for the same individual. These assumptions are crucial if we want to interpret the parameters as measuring stable characteristics of an individual, rather than some inessential characteristics of a laboratory task. So far, we achieved some initial success obtaining model generalization and parameter consistency. But success has been limited for at least two reasons: one is the need to find better models through model comparison, and the other is the need for better methods of estimating model parameters. We plan to improve our methods using new hierarchical Bayesian analyses. This new methodology allows one to build a model for individual differences rather than fitting individuals separately. This way the parameters for a single individual are estimated through a model which is informed by data from all individuals. This provides more stable parameter estimates and more powerful methods for model comparison. We also plan to extend the hierarchical Bayesian method for comparing model generalization.

Public Health Relevance

hisrsarchinvstiats th undrly in causesfor decision main dficits in brai daaed,drug abuse, and psychopathological populations his information will eventually b usdtod sign and prdicttratetutcmes o theseciica sychoopgroyblms.

Agency
National Institute of Health (NIH)
Institute
National Institute on Drug Abuse (NIDA)
Type
Research Project (R01)
Project #
5R01DA030551-02
Application #
8249805
Study Section
Cognition and Perception Study Section (CP)
Program Officer
Bjork, James M
Project Start
2011-04-01
Project End
2014-03-31
Budget Start
2012-04-01
Budget End
2013-03-31
Support Year
2
Fiscal Year
2012
Total Cost
$154,000
Indirect Cost
$54,000
Name
Indiana University Bloomington
Department
Psychology
Type
Schools of Arts and Sciences
DUNS #
006046700
City
Bloomington
State
IN
Country
United States
Zip Code
47401
Dai, Junyi; Busemeyer, Jerome R (2014) A probabilistic, dynamic, and attribute-wise model of intertemporal choice. J Exp Psychol Gen 143:1489-514