|SQAB Tutorial: Characterization of Delay Discounting Using Multiple Models and Effective Delay 50
|Sunday, May 29, 2016
|2:00 PM–2:50 PM
|Area: EAB; Domain: Basic Research
|PSY/BACB CE Offered. CE Instructor: Amy Odum, Ph.D.
|Chair: Amy Odum (Utah State University)
|Presenting Author: CHRISTOPHER FRANCK (Virginia Tech)
The study of delay discounting, or valuation of future rewards as a function of delay, has contributed to understanding the behavioral economics of addiction. Accurate characterization of discounting can be furthered by statistical model selection given that many functions have been proposed to measure future valuation of rewards. This tutorial will present a convenient Bayesian model selection algorithm that selects the most probable discounting model among a set of candidates chosen by the researcher. The approach assigns the most probable model for each individual subject using an asymptotic approximation to model probability based on the Bayesian Information Criterion. Importantly, effective delay 50 (ED50) functions as a suitable unifying measure that is computable for and comparable between several popular functions, including both one- and two-parameter models. Software to execute the combined model selection/ED50 approach is illustrated using empirical discounting data collected from a sample of 111 undergraduate students with five discounting models proposed between 1937 and 2006. The work this tutorial is based upon was published in the January, 2015, special issue of the Journal of the Experimental Analysis of Behavior studying experimental manipulations of delay discountingand related processes.
|Instruction Level: Basic
Licensed psychologists, certified behavior analysts, graduate students.
|Learning Objectives: At the conclusion of the event, the participant will be able to: (1) describe the devaluation of future rewards as a function of delay in terms of delay discounting; (2) recognize several proposed models of delay discounting both mathematically and graphically, and state the computational approach to fit these models to observed data; (3) explain Effective Delay 50 (ED50); (4) execute approximate Bayesian model selection to choose among candidate models given observed data using the Bayesian Information Criterion (BIC). Make informed decisions about the merits and caveats of choosing among candidate models on the basis of observed data.
|CHRISTOPHER FRANCK (Virginia Tech)
|Christopher Franck received his Ph.D. from the Department of Statistics at North Carolina State University in 2010. Dr. Franck is an Assistant Research Professor in the Department of Statistics at Virginia Tech, where he also serves as the assistant director of the Laboratory for Interdisciplinary Statistical Analysis (LISA). Dr. Franck collaborates with researchers from the Addiction Recovery Research Center (ARRC) in the Virginia Tech Carilion Research Institute studying a variety of psychological, behavioral economic, and statistical aspects of those who suffer from addiction and are successful in recovery. Dr. Franck's research interests include non-additivity in unreplicated studies with a focus on the identification of latent-groupings, predictive modeling of health outcomes, spatial modeling, and bioinformatics.
|Keyword(s): Bayesian Model, Delay Discounting, Effective Delay50