A Behavioral-Economic Approach to Maximizing Feedback

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This project will investigate a behavioral economics approach to the management of motivational variables for persons with intellectual disabilities. In behavioral economics, reinforcers are treated as commodities, and the environmental contingencies that govern access to those commodities as the price. To conduct the analysis, we will first determine individual demand functions for specific commodities. Demand functions relate consumption rate to price; they can be obtained by a parametric assessment of consumption over a range of prices. The demand function provides a more comprehensive picture of performance dynamics than the point estimate produced by traditional/current methods of evaluating reinforcer preference and effectiveness. Demand is termed elastic if consumption falls rapidly as price increases, and inelastic if it does not (i.e., output increases as price rises). We will characterize variables that affect demand functions and the subsequent effects on dependent measures that include both acquisition and performance. These variables include (1) price increases due to amount vs. difficulty of work; and (2) substitutability of commodities. Commodities are substitutes if the concurrent availability of one increases the elasticity of demand for another, and complements if increased consumption of one increases demand for the other. Substitutability analyses will identify classes of reinforcers defined by similar economic functions. (3) We will also examine the effects of open vs. closed economies. This dimension refers to the degree to which commodities are available outside of the economy under study. (4) The project will also include research to examine the economic dynamics of token exchange systems, or "token economies." We will study the effects of schedule manipulations for earning and exchanging tokens on performance accuracy, response rate, and off-task or disruptive behavior, and we will investigate the potential for "virtual tokens" for computer-based instruction. We expect the results of our studies to define a technology for obtaining individualized economic profiles that will be useful in designing more effective motivational systems, and to inform designs of motivational systems for persons with intellectual disabilities by producing algorithms to support a technology for "tuning" such systems to accommodate individual differences. A series of applied intervention studies will demonstrate the application of the research to clinical and educational problems in the special-education classroom.