Modeling the Motivation-Learning Interface in Learning and Decision Making
Abstract
The theoretical framework that motivates our research program predicts a three-way interaction between global incentives, local rewards, and task structure, such that the optimal combination of incentives and rewards depends on the factors that govern optimal task performance. When optimal task performance requires cognitive flexibility, a "match" between the global incentive structure and the local rewards is advantageous. When optimal task performance requires a less flexible, incremental approach, a "mismatch" between the global incentive structure and the local rewards is advantageous. We found support for this three way interaction in a series of risky decision making/choice tasks as well as in the realm of stereotype threat. Specifically, we found that when exploration of the choice space was optimal, participants in a regulatory match (whether induced through regulatory fit or stereotype threat) performed better, whereas when exploitation of the choice space was optimal, participants in a regulatory mismatch performed better. Similarly, in a rule-based category learning task (that required flexible processing of the rule space) we found better performance for "match" participants, but in an information-integration category learning problem (that required incremental learning) we found better performance for "mismatch" participants. Finally, we developed a computational model of the motivation learning interface and applied it to data collected during the grant period.
Document Details
- Document Type
- Technical Report
- Publication Date
- Nov 01, 2009
- Accession Number
- ADA531596
Entities
People
- Arthur B. Markman
- W. T. Maddox
Organizations
- University of Texas at Austin