An Integrated Model of Associative and Reinforcement Learning
Abstract
Any successful attempt at explaining and replicating the complexity and generality of human and animal learning will require the integration of a variety of learning mechanisms. Here we introduce a computational model which integrates associative learning and reinforcement learning. We contrast the integrated model with associative learning and reinforcement learning models in two simulation studies. The first simulation demonstrates performance advantages for the integrated model in an environment with a dynamic and diverse reward structure. The second simulation contrasts the performances of the three models in a classic latent learning experiment (Blodgett, 1929), demonstrating advantages for the integrated model in predicting and explaining the behavioral data.
Document Details
- Document Type
- Technical Report
- Publication Date
- Aug 01, 2012
- Accession Number
- ADA577508
Entities
People
- Christopher W. Myers
- Kevin A. Gluck
- Vladislav D. Veksler
Organizations
- Air Force Research Laboratory