Maximizing the Benefits of Training by Example and Direct Instruction

摘要

One major accomplishment in this project was the development of the CLUSTer Error Reduction (CLUSTER) model's formalism. One popular approach to modeling human category learning in the face of challenging data has been to propose models containing multiple systems. These systems could include prototype, exemplar, or rule-based components, as well as gating mechanisms that determine how to combine the outputs from these systems or components. CLUSTER takes a complex systems approach in which 'systems' emerge out of the learners interactions with their environment. One claim is that what appears as separate cognitive systems are all based on cluster representations that follow from CLUSTER's recruitment and learning rules.