| Neuroscientists and educationists share an interest in learning, suggesting that neuroscience can inform education. Despite educators' eagerness to apply neuroscience to improve their practice, however, few clear examples of such applications exist. Many researchers point to the gap separating microscopic neural processes from macroscopic classroom behaviors as a major obstacle to establishing the neuroscience-education bridge. In this thesis, I describe methods for bridging this gap using computational models to link neural mechanisms to behavioral patterns, and then using these causal neural-behavioral models to inform education. This application of computational models in principle bridges the gap, but in turn raises new issues concerning the validity and interpretability of model properties in relation to human cognition and behavior. The main contribution of this thesis is a set of analytical and experimental tools for addressing the theoretical and practical problems arising in this context.;As I develop the general neuroscience-education tools, I simultaneously demonstrate their application in a specific case. First, I derive a novel analytical framework for comparing disparate psychological theories. I use this framework to justify my selection of an artificial neural network (ANN) over other candidate models, to explain how the ANN relates to human brain and behavior, and to identify a specific neural mechanism that could inform education. Next, I describe an experimental paradigm in which predictions of the neural mechanism are tested against human learning data using multi-level regression models in a novel way to relate ANN behavior to human behavior. Finally, I discuss implications of the research for the educationally relevant phenomenon of knowledge transfer. The experimental findings are consistent with the model predictions, and on the whole the case study demonstrates the feasibility of using the proposed methods to bridge the neuroscience-education gap in the near term. |
