Grantee: Carnegie Mellon University, Pittsburgh, PA, USA
Researcher: John R. Anderson, Ph.D.
Grant Title: Theoretical methodology for using fMRI to understand complex cognition
https://doi.org/10.37717/220020162
Program Area: Understanding Human Cognition
Grant Type: Scholar Award
Amount: $600,000
Year Awarded: 2008
Duration: 6 years
We are interested in understanding the cognitive processes that underlie mathematical competences that are learned in school. These represent intellectual activities that have important consequences for the economic and technological well being of our society. However, the complexity of these activities poses a major barrier to their scientific understanding. Functional magnetic resonance imaging (fMRI) has the potential to be an unparalleled source of information about complex cognition. The temporal resolution of fMRI, often a source of frustration in understanding the detail of simple acts of cognition, is well matched to the temporal grain size of complex cognition. The spatial resolution of fMRI is sufficient to allow identification of regions that have functional significance in the interpretation of complex cognition.
The goal of the project is to simultaneously address the two main barriers to realizing the potential of fMRI to inform theories of complex cognition. The first barrier is the lack of a rigorous theoretical framework with which to interpret the significance of the complex activation patterns that fMRI reveals. Cognitive architectures (e.g., Anderson et al., 2004; Just & Varma, 2007; Laird, 2008; Meyer & Kieras, 1997) can provide that interpretative structure. To date these cognitive architectures have largely been used to explain behavioral data. Models have been developed in these architectures that can provide detailed accounts of complex cognitive competences as varied as solving mathematical problems, playing backgammon, and flying fighter jets. In our lab we have been developing bridging assumptions that allow one to map the component activities in these architectures to the activity patterns in different brain regions. This can lead to better understanding of the functional significance of these activity patterns and bring needed empirical constraint to theorizing within these architectures.
The second barrier is more methodological. Complex activities like mathematical problem solving display substantial variability in the timing and choice of steps. Because interpreting fMRI data requires inference from multiple aligned observations, such temporal and strategic variability typically destroys the interpretability of this data source. This project will use rich behavioral measures such as response times, eye movements, and verbal protocols to provide the signposts for aligning fMRI data from different trials. Such behavioral data can reveal the content of specific problem-solving episodes and the accompanying imaging data can reveal the engagement of the underlying cognitive processes. This project will integrate the theoretical structure of cognitive architectures with aligning procedures for fMRI to develop a new theoretical methodology for understanding complex cognition.