Cognitive Model Optimization with Parallel Genetic Algorithms: Investigating Individual Differences

To enable an individual differences investigation of how stress affects cognitive performance in a mental serial subtraction task a new optimization approach was developed for computing the fit of a serial subtraction cognitive model to human performance data by varying architectural parameters. The new optimization approach utilizes a modified parallel genetic algorithm (PGA) on a high performance computing (HPC) cluster together with the ACT-R cognitive architecture and a serial subtraction cognitive model. The optimization approach was highly successful in fitting the serial subtraction model superseding the field¿s traditional manual optimization process. The optimization results revealed several interesting patterns in the parametric values found to produce best fits to the human data; some of which are supported by individual differences theories of stress and anxiety from cognitive performance research. The book describes a prototype optimization system for individual differences modeling and validation that is useful to cognitive science and performance researchers.