Cognitive Model Optimization with Parallel Genetic Algorithms

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.

Autorentext

PhD, College of Information Sciences and Technology at the Pennsylvania State University. Postdoctoral Fellow at the Defense Threat Reduction Agency, Directorate of Basic and Applied Science, Fort Belvoir, Virginia. Senior Modeling and Simulation Engineer at The MITRE Corporation, McLean, Virginia.

Klappentext

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.