Cognitive Model Optimization with Parallel Genetic Algorithms
Investigating Individual Differences
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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 mod...
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.
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.
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