A computational model of multi-sensory perception and its application to investigating the controversy around learning styles

The computational modeling of multi-sensory perception is challenging. It is important to understand and model temporal phenomena such as synchronization and synaptic integration over varying time intervals. Previously, we developed a sparse spatio-temporal model using networks of interconnected oscillators. This model achieved a binding of related object features in multiple sensory pathways.We apply this model to address a controversy in the field of psychology and education concerning the theory of learning styles. According to a popularly held view, learners fall into categories such as visual learners or auditory learners, and prefer to imbibe materials that match their learning styles. This view has been disputed as this categorization does not result in a demonstrable improvement in learning.We model preferences for a learning style through variations in synaptic learning efficacies. Hence a visual learner will possess higher synaptic efficacy in the visual pathway. The results of our simulation with the sparse spatio-temporal model show that there is little benefit to having or exploiting such differences in synaptic efficacy. The best system performance is achieved when all available sensory pathways are utilized rather than favoring a preferred pathway.Our model weighs in on this controversy, and supports the side that is against the categorization of students based on their learning styles. Since the idea of learning styles is quite prevalent in the literature and used in schools, it is important to disseminate appropriate research findings including those that utilize a computational neuroscience perspective, as in the current paper.