The Eye's mind - Visual imagination, neuroscience and the humanities

Convolutional neural networks and genetic algorithm for visual imagery classification

Brain-Computer Interface (BCI) systems establish a channel for direct communication between the brain and the outside world without having to use the peripheral nervous system. While most BCI systems use evoked potentials and motor imagery, in the present work we present a technique that employs visual imagery. Our technique uses neural networks to classify the signals produced in visual imagery. To this end, we have used densely connected neural and convolutional networks, together with a genetic algorithm to find the best parameters for these networks. The results we obtained are a 60% success rate in the classification of four imagined objects (a tree, a dog, an airplane and a house) plus a state of relaxation, thus outperforming the state of the art in visual imagery classification.

Education and visual neuroscience: A mini-review

Neuroscience, especially visual neuroscience, is a burgeoning field that has greatly shaped the format and efficacy of education. Moreover, findings from visual neuroscience are an ongoing source of great progress in pedagogy. In this mini-review, I review existing evidence and areas of active research to describe the fundamental questions and general applications for visual neuroscience as it applies to education. First, I categorize the research questions and future directions for the role of visual neuroscience in education. Second, I juxtapose opposing views on the roles of neuroscience in education and reveal the "neuromyths" propagated under the guise of educational neuroscience. Third, I summarize the policies and practices applied in different countries and for different age ranges. Fourth, I address and discuss the merits of visual neuroscience in art education and of visual perception theories (e.g., those concerned with perceptual organization with respect to space and time) in reading education. I consider how vision-deprived students could benefit from current knowledge of brain plasticity and visual rehabilitation methods involving compensation from other sensory systems. I also consider the potential educational value of instructional methods based on statistical learning in the visual domain. Finally, I outline the accepted translational framework for applying findings from educational neuroscience to pedagogical theory.