  Processes occurring in brains, a.k.a. biological neural networks, can and
have been modeled within artificial neural network architectures. Due to this,
we have conducted a review of research on the phenomenon of blindsight in an
attempt to generate ideas for artificial intelligence models. Blindsight can be
considered as a diminished form of visual experience. If we assume that
artificial networks have no form of visual experience, then deficits caused by
blindsight give us insights into the processes occurring within visual
experience that we can incorporate into artificial neural networks. This
article has been structured into three parts. Section 2 is a review of
blindsight research, looking specifically at the errors occurring during this
condition compared to normal vision. Section 3 identifies overall patterns from
Section 2 to generate insights for computational models of vision. Section 4
demonstrates the utility of examining biological research to inform artificial
intelligence research by examining computation models of visual attention
relevant to one of the insights generated in Section 3. The research covered in
Section 4 shows that incorporating one of our insights into computational
vision does benefit those models. Future research will be required to determine
whether our other insights are as valuable.
