Title: A Brain-inspired Cognitive System that duplicates cognitive
interference within human-like timescales

Abstract: In recent years, some impressive AI systems have been built
that can play games and answer questions about large quantities of
data. However, we are still a very long way from AI systems that can
think and learn in a human-like way. We have a great deal of
information about how the brain works and can simulate networks of
hundreds of millions of neurons. It seems likely that we could start
building brain-inspired neuronal networks using neuroscientific
knowledge to duplicate actions like humans on similar timescales. This
paper describes a simulated neuron-network that can recreate the
Stroop Effect, which is a prominent cognition interference
phenomenon. The system can produce the correct reactions in a similar
timescale on the word recognition and colour naming task. In the
longer term, this type of AI technology could lead to more flexible
general-purpose artificial intelligence and to more natural
human-computer interaction.