Cabe
Hi Cabe
Very interesting information and a great excuse for playing games!
Whilst studying for my Masters in Neuroscience, I discovered that although the brain seems to work in a probabilistic way, it is more accurate to describe decision making in the brain in terms of a sum of weighted inputs. Each neuron takes a very small part of the decision based on its inputs - some conributing negatively and some positively, with each neuron being a part of a neural network which will produce a generalistic answer, e.g., there is a 30% chance that child 200 yards ahead will step into the road. Creating a model or function for this behaviour is so complex that artificial neural networks are often termed as 'black box methods' as you may get an answer, but you're never sure how it was really reached. I believe Professor Bavelier may have simplified this process somewhat! 
I hope this might interest someone
Philip
Philip,
Do you think artificial intelligence, on a child level at least, is anywhere near being possible? Thoughts on the subject?
Cabe
Cabe,
It all depends on what you mean by "on a child level" and on the application. If you want to run a neural network to sniff out illegal drugs from coffee or detect the presence of an object in a field of view or detecting audio patterns / frequency components from a sound source, these can all be performed. However, putting all this together by modelling auditory, visual, muscle motor movement, touch etc. with a neural network is hard enough, but trying to model how a child thinks - well, I have two of these real children and how you could produce AI to compete with them is a complex task. In the end, I guess it would eventually become possible, but philosophically, would you have a child?
Philip