Dual neural networks experiment (one logical, one emotional)?

Question

Seeing that as as far as we know, one half of your brain is logical and the other half of your brain is emotional, and that the wants of the emotional side are fed to the logical side in order to fulfill those wants; has there been any research done in connecting two separate neural networks to one another (one trained to be emotional, and one trained to be logical) to see if it would result in almost a free-will sort of "brain"?

I don't really know anything about neural networks except that they were modeled after the biological synapses in the human brain, which is why I ask.

I'm not even sure if this would be possible considering that even a trained neural network sometimes doesn't act logically (a.k.a. do what you thought you trained it to do).

Answer 1

First, most modern neural networks aren't really modeled after biological synapses. They use an Artificial Neuron which allowed Back Propagation to work rather than a Perceptron which is a much more accurate representation.

When you feed the output of one network into the input of another network, you've really just created one larger network, not two separate networks. It just happens that in this case portions of the networks would be trained independently.

That said, all neural networks have to be trained. Which means you need sample input and sample output. You are looking to create a decision engine of sorts I suppose. So you would need to create a dataset where it makes sense that there might be an emotional and rational response, such as purchasing an item. You'd have to train the 'rational' network to accept as a set of inputs the output of an 'emotional' network. Which means you are really just training the rational decision engine to be responsive based on the leve of 'distress' caused by the emotional network.

Just my two cents.

Answer 2

I have also heard of one hemisphere being called "divergent" and one "convergent". This may not make any more sense than emotional vs logical, but it does hint at how you might model it more easily. I don't know how the brain achieves some of the impressive computational feats it does, but I wouldn't be very surprised if all revolved around balance, but maybe that is just one of the baises you have when you are a brain with two hemipheres (or any even number) :D

A balance between convergence and divergence is the crux of the creativity inherent in evolution. Replicating this with neural nets sounds promising to me. Suppose you make one learning system that generalizes and keeps representations of only the typical groups of patterns it is shown. Then you take another and make it generate all the in-betweens and mutants of the patterns it is shown. Then you feed them to eachother in a circle, and poof, you have made something really interesting!