Emotional Memory For Intelligent Machines

As we try and build more and more intelligent machines it seems that their is many lessons to be learned from the human brain about how to avoid dangerous situations and how to use learning as a basis of computer memory models.

Today, our idea of an intelligent machine that can understand situations from the scenes that it sees is to do something like the following:
a) Create a taxonomy of all the objects expected in an environment
b) Create relationships between the objects and have some idea of purpose and function associated with objects.
c) Look at a scene with sensors of various kinds and correlate 2D representations of 3D models to develop a list of related scene objects from the taxonomy.
d) From the structure, understand the scene.
e) Modify the scene, update the structure and update the understanding.
f) All object matching, relationship establishment, understanding and so on is done from taxonomy information saved in a relational database and recovered by search mechanisms related to relational tuples in the database.

If we have Stanley (Stanford's robot Toureg) driving down a desert road without too much around where it has weighpoints along the way and maps for terrain, this kind of sort of works. If you miss a weighpoint (CMU's red team did this), big trouble may follow because the system isn't really intelligent. Stanley didn't have the same weakness because it was smarter but still had many limitations.

Now consider how the human brain solves the same problem. It is a very different scenario.
a) The brain learns from birth, building up its knowledge of the surroundings.
b) Strong emotional response related to danger, pain, happiness etc keeps training the brain to remember certain scenes and experiences.
c) Discussion of events reinforces these event memories.
d) High level extraction of abstract concepts are related to these strong memories which are tied to the detailed memories, allowing further refinement and evolution of the concepts build upon these memories.
e) The strength of the emotion at the time creates a window by which to filter memory response time.

Would it not be relatively easy to add emotional memory to the computer system to improve response? Then the system could use strong emotional memories to respond quickly to critical events and take more time when events are not so critical.

The cost of this extra processing would be the cost of creating an emotional measure for each scene as it changes over time. This could be done in many ways but must be related to the variable score over a broad set of emotional words for a given language. But why stop at just emotional words? Shouldn't we take a scene and create a scene understanding dialog rating the scene on all means of words related to the scene and use this as a key for identifying all future scenes?

Imagine leaning and feeling happy about what you're learning. The subject matter abstract concepts, emotional feelings and the fact that your basic activity is learning are all keys to finding similar scenes. Emotional response could be a quick first pass but all words with a meansure of strength could be good measures of the correlation between this scene and others with similar characteristics.

Has anyone seen any research in this area?