Computer model explains how people act for rewards

Scientists from the University of Sheffield and the University of Manchester have developed a computer model that can explain what happens in the brain when an action is chosen that leads to a reward.

Learning to associate rewarding outcomes with specific actions is a key part of survival like searching for food or avoiding predators.

Researchers wanted to look at how people learned from feedback - particularly how people learned to associate actions to new unexpected outcomes.

"To do this, we created a series of computational models to show how the firing of dopamine neurons caused by receiving reward ultimately translates into selecting the causative action more frequently in the future," explained Mark Humphries from the University of Manchester.

It is already known that actions are represented in the cortex -- the brain's outer layer of neural tissue -- and rewarding outcomes activate neurons that release a brain chemical called dopamine.

These neuronal signals are sent to another area of the brain, the striatum which plays an important role in selecting which action to take.

Their model revealed how several brain signals work together to shape the inputs so the appropriate action is chosen.

"The model reveals that the relative strength of cortical inputs, which represent different possible actions, to the two populations of dopamine responsive cells, determines whether an action is selected or suppressed," added professor Kevin Gurney from the University of Sheffield.

The model could provide new insights into the mechanisms behind motor disorders such as Parkinson's disease.

It may also shed light on conditions involving abnormal learning such as addiction, the authors concluded.