Computer Model Explains How Animals Select Actions with Rewarding outcomes

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

Dr. Mark Humphries from The University of Manchester explains the research: “We wanted to look at how we learn from feedback — particularly how we learn 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.” Learning to associate rewarding outcomes with specific actions is a key part of survival, for example searching for food or avoiding predators. 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.

Collectively, this evidence suggests that dopamine signals change the strength of connections between cortical and striatal neurons, thereby determining which action is appropriate for a specific set of circumstances. But until now, no model had integrated these strands of evidence to test this.

Read the full article here.

© The UCLA Institute for Society and Genetics. All Rights Reserved.