By combining planning and learning into one coherent model, we’ve made what is probably the most comprehensive model of complex decision-making to date
- Johannes Friedrich
Researchers have constructed the first comprehensive model of how neurons in the brain behave when faced with a complex decision-making process, and how they adapt and learn from mistakes.
The mathematical model, developed by researchers from the University of Cambridge, is the first biologically realistic account of the process, and is able to predict not only behaviour, but also neural activity. The results, reported in The Journal of Neuroscience, could aid in the understanding of conditions from obsessive compulsive disorder and addiction to Parkinson’s disease.
The model was compared to experimental data for a wide-ranging set of tasks, from simple binary choices to multistep sequential decision making. It accurately captures behavioural choice probabilities and predicts choice reversal in an experiment, a hallmark of complex decision making.
Our decisions may provide immediate gratification, but they can also have far-reaching consequences, which in turn depend on several other actions we have already made or will make in the future. The trouble that most of us have is how to take the potential long-term effects of a particular decision into account, so that we make the best choice.
There are two main types of decisions: habit-based and goal-based. An example of a habit-based decision would be a daily commute, which is generally the same every day. Just as certain websites are cached on a computer so that they load faster the next time they are visited, habits are formed by ‘caching’ certain behaviours so that they become virtually automatic.
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Image: EyeWire Candy Neurons
Credit: Seung Lab
Reproduced courtesy of the University of Cambridge
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