How do neuromodulators encode uncertainty to aid flexible behaviour?

How do we decide things quickly in an everchanging world with a lot of uncertainty? Healthy humans are exceptionally good at making optimal choices in everyday life; perhaps deciding that the bus is the fastest and best way to get to work. Despite small uncertainties in the exact journey time of the bus, we should stick to our choice if it's the fastest. However, if there is a sudden roadwork on the bus route, we may have to adapt flexibly and instead walk to the train station further away to get the quickest journey. We can imagine that if we cannot adapt well to uncertainty, we end up with bad choices. In one scenario, we may change transportation every day just because the bus was late once. Alternatively, when the road work happen, we may fail to adapt and still favor staying on the slow bus. Both of these scenarios result in a worse choice in the form of longer travel time.

It turns out that computations needed for making good choices under uncertainty are not simple - neither computers or robots, nor people with psychiatric disorders are particularly good at flexible decision making. Currently, machine learning algorithms and robots can be amazingly good at performing single, defined tasks; but most fail at adapting to new environments or performing many different tasks. Hence, it would be useful to understand what processes in the brain help us make flexible choices, possibly benefiting both the advancing field of artificial intelligence and robotics, as well as mental health care.

So far, scientist believe that making adaptive choices, our brain forms 'best guesses' (predictions) about the world and what actions might lead to something rewarding in the future helps us. However, we don't know in detail what biological signals in what area may carry information on these 'predictions' about our uncertain world. In this project, I will look at two specific neurochemicals (called noradrenaline and acetylcholine), to ask if these may be acting as potential biological 'uncertainty signals'. These neurochemicals will have a fluorescent tag, allowing me to monitor them with a photometry microscope and optical fibers inserted into the brains of living mice; all whilst animals are solving an uncertain decision-making game.
I will also manipulate these neurochemicals with drugs in specific brain areas know to be involved in learning and decision making (called Hippocampus and Prefrontal Cortex). Do we need the neurotransmitters in these areas to make good guesses about our environment and predict good choices?

Answering these questions could better guide what drug targets are relevant for future psychiatric drugs, such as improved antidepressants. After having investigated the relevant neurochemicals and brain flexible decision making, I aim to build computational models testing different potential mechanism that the brain might be using, and see which model best fits the animal's choices. Such successful models could inform future flexible, multi-task machine learning algorithms, relevant to a broad range of fields; from tech and engineering industry, to finance, to healthcare.