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Motivation is a central part of our everyday lives. It makes us perform well when the stakes are high. Motivation varies across people, from day to day, and from moment to moment. Furthermore many neurological diseases, such as Parkinson's disease and stroke, cause clinical apathy: a disabling lack of motivation. Motivation is hard to measure, and apathy is hard to treat. In this project, we develop methods of quantifying motivation, and study the anatomy and neurochemistry behind it.

One way to quantify motivation is by measuring “true improvements” in performance. For example, both the speed and accuracy of our decisions or actions are improved when incentives are on offer. If we can sometimes perform very well, then why don’t we put in 100% effort all the time? And why do both cognitive and physical efforts feel hard? We have previously proposed a potential answer to this question: when people are motivated, they invest in making internal representations less noisy, thus improving performance (Manohar et al. 2015). The cost of reducing noise could explain some of these phenomena, and we have shown that this cost is modulated by dopamine. However it remains unclear whether motivation is a unitary phenomenon, or is comprised of multiple components.

This project will study the neural mechanism of how performance improves with incentives, in healthy people and in patients with Parkinson’s disease. To pinpoint neural mechanisms, we will use behaviour and pharmacological manipulations of dopamine or acetylcholine. The studies will examine whether motivation applies independently to attention and action.

First of all, you will design behavioural tasks that will measure the cost of precision in attention and action. This will allow us to examine whether these two domains, which both entail effort, exhibit comparable and correlated precision costs.  

Second, we will test the hypothesis that frontostriatal dopamine or acetylcholine reduces the cost of improving motor representations. We will test the neurochemical basis by testing patients with Parkinson’s disease on and off their medication.

As part of the Master’s / DPhil project, you will gain experience in designing experiments, using an eye tracker, and pupillometry. You will also have the opportunity to work with patients, testing on and off medication, and if interested, you will also have the opportunity to learn how to apply computational modelling and machine learning to data.   

You will work in a team comprising a postdoctoral researcher, a research assistant, other DPhil students and undergraduates. There will be plenty of guidance on day-to-day issues but you are expected to be responsible for your own task design and dataset; you will meet with Prof Manohar weekly, and attend a weekly lab meeting.  Ethics is in place and we have a participant database ready to go.

PRIOR READING BEFORE COMMENCING THE PROJECT:

Manohar et al. Current Biology 2015;https://www.ncbi.nlm.nih.gov/pubmed/26096975

Manohar et al. Psychological Science 2017;http://journals.sagepub.com/doi/full/10.1177/0956797617693326