I was a winner of the 2018 Spence Award for Transformative Early Career Contributions from the Association for Psychological Science. It is a great honor to be recognized for my efforts to develop computational models for happiness and to study happiness in samples of tens of thousands of people using our smartphone app, The Great Brain Experiment. There is an interview with me in this month's APS Observer magazine. I am grateful to all of the support of my mentors and colleagues over the years. I hope that my lab's research will advance our understanding of how happiness works and help explain the symptoms of psychiatric disorders including major depression and bipolar disorder.
PhD students Rachel Bedder, Benjamin Chew, and Akshay Nair all presented their research at the Society of Biological Psychiatry conference in New York. Rachel presented her results showing that happiness is higher when subjects know future blocks contain trials with potential rewards, and the people for whom mood is most affected by future prospects take more risks when they are in a good mood. Benjamin presented his real-time fMRI study showing that subjects take more risks when BOLD activity in the dopaminergic midbrain is low at the time that options are presented, suggesting that endogenous fluctuations in dopamine-related activity are a major factor in determining choice variability, possibly including irrational behaviour. Akshay presented his results showing that people respond more quickly when the amount of reward they lose by not acting is high. People with greater apathy were less sensitive to changes in this opportunity cost and also to changes in the overall reward rate of the environment.
- Rachel Bedder, Bastien Blain, Emily Lowther & Robb Rutledge. A computational model of mood and future prospects.
- Benjamin Chew*, Tobias Hauser*, Marina Papoutsi, Raymond Dolan & Robb Rutledge. Endogenous fluctuations in the dopaminergic midbrain modulate choice behavior.
- Akshay Nair, Geraint Rees, Sarah Tabrizi & Robb Rutledge. How fast should I go? Sensitivity of action initiation to opportunity cost predicts apathy.
We recently published two studies in the journal JAMA Psychiatry showing how computational models can be applied to understanding mood and its relation to behaviour. In the first study, we describe how basic reward processing is surprisingly intact in people with major depression in tasks that do not require significant learning. Our results were shown using brain scanning in the lab and in 1,833 players of our smartphone app, The Great Brain Experiment. These results suggest that the dopamine system that produces reward prediction errors is probably functioning normally in depression and that the reward-related symptoms of depression have a different cause.
In the second study, Liam Mason and Eran Eldar describe how symptoms of bipolar disorder could be explained by the idea that mood represents the momentum of rewards in the environment. When we are in a good mood, we may perceive rewards as better than they actually are. This bias of mood on how we experience our world may be beneficial because it helps us adapt quickly to a changing environment. However, people whose moods bias the perception of rewards too strongly may be more likely to experience greater mood swings, potentially resulting in extreme behaviour. Our simulations suggest that this theoretical framework could explain a range of symptoms observed in bipolar disorder.
The papers can be downloaded here. The research is supported by the Max Planck Society, the Wellcome Trust, and the UK Medical Research Council.
My lab has a new website, designed by my PhD student Rachel Bedder. Other members of my lab include Bastien Blain, Liam Mason, Benjamin Chew, Yunzhe Liu, and Akshay Nair.
Our lab is at University College London and is located on Russell Square in London. Our goal is to understand the factors that determine happiness and to describe how our emotional state influences the decisions we make. We explore these questions using a combination of computational models and brain scanning in healthy and clinical populations. We are working to develop a mechanistic understanding of how major depression develops, is sustained, and can relapse.
I will be opening my lab at University College London funded by a MRC Career Development Award ("The computational psychiatry of major depressive disorder"). My lab will be located on Russell Square at the new Max Planck Centre for Computational Psychiatry and Ageing Research.
Depression is the leading cause of disability worldwide, affecting more than 300 million people. Unfortunately, current antidepressant treatments do not help many of those who suffer from the disorder. It is now widely accepted that depression can result from a variety of different sources, much like a cough can have many different underlying causes. There is currently no reliable way for a psychiatrist to know which treatment is likely to be the most effective for helping a particular depressed individual. If we can identify the causes, we can treat depression more effectively.
Our research lies at the intersection of decision and affective neuroscience and combines computational modeling with neuroimaging, pharmacology, and smartphone-based data collection. By incorporating subjective ratings into a variety of decision tasks, we will examine the neural circuits that determine mood and explain its relation to behaviour in healthy and depressed individuals.