Mood Algorithm Can Predict Your Preferences Better Than You Can
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A new study led by UCL researchers shows that using mathematical equations with continuous mood sampling may be better at assessing what people prefer over asking them directly.
People can struggle to accurately assess how they feel about something, especially something they feel social pressure to enjoy, like waking up early for a yoga class.
How people really feel about something can be gleaned from their mood and their brain activity in reward regions, according to new research published in the Journal of Neuroscience.
The researchers measured participants’ brain activity with fMRI and their mood with repeated questions about how they felt as the participants earned two types of rewards.
First, participants chose a box to earn points tied to how much money they would earn at the end of the task, which is an example of an extrinsic reward. They then played a game where they navigated a cursor through on-screen barriers. This time they did not earn anything for playing the game beyond their own satisfaction in doing well – an example of an intrinsic reward.
The participants answered the same question about how they felt repeatedly throughout the study. Most people felt happier after earning more points or successfully navigating through the barriers but the contribution of either reward to happiness varied from person to person.
The research team developed a mathematical equation to determine how much intrinsic and extrinsic rewards contributed to the participants’ mood in a particular moment.
They found that people whose happiness was more swayed by intrinsic rewards had more activity in their ventromedial prefrontal cortex, a reward area, during intrinsic rewards compared to extrinsic rewards, and vice versa. These results provide a potential avenue to assess preferences without asking people outright.
Dr Bastien Blain, a Research Associate within the UCL Queen Square Institute of Neurology who co-authored the study, said: "Using mathematical equations, we can measure the value of any repeatable experience based on its impact on happiness. We use our mathematical equation to measure the intrinsic reward of skilfully navigating a simple maze and the extrinsic reward of earning money in a different task. Our mathematical equation lets us predict which individuals will have both more happiness and more brain activity for intrinsic compared to extrinsic rewards. The same approach can be used in principle to measure what people actually prefer without asking them explicitly, but simply by measuring their mood."
Professor Robb Rutledge, Assistant Professor at Yale University who co-authored the study, said: “Over a century ago, Francis Edgeworth described an idealised instrument, which he called a hedonometer, for continually registering the height of pleasure experienced by an individual. Edgeworth's idea is not actually as useful as it could be because you never know what it was that caused changes in happiness. Our 'computational hedonometer' can be used to tell how much people emotionally value experiences.”
Chew B, Blain B, Dolan RJ, Rutledge RB. A neurocomputational model for intrinsic reward. J Neurosci. Published online September 20, 2021. doi:10.1523/JNEUROSCI.0858-20.2021
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