RAPID: Analyzing forced habit change from COVID-19 using large-scale data

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During the COVID-19 pandemic, people were forced to try new routine at school, work, and home — “forced exploration”. That is, habits used to guide how we work, conduct our daily routine, exercise, eat, go to school, and interact with friends and neighbors, Habits are formed to make the same routines effortless, to save time and mental effort, when routine choices work well. However, while the mental savings from habits are a benefit, when people are choosing habitually, they may not be exploring other options which could be even better— that is the hidden cost of habit. Forced exploration can actually be beneficial if it shows people better ways of school, work, and social interaction. This kind of exploration is like going to your favorite restaurant and finding out they have run out of your favorite dish — now you have to try something new, which you would not have explored without the disruption. This wave of forced exploration raises important questions: What new habits are formed that will persist— what will be the “new normal”? Consider, for example, wearing a face-mask outside of the house. This is exactly the kind of “muscle memory” behavior that usually habitizes— it can be triggered while stepping out of your car, or entering a store, and quickly becomes automatic and effortless. Whether a lot of other people are wearing masks or not can also be a trigger that prompts habit (in either direction).The same question arises across the board: Will people go back to movie theaters (or stay home with streaming)? Will restaurants fully reopen or will home delivery take over? Will knowledge firms switch to more remote “tele-work”? Will schools find better mixtures of home learning and in-school activity? This project will analyze two different kinds of big data to test whether or not this kind of forced exploration really does result in new habits.In social sciences, habits are usually modelled mathematically using a simple equation in which the more an activity has been done in the past, the more it is done in the future. This is called a "reduced form" approach because it reduces a biologically complicated mechanism to something much simpler. It is a good starting point but cannot answer questions such as "What if past behavior is disrupted?” This research project uses a new approach to habits based on animal learning and human cognitive neuroscience. The starting point is that habits have developed to save effort ⎯— both physical and mental. The “neural autopilot” framework proposed here predicts that individuals develop habits for actions which, after repeated decisions, have proven to be reliably rewarding. Such habitual behavior drains fewer physical and mental resources. At the same time, when people are habitized⎯- about exercise, eating, or work — they ignore new goods and activities they would prefer if they actually tried them. While the neural autopilot approach has been tested in many lab studies of animal and human habituation, it has never been systematically explored using a large amount of data about how people actually behave in everyday life. An ideal test of this model is in a field setting where choice sets are artificially truncated, so people resort to new choices; and that is exactly what happened during the ongoing lockdowns. This project will use data from Weibo chat data and Fitbit fitness and sleep tracking. These large sets of data contain fine-grained measurements of behavior. Using this data, we will develop and test a statistical neural autopilot model, to recover values for the model’s main parameters. The parameters are numbers that measure, for each person, how fast habits are formed and the threshold to break out of a habit and explore something that might be better. The estimated parameter values will be used to make predictions about which habits acquired during the pandemic will persist, and which behavior will revert to pre-pandemic patterns.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.