A computational model of reward learning and habits on social media

Social media have fundamentally transformed how we live and communicate. However, the methods to study how our cognitive systems interact with technology platforms are very limited. Computational modelling represents a new avenue to uncover the finegrained cognitive processes driving social media behaviour. Here, we develop a novel computational model of real-world social media posting data, adapted from the animal reward learning literature. Across a Twitter dataset (n= 2,696 users), including a preregistered confirmatory sample, we separate the contribution of different cognitive processes and show that a hybrid goal-directed and habitual reward-seeking process underlies social media posting behaviour. Further, younger people and women are more goal-directed – updating their strategy more purposefully to maximise social media rewards – while older users and men tend to be more habitual. Our model paves the way for large-scale investigation into the cross-species cognitive processes motivating social media behaviours, and their downstream impacts on individuals and society.