# A computational reward learning account of social media engagement - Author(s): B. Lindström, Martin Bellander, D. Schultner, Allen Chang, P. Tobler, D. Amodio - Date: 2021 - Publication: Nature Communications - [Link](https://doi.org/10.1038/s41467-020-19607-x) --- ## Summary - Computational models based on reinforcement learning theory show that human behavior on social media conforms to the principles of reward learning. - I.e., people update their behavior to maximize rewards — in this case the social rewards provided by likes, etc. on social media platforms - Social media users spaced their posts to maximize the average rate of accrued social rewards, in a manner subject to both the effort cost of posting and the opportunity cost of inaction. - An online experiment verifies that social rewards causally influence behavior as posited by the computational account. --- #### Related #social_media_dynamics #misinformation #misinfo_interventions #reward_learning #social_rewards - [[Paper_Globig_2022_ChangeIncentiveStructure]] - [[Paper_Sharot_2022_CarrotsNotSticks]] - [[Paper_Zeng_2022_SocialNudgesContentCreation]]