ArticleNature communications2026
A computational model of reward learning and habits on social media.
Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed.
- A computational model of reward learning and habits on social media.Nature communications · 2026Article
- Cognitive modeling of real-world behavior for understanding mental health.Trends in cognitive sciences · 2026Review
- The cultural ecology of social media.Evolutionary human sciences · 2026Article
- Overestimates of social media addiction are common but costly.Scientific reports · 2025Article
- Signatures of reinforcement learning in natural behavior.Current directions in psychological science · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
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 computational model of real-world social media posting data, adapted from the animal reward learning literature. Using a Twitter (currently X) dataset (n = 2696 users), including a preregistered replication, we show that a hybrid reinforcement learning and habitual cognitive process underlies social media posting behaviour. More frequent posters show more signs of habitual behaviour. Further, younger people and women are more driven by reinforcement learning - updating their strategy more adaptively to maximise social media rewards - while older users and men are more habitual.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.