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Leveling up fun: Learning progress, expectations, and success influence enjoyment in video games.
Sci. Rep. 15:34153 (2025)
What factors influence how much fun people have when engaging in inherently enjoyable tasks? Several theories predict that people will have the most fun in environments of intermediate difficulty because these environments usually offer the most progress in learning about the world. Past studies have frequently focused on simple experimental paradigms in which learning was still instrumental for later tasks. Here, we put these theories to a test in three large and realistic video game data sets: a puzzle game (with 7,994 levels and 376,341 votes), a racing game (138,662 levels and 614,770 votes), and a platformer game (115,032 levels and 795,313 votes). As predicted, people preferred levels of intermediate difficulty in all games. Yet, additional factors influencing people's enjoyment also emerged: players preferred levels that matched closely with their prior expectations of difficulty and were also motivated by success. We further confirmed these factors in two precisely controlled experiments. Taken together, these results advance our understanding of the dynamics of fun in realistic environments and emphasize the importance of using both realistic, game-like environments and highly controlled experiments to refine theories of human learning and decision-making.
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Publication type
Article: Journal article
Document type
Scientific Article
Keywords
Curiosity ; Engagement ; Enjoyment ; Fun ; Learning Progress; Intrinsic Motivation; Curiosity; Information; Psychology; Liking
ISSN (print) / ISBN
2045-2322
e-ISSN
2045-2322
Journal
Scientific Reports
Quellenangaben
Volume: 15,
Issue: 1,
Article Number: 34153
Publisher
Nature Publishing Group
Publishing Place
London
Reviewing status
Peer reviewed
Institute(s)
Human-Centered AI (HCA)
Grants
Jacobs Research Fellowship
Volkswagen Foundation
Max Planck Society
Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)
German Federal Ministry of Education and Research (BMBF): Tuebingen AI Center
Volkswagen Foundation
Max Planck Society
Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)
German Federal Ministry of Education and Research (BMBF): Tuebingen AI Center