Deceptive Game Design? Investigating the Impact of Visual Card Style on Player Perception

  • Leonie Kallabis*
  • , Timo Bertram*
  • , Florian Rupp*
  • *Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceedingspeer-review

Abstract

The visual style of game elements considerably contributes to the overall experience. Aesthetics influence player appeal, while the abilities of game pieces define their in-game functionality. In this paper, we investigate how the visual style of collectible cards influences the players' perception of the card's actual strength in the game. Using the popular trading card game Magic: The Gathering, we conduct a single-blind survey study that examines how players perceive the strength of AI-generated cards that are shown in two contrasting visual styles: cute and harmless, or heroic and mighty. Our analysis reveals that some participants are influenced by a card's visual appearance when judging its in-game strength. Overall, differences in style perception are normally distributed around a neutral center, but individual participants vary in both directions: some generally perceive the cute style to be stronger, whereas others believe that the heroic style is better.
Original languageEnglish
Title of host publicationProceedings 2025 IEEE Conference on Games (CoG)
PublisherIEEE Xplore
Number of pages8
ISBN (Electronic)979-8-3315-8904-2
ISBN (Print)979-8-3315-8905-9
DOIs
Publication statusPublished - 19 Aug 2025

Publication series

NameIEEE Conference on Computational Intelligence and Games
ISSN (Print)2325-4270
ISSN (Electronic)2325-4289

Fields of science

  • 101019 Stochastics
  • 102003 Image processing
  • 103029 Statistical physics
  • 101018 Statistics
  • 101017 Game theory
  • 102001 Artificial intelligence
  • 202017 Embedded systems
  • 101016 Optimisation
  • 101015 Operations research
  • 101014 Numerical mathematics
  • 101029 Mathematical statistics
  • 101028 Mathematical modelling
  • 101026 Time series analysis
  • 101024 Probability theory
  • 102032 Computational intelligence
  • 102004 Bioinformatics
  • 102013 Human-computer interaction
  • 101027 Dynamical systems
  • 305907 Medical statistics
  • 101004 Biomathematics
  • 305905 Medical informatics
  • 101031 Approximation theory
  • 102033 Data mining
  • 102 Computer Sciences
  • 305901 Computer-aided diagnosis and therapy
  • 102019 Machine learning
  • 106007 Biostatistics
  • 102018 Artificial neural networks
  • 106005 Bioinformatics
  • 202037 Signal processing
  • 202036 Sensor systems
  • 202035 Robotics

JKU Focus areas

  • Digital Transformation

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