MemoryGame: Decentralized P2E Game for Visual Working Memory Training

Visual Working Memory (VWM) is the ability to maintain task-relevant visual information over a brief delay after direct visual input has been removed and it is essential for learning new skills, solving tasks and acquiring new knowledge. In this paper, authors presented MemoryGame: a decentralized P...

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Bibliographic Details
Published in2024 IEEE Gaming, Entertainment, and Media Conference (GEM) pp. 1 - 2
Main Authors Christie, Gabrielle, Durkin, Sean, Olson, Alexander, Casal, Maylen, Santoso, Markus, Suvajdzic, Marko
Format Conference Proceeding
LanguageEnglish
Published IEEE 05.06.2024
Subjects
decentralization
Delays
Entertainment industry
Games
Media
play-to-earn
Privacy
Training
visual working memory
Visualization
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Summary:Visual Working Memory (VWM) is the ability to maintain task-relevant visual information over a brief delay after direct visual input has been removed and it is essential for learning new skills, solving tasks and acquiring new knowledge. In this paper, authors presented MemoryGame: a decentralized Play-to-Earn (P2E) games that aim to train VWM. Blockchain implementation will maintain player's privacy where they could play the game activities without sharing any personal information. Furthermore, MemoryGame applied P2E model to encourage player's engagement and maintain the sustainable retention rate.
ISSN:2766-6530
DOI:10.1109/GEM61861.2024.10585774