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Dayana Hristova
Researcher at University of Vienna
Publications - 16
Citations - 233
Dayana Hristova is an academic researcher from University of Vienna. The author has contributed to research in topics: Social media & Game design. The author has an hindex of 5, co-authored 14 publications receiving 159 citations. Previous affiliations of Dayana Hristova include Medical University of Vienna.
Papers
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Journal ArticleDOI
Joint Action: Mental Representations, Shared Information and General Mechanisms for Coordinating with Others.
Cordula Vesper,Ekaterina Abramova,Judith Bütepage,Francesca Ciardo,Benjamin Crossey,Alfred O. Effenberg,Dayana Hristova,April Karlinsky,Luke McEllin,Sari R. R. Nijssen,Laura Schmitz,Basil Wahn +11 more
TL;DR: An overview of the mental representations involved in joint action is provided, how co-actors share sensorimotor information and what general mechanisms support coordination with others are discussed.
Journal ArticleDOI
Sources of Embodied Creativity: Interactivity and Ideation in Contact Improvisation.
TL;DR: The answer is that dancers produce a stream of momentary micro-intentions that say “yes, and”, or “no, but” to short-lived micro-affordances, which allows both individuals to skillfully continue, elaborate, tweak, or redirect the collective movement dynamics.
Journal ArticleDOI
The Micro-genesis of Improvisational Co-creation
Michael Kimmel,Dayana Hristova +1 more
TL;DR: In this article, the authors emphasize the vital role that processes of active engagement and interaction play play in creativity research and emphasize the importance of the role of the environment in this process.
Posted ContentDOI
Snapchat Streaks: How Adolescents Metagame Gamification in Social Media
TL;DR: This paper presents strategies that Viennese adolescents use to uphold snap streaks – a gamified challenge on Snapchat inviting users to exchange at least one snap each 24 hours to keep the score.
Journal Article
Assessing the Difficulty of Chess Tactical Problems
TL;DR: It is found that assessing difficulty is also very difficult for human experts, and algorithms designed to estimate difficulty should interpret the complexity of a game tree in the light of knowledge-based patterns that human players are able to detect in a chess problem.