crea.blender: A Neural Network-Based Image Generation Game to Assess Creativity

Janet Frances Rafner*, Hermes Arthur Hjorth, Sebastian Risi, Lotte Philipsen, Charles Dumas, Michael Mose Biskjaer, Lior Noy, Kristian Tylén, Carsten Bergenholtz, Jesse John Lynch, Blanka Zana, Jacob Sherson

*Corresponding author af dette arbejde

Publikation: Bidrag til bog/antologi/rapport/proceedingKonferencebidrag i proceedingsForskningpeer review

Abstract

We present a pilot study on crea.blender, a novel co-creative game designed for large-scale, systematic assessment of distinct con- structs of human creativity. Co-creative systems are systems in which humans and computers (often with Machine Learning) col- laborate on a creative task. This human-computer collaboration raises questions about the relevance and level of human creativity and involvement in the process. We expand on, and explore aspects of these questions in this pilot study. We observe participants play through three different play modes in crea.blender, each aligned with established creativity assessment methods. In these modes, players “blend” existing images into new images under varying constraints. Our study indicates that crea.blender provides a playful experience, affords players a sense of control over the interface, and elicits different types of player behavior, supporting further study of the tool for use in a scalable, playful, creativity assessment.
OriginalsprogEngelsk
TitelCHI PLAY 2020 - Extended Abstracts of the 2020 Annual Symposium on Computer-Human Interaction in Play
Antal sider5
UdgivelsesstedNew York
ForlagAssociation for Computing Machinery
Publikationsdato2 nov. 2020
Sider340-344
ISBN (Elektronisk)9781450375870
DOI
StatusUdgivet - 2 nov. 2020

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