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

Published: 03 November 2020 Publication History

Abstract

We present a pilot study on crea.blender, a novel co-creative game designed for large-scale, systematic assessment of distinct constructs of human creativity. Co-creative systems are systems in which humans and computers (often with Machine Learning) collaborate 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.

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Cited By

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  • (2023)Using Process Data of Task Performance in Creative Thinking AssessmentPsychological Science and EducationПсихологическая наука и образование10.17759/pse.202328040428:4(63-80)Online publication date: 3-Nov-2023
  • (2023)Creativity in the age of generative AINature Human Behaviour10.1038/s41562-023-01751-17:11(1836-1838)Online publication date: 20-Nov-2023
  • (2022)Is Flexibility More than Fluency and Originality?Journal of Intelligence10.3390/jintelligence1004009610:4(96)Online publication date: 1-Nov-2022
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  1. crea.blender: A Neural Network-Based Image Generation Game to Assess Creativity

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        cover image ACM Conferences
        CHI PLAY '20: Extended Abstracts of the 2020 Annual Symposium on Computer-Human Interaction in Play
        November 2020
        435 pages
        ISBN:9781450375870
        DOI:10.1145/3383668
        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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        Published: 03 November 2020

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        Author Tags

        1. co-creative systems
        2. divergent and convergent thinking
        3. gan

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        View all
        • (2023)Using Process Data of Task Performance in Creative Thinking AssessmentPsychological Science and EducationПсихологическая наука и образование10.17759/pse.202328040428:4(63-80)Online publication date: 3-Nov-2023
        • (2023)Creativity in the age of generative AINature Human Behaviour10.1038/s41562-023-01751-17:11(1836-1838)Online publication date: 20-Nov-2023
        • (2022)Is Flexibility More than Fluency and Originality?Journal of Intelligence10.3390/jintelligence1004009610:4(96)Online publication date: 1-Nov-2022
        • (2022)A distributed model of collective creativity in free playFrontiers in Education10.3389/feduc.2022.9022517Online publication date: 1-Dec-2022
        • (2022)Dancing with the Unexpected and BeyondProceedings of the Tenth International Symposium of Chinese CHI10.1145/3565698.3565777(129-140)Online publication date: 22-Oct-2022
        • (2022)Initial Images: Using Image Prompts to Improve Subject Representation in Multimodal AI Generated ArtProceedings of the 14th Conference on Creativity and Cognition10.1145/3527927.3532792(15-28)Online publication date: 20-Jun-2022
        • (2022)GANSlider: How Users Control Generative Models for Images using Multiple Sliders with and without Feedforward InformationProceedings of the 2022 CHI Conference on Human Factors in Computing Systems10.1145/3491102.3502141(1-15)Online publication date: 29-Apr-2022
        • (2022)Image Generation: A ReviewNeural Processing Letters10.1007/s11063-022-10777-x54:5(4609-4646)Online publication date: 11-Mar-2022
        • (2021)Creativity assessment games and crowdsourcingProceedings of the 13th Conference on Creativity and Cognition10.1145/3450741.3467465(1-5)Online publication date: 22-Jun-2021
        • (2021)Digital Games for Creativity Assessment: Strengths, Weaknesses and OpportunitiesCreativity Research Journal10.1080/10400419.2021.1971447(1-27)Online publication date: 23-Sep-2021

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