Crea.Blender: A Neural Network-Based Image Generation Game to Assess Creativity

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.Comment: 4 page, 6 figures, CHI Pla

Crea.visions: A Platform for Casual Co-Creation with a Purpose Envisioning the Future through Human-AI Collaboration with Multiple Stakeholders

With recent advances in Artificial Intelligence and increasing emphasis on human augmentation and collaboration, time is ripe for AI-enhanced support tools which empower the public to formulate and visualize a collective vision of societal issues such as climate change. Here, we report on crea.visions, a platform for human-AI co-creation within Sustainable Development Goals centered community engagement. We present in-the-wild experiments with four versions of crea.visions involving 1,000+ participants and 25,000+ generated images over three years: Versions 1 and 2 focused on developing the novel tool empowering citizens to artistically communicate their favorite abstract societal issues. In versions 3 and 4, the generic image generation GAN was replaced by custom-trained versions for Venice and Paris respectively. Refining the platform towards communityspecific action, users of version 4 can geotag their identified problems, submit solutions candidates, and are actively linked up with relevant NGOs. Finally, version 4 includes the first workflow todate which combines AI image-generating modalities of sliders and text-to-image

Measuring Cognitive Abilities in the Wild: Validating a Population-Scale Game-Based Cognitive Assessment

Rapid individual cognitive phenotyping holds the potential to revolutionize domains as wide-ranging as personalized learning, employment practices, and precision psychiatry. Going beyond limitations imposed by traditional lab-based experiments, new efforts have been underway towards greater ecological validity and participant diversity to capture the full range of individual differences in cognitive abilities and behaviors across the general population. Building on this, we developed Skill Lab, a novel game-based tool that simultaneously assesses a broad suite of cognitive abilities while providing an engaging narrative. Skill Lab consists of six mini-games as well as 14 established cognitive ability tasks. Using a popular citizen science platform (N = 10725), we conducted a comprehensive validation in the wild of a game-based cognitive assessment suite. Based on the game and validation task data, we constructed reliable models to simultaneously predict eight cognitive abilities based on the users’ in-game behavior. Follow-up validation tests revealed that the models can discriminate nuances contained within each separate cognitive ability as well as capture a shared main factor of generalized cognitive ability. Our game-based measures are five times faster to complete than the equivalent task-based measures and replicate previous findings on the decline of certain cognitive abilities with age in our large cross-sectional population sample (N = 6369). Taken together, our results demonstrate the feasibility of rapid in-the-wild systematic assessment of cognitive abilities as a promising first step towards population-scale benchmarking and individualized mental health diagnostics