In recent years, there has been a growing interest in employing intelligent agents in writing. Previous work emphasizes the evaluation of the quality of end product—whether it was coherent and polished, overlooking the journey that led to the product, which is an invaluable dimension of the creative process. To understand how to recognize human efforts in co-writing with intelligent writing systems, we adapt Flower and Hayes’ cognitive process theory of writing and propose CoCo Matrix, a two-dimensional taxonomy of entropy and information gain, to depict the new human-agent co-writing model. We define four quadrants and situate thirty-four published systems within the taxonomy. Our research found that low entropy and high information gain systems are under-explored, yet offer promising future directions in writing tasks that benefit from the agent’s divergent planning and the human’s focused translation. CoCo Matrix, not only categorizes different writing systems but also deepens our understanding of the cognitive processes in human-agent co-writing. By analyzing minimal changes in the writing process, CoCo Matrix serves as a proxy for the writer’s mental model, allowing writers to reflect on their contributions. This reflection is facilitated through the measured metrics of information gain and entropy, which provide insights irrespective of the writing system used.
This paper discusses the digital animation artwork Motion and Trails, which builds on previous explorations of procedural techniques to generate unexpected visual results. Originating from animation processes that require quick workflows by embracing procedural methods, Motion and Trails continues this approach for the 2024 Creativity and Cognition Conference art exhibition by using simulated techniques in Maya and After Effects to transform 3D forms. Considering the theme of Creative Organic Spaces, the artwork aims to tap into creative possibilities through an informed but ‘unknown outcome’ approach to art making. As the latest iteration in an evolving organic process, Motion and Trails focuses animation as an innovative medium while demonstrating its immersive and cinematic capabilities. By utilizing procedural animation to embrace the unknown, this work emphasizes digital practice and abstraction to reveal imaginative new ways of creating virtual spaces.
This paper presents a novel 3D system for human motion analysis - Motion Data Visualization and Annotation (MoViAn). Designed to provide a comprehensive visual representation of 3D human motion data, MoViAn incorporates detailed visualization of gaze direction, hand movements, and object interactions, alongside an interactive interface for efficient data annotation. A user study involving eight participants indicates that MoViAn enables users to thoroughly explore and annotate human motion data, with System Usability Scale (SUS) results demonstrating a satisfactory usability level. The contribution of this paper lies in the development of an interactive and usable data analytics tool aimed at deepening the understanding of human behaviors and intentions in various creative, cognitive, and physical activities that ultimately can facilitate the design and creation of innovative tools that enhance human life in multiple domains.
How do we interpret a multi-participant choreographed performance in the public domain through digital technologies? In collaboration with data visualisation expert David Hunter from University of Colorado at Boulder, and visual artist Zach Duer from Virginia Tech, dAnCing LiNes explores how dance can generate a choreographic view of drawing through mediated representation. In this respect the artwork produced for dAnCing LiNes is not intended as a means of documentation of the live events but as a tool for new artistic production. The intention is to rethink performative drawing beyond the gestural trace of the body in movement through the use of data visualisations. Capturing chorographic scores and task-based instructions through digital technologies, the data visualisations explore how the agency of dance moves from the performative to the visual via technological means by using combinations of established computer vision techniques from OpenCV [1] like Optical Flow, Blob Detection. The visualisations not only reveal the rules of the underlying choreography in each location but also computationally play with and exemplify those rules on a per location basis (five in total).
