🤖 AI Summary
Existing research on human-AI co-creativity struggles to capture the temporal evolution of higher-order interaction dynamics. This work proposes a Cognitive Trajectory Modeling (CTM) framework that, for the first time, generalizes cognitive trajectories beyond task-specific encodings. Grounded in theories of generative creativity and meaning-making, the framework formalizes trajectory principles imbued with directional cognitive significance. It integrates attractor-based dynamical systems with meaning-making curves to construct a three-layer dynamic architecture—cognition, interaction, and domain—thereby distinguishing cognitive trajectories from mere interaction traces. The resulting paradigm offers an interpretable, theory-driven approach to temporal modeling, establishing a novel foundation for quantitative analysis and evolutionary modeling in human-AI co-creative processes.
📝 Abstract
Co-creative AI research increasingly seeks methods capable of representing how interaction dynamics evolve through time. While many existing approaches focus on observable interaction characteristics, interaction metrics, behavioral coding schemes, or activity traces, these methods often struggle to capture higher-order interaction dynamics, including how collaborative processes reorganize, stabilize, regulate, and evolve through time. This paper introduces Cognitive Trajectory Modeling (CTM) as a cognitive theory of interaction dynamics that conceptualizes cognition, interaction, and creative processes as temporally organized trajectories unfolding across cognitively meaningful attractor landscapes. CTM builds upon the theoretical foundations of the Enactive Model of Creativity and Creative Sense-Making (CSM), revisiting the role of sense-making curves and cognitive trajectories in representing co-creative interaction dynamics. We formalize this perspective through the Cognitive Trajectory Principle, which states that temporal representations are only theoretically interpretable as cognitive trajectories when their underlying states possess directional cognitive meaning. Building on this principle, CTM generalizes the notion of cognitive trajectories beyond any particular coding scheme and provides a broader framework for modeling interaction dynamics through trajectories unfolding across meaningful attractor landscapes. We further distinguish cognitive trajectories from interaction traces and situate CTM within a broader hierarchy of cognitive, interaction, and domain dynamics. More broadly, we argue that understanding co-creative systems requires methods capable of modeling how cognition and interaction dynamics unfold through time. CTM provides a foundation for studying interaction dynamics across co-creative AI and human-AI interaction.