Proceedings of the Annual Meeting of the Cognitive Science Society

Classical attractor networks such as Hopfield models treat memories as static points in a fixed energy landscape. While foundational, this framework cannot capture how cognition unfolds through time as agents interact with their environment and recall structured experiences. In this work, I present an open dynamical systems model of memory implemented in an interactive physicsbased environment, where memories are not point attractors but attracting trajectories carved into a deformable potential landscape through experience dependent learning. A state variable evolves under the combined influence of learned landscape gradients and an external driving force, producing time-resolved recall paths rather than instantaneous convergence. I show that this system naturally produces graded recall, intrusion errors, and interference from basin geometry and drive dynamics alone. This framework reframes memory as a dynamical process embedded in perception/action loops and offers a bridge between attractor networks, sequential memory, and embodied cognition.