Working memory is a key aspect of human cognition, from daily tasks like cooking dinner to complex problem-solving. While traditional theories and computational models have provided insights into dynamic activation of working memory (attributed to the inherent nature of neural oscillatory activity), they often fall short in comprehending how information is retained and processed over time. Consider the scenario of cooking dinner: a cook must maintain multiple ingredient representations and translate them into actions while cooking. Adaptive working memory relies on the interplay between fast neural oscillatory mechanisms and slower learning processes (typically via changing neural network weights). Building on recent advances in modeling, experimental design, and data analysis, we will bridge this gap. First, we will develop a framework demonstrating how oscillatory patterns in working memory can be learned and recruited for doing cognitive tasks. Second, we will investigate behavioral signatures of the model via “dense sampling” experimental designs. Third, using EEG we will evaluate the model’s predictions by measuring phase relationships between different processing areas. In all, we foresee that we will integrate working memory into the realm of adaptive cognitive processing.