Proceedings of the Annual Meeting of the Cognitive Science Society

Memory is a vital part of cognition. Its many parts determine how one learns, plans, and ultimately experiences the world around them. As a bridge between short-term memory and long-term memory, intermediate-term memory allows for semi-stable memories to be rapidly encoded through short-term synaptic plasticity. We present a model of short-term and intermediate-term memory based around Semantic Pointers and realize the model in a spiking neural network. Unlike past work, we characterize short-term memory as a dynamical system and realize intermediate-term memory as implicit association learning. Further, we demonstrate our model's ability to generalize across domains by achieving human-like results on semantic and spatial memory tasks with minimal parameter changes. By examining Cohen's _ values first within and then between tasks, we find an overall small difference (Mean: |_| = 0.12 ± 0.06, Median: ˆ |_| = 0.16)), suggesting strong similarity between model and human data across all three tasks.