Discrete Representations in Working Memory: A Hypothesis and Computational Investigations

We present a novel hypothesis concerning the nature and development of working memory representations and some initial computational investigations of this hypothesis. Working memory refers to the active maintenance of information in the service of complex cognition, such as language comprehension, spatial thinking, and problem solving (Miyake & Shah, 1999). We propose that the unique demands placed on the working memory system shape its representations over learning and development, affecting the use of working memory by the cognitive system as a whole. Our primary source of insight into this process comes from a computational analysis, which is used to integrate and explore relevant findings from neurobiology as well as developmental and adult cognition. Our specific hypothesis is that to maintain information in an active state over delays and in the face of interference (e.g., from incoming stimuli, ongoing processing, and noise), working memory representations should be discrete in nature. A discrete representation admits to only a finite set of possible states, rather than representing continuous states. For example, the integers from 1 to 100 form a discrete set, in contrast to the real numbers in this range. Discreteness imparts a measure of robustness to the representation because small amounts of noise can be overcome by interpreting an observed state as the nearest discrete state (Figure 1). Many different processing mechanisms could achieve this remapping of perturbed states, including attractor dynamics in neura