George A. Miller’s classic discussion of memory capacity and Sidney Smith’s recoding experiments demonstrated that cognitive limits depend more strongly on the number of active representational units (“chunks”) than on the total amount of raw information being processed. Here, we reinterpret Smith’s experiments from the perspective of modern computational neuroscience and representation learning. We argue that Smith’s recoding procedure illustrates a general principle of representational recoding, whereby learning increases the amount of information associated with each active representational unit without increasing the number of units available to the system. This principle provides conceptual links among classical theories of chunking, modern representation learning, vector symbolic architectures, and biological memory systems. The framework may offer useful guidance for future neuromorphic systems operating under strict energetic and structural constraints.