Cowan (2001) is an oft-cited paper proposing three to five "chunks" of capacity in human attention: https://www.cambridge.org/core/journals/behavioral-and-brain

Cowan (2001) is an oft-cited paper proposing three to five "chunks" of capacity in human attention: https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/article/magical-number-4-in-shortterm-memory-a-reconsideration-of-mental-storage-capacity/44023F1147D4A1D44BDC0AD226838496 https://www.cambridge.org/core/journals/behavioral-and-brain... The full PDF is worth a read (Figure 1 may be of interest to many here): https://www.cambridge.org/core/services/aop-cambridge-core/content/view/44023F1147D4A1D44BDC0AD226838496/S0140525X01003922a.pdf/the-magical-number-4-in-short-term-memory-a-reconsideration-of-mental-storage-capacity.pdf https://www.cambridge.org/core/services/aop-cambridge-core/c... If "attention is all you need" then it's something we do indeed lack, in comparison to LLMs! But it's an interesting question: might machine cognition benefit from similar bottlenecks in an attention algorithm? Advancements like Kimi Linear seem to indicate that we're far from the finish line: https://arxiv.org/abs/2510.26692 https://arxiv.org/abs/2510.26692