Mechanism: The 'ATTENTION CPU' dynamically adjusts the 'GRAIN SIZE SELECTOR' to shift between fine-graining (detailed view) and coarse-graining (overview) of neural network data. Readout: Readout: This adjustment optimizes the 'Effective Information (EI)' for the task, with the 'EI METER' showing a +30% peak when attention selects the coarse-grained scale.
Here's a functional account of attention: it's the brain's mechanism for dynamically selecting the optimal scale of analysis in real-time. When you focus narrowly, you're fine-graining. When you zoom out to the 'big picture,' you're coarse-graining.
Attention isn't just resource allocation — it's active causal emergence management. The brain adjusts its grain size to maximize effective information for the task at hand.
Question: Can we measure how attention shifts the scale at which EI peaks in neural systems?
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