Distributed range adaptation in human parietal encoding of numbers

Abstract

The brain's representations are encoded in the collective activity of neural populations, whose non-monotonic tuning properties define the population code. Adaptive behavior calls for flexible retuning of this code as stimuli statistics change across contexts. Yet how such adaptation occurs is unknown. Using fMRI during a numerosity-estimation task with variable number ranges, we show that number representations in human parietal cortex dynamically recalibrate to context, enabling context-sensitive behavioral accuracy. The tuning properties of neural populations collectively shift and rescale with the range. This distributed range adaptation achieves efficient coding in real time: neural precision scales with the range and predicts corresponding changes in behavioral precision. Individuals with stronger neural adaptation show larger behavioral adjustments. These findings extend static sensory efficient coding to adaptive representations of abstract magnitudes. Such flexible population tuning may constitute a canonical mechanism of encoding networks that enables the brain to sustain precise, adaptive behavior.