Distributed range adaptation in human parietal encoding of numbers

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 stimulus 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.

We are grateful to Cornelia Schnyder and Elena Boldin at the Zurich Center for Neuroeconomics for their excellent assistance with recruitment and participant facilitation, and thank Irini Paschalis for scanning support during data collection. C.C.R. received funding from the University Research Priority Program Adaptive Brain Circuits in Development and Learning (URPP AdaBD) at the University of Zurich as well as from the Swiss National Science Foundation (SNSF, grants no. 100019L-173248 and 10006863). G.d.H. was supported by the Dutch Research Council (NWO, Rubicon grant no. 019.183SG.017/8O3B) and the University of Zurich (Forschungskredit grant no.

K-33153-02-01), as well as the URPP AdaBD. S.B. was supported by a PhD scholarship from the Marlene Porsche Graduate School of Neuroeconomics and funding by the URPP AdaBD. S.J.G. received funding from the U.S.

National Science Foundation (NSF, grant no. DRL-2024462), the United States Air Force Office of Scientific Research (grant no. FA9550-20-1-0413), Schmidt Sciences (Polymath Award), and the Kempner Institute for the Study of Natural and Artificial Intelligence.