Population codes for biological stereopsis extend beyond correlation-based binocular disparity computations

Binocular stereopsis depends on comparing the images seen by the two eyes. Although correlation-based models explain responses of individual binocular neurons in primary visual cortex (V1), it remains elusive whether population representations formed by local correlation activity patterns can support depth perception under ambiguous inputs. Using psychophysics, fMRI, and neural network modeling, we tested human stereopsis with dynamic anticorrelated stimuli that were dominated by binocular mismatches. Humans reliably perceived reversed depth as predicted by correlation-based computations, yet population representations consistent with this percept were detected in V3A, not V1. Shallow and deep neural networks constrained by correlation-like binocular interactions did not capture the full pattern of human depth judgments. Analyses of their internal representations showed greater representational overlap, whereas deep architectures not constrained to explicit correlation interactions exhibited less entangled representations and better aligned with human behavior. These findings suggest that biological stereopsis may rely on population coding beyond correlation-like computations.