Correlation-based binocular disparity computations induce representational bottlenecks at the population level

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 such computations can support depth perception at the population level. Using psychophysics, fMRI, and deep neural networks, we investigated human stereopsis with dynamic anticorrelated stimuli that were dominated by mismatched binocular information. Humans reliably perceived reversed depth as predicted by correlation-based computations, yet population representations consistent with this percept emerged in mid-dorsal V3A, not in V1. Similarly, correlation-based neural networks failed to reproduce human depth judgments. Superposition theory from AI interpretability analysis reveals that correlation-based networks represented features with strong entanglement in shared dimensions, leading to destructive interference. Conversely, architectures integrating non-correlation mechanisms exhibited less entangled representations, aligning closely with human behavior. These findings suggest that correlation mechanisms induce representational bottlenecks at the population level, requiring the joint contribution of correlation and non-correlation processing channels to support robust stereopsis.