Low-Dimensional Frontal Feedback Resolves High-Dimensional Visual Ambiguity in Human Visual Cortex

One major distinction between artificial neural networks and biological brains is the prevalence of extensive, long-range feedback connections in biological systems. Here we investigate unique contributions of these hierarchical feedback signals beyond feedforward processing and local recurrence by exploring their mechanistic role in resolving visual ambiguity caused by occlusion. Both empirical fMRI and EEG experiments and computational modeling show that when sensory evidence for faces became insufficient, the ventrolateral prefrontal cortex (vlPFC) sustained a low-dimensional belief state (e.g., animate vs. inanimate objects) and transmitted this abstract information back to the animacy map in the ventral temporal cortex (VTC) encompassing face-selective representations. Critically, in the hierarchical vision model inspired by this finding, this frontal feedback did not reshape the attractor geometry of the VTC; instead, it provided guidance to reroute ongoing neural dynamics away from ambiguous pseudo-states toward face attractor basins in the energy representational landscape. This control-based mechanism of feedback signals thus enabled perceptual completion by reconstructing missing facial features with temporal costs verified through EEG. Together, this multimodal study bridges analysis-by-synthesis theories of vision and dynamical-systems perspectives on long-range feedback as state-space control, and offers inspiration for the design of hierarchical AI architectures incorporating feedforward, recurrent, and feedback connections.