Shape Perception Simultaneously Up- and Downregulates Neural Activity in the Primary Visual Cortex

• Shape perception has both excitatory and inhibitory effects in V1
• V1 regions whose bottom-up input is predicted by the shape are suppressed
• V1 regions where input is expected but absent are enhanced
• Shape perception modulates V1 activity independently of task requirements

SummaryAn essential part of visual perception is the grouping of local elements (such as edges and lines) into coherent shapes. Previous studies have shown that this grouping process modulates neural activity in the primary visual cortex (V1) that is signaling the local elements [1, 2, 3 and 4]. However, the nature of this modulation is controversial. Some studies find that shape perception reduces neural activity in V1 [2, 5 and 6], while others report increased V1 activity during shape perception [1, 3, 4, 7, 8, 9 and 10]. Neurocomputational theories that cast perception as a generative process [11, 12 and 13] propose that feedback connections carry predictions (i.e., the generative model), while feedforward connections signal the mismatch between top-down predictions and bottom-up inputs. Within this framework, the effect of feedback on early visual cortex may be either enhancing or suppressive, depending on whether the feedback signal is met by congruent bottom-up input. Here, we tested this hypothesis by quantifying the spatial profile of neural activity in V1 during the perception of illusory shapes using population receptive field mapping. We find that shape perception concurrently increases neural activity in regions of V1 that have a receptive field on the shape but do not receive bottom-up input and suppresses activity in regions of V1 that receive bottom-up input that is predicted by the shape. These effects were not modulated by task requirements. Together, these findings suggest that shape perception changes lower-order sensory representations in a highly specific and automatic manner, in line with theories that cast perception in terms of hierarchical generative models.