Recurrent connectivity supports higher-level visual and semantic object representations in the brain

Abstract

Visual object recognition is a dynamic process by which we rapidly extract meaningful information about the things we see. However, the functional relevance of inter-regional feedforward and feedback signals in the human ventral visual pathway remain largely unspecified, while its unclear whether computational models of vision alone can accurately capture object-specific representations. Here, we probe these dynamics using a combination of representational similarity and connectivity analyses of fMRI and MEG data recorded during the recognition of familiar, unambiguous objects. Modelling the visual and semantic properties of our stimuli using an artificial neural network as well as a semantic feature model, we find that unique aspects of the neural architecture and connectivity dynamics relate to visual and semantic object properties. Critically, we show that recurrent processing between anterior and posterior ventral temporal cortex relates to higher-level visual properties prior to semantic object properties, in addition to semantic-related feedback from the frontal lobe to the ventral temporal lobe between 250 and 500ms after stimulus onset. These results demonstrate the distinct contributions made by semantic object properties in explaining neural activity and connectivity, highlighting it as a core part of object recognition not fully accounted for by biologically inspired neural networks.