AbstractWhen recalling an experience of the past, many of the component features of the original episode may be, to a greater or lesser extent, reconstructed in the mind’s eye. There is strong evidence that the pattern of neural activity that occurred during an initial perceptual experience is recreated during episodic recall (neural reactivation), and that the degree of reactivation is correlated with the subjective vividness of the memory. However, while we know that reactivation occurs during episodic recall, we have lacked a way of precisely characterizing the contents—in terms of its featural constituents—of a reactivated memory. Here we present a novel approach, feature-specific informational connectivity (FSIC), that leverages hierarchical representations of image stimuli derived from a deep convolutional neural network to decode neural reactivation in fMRI data collected while participants performed an episodic recall task. We show that neural reactivation associated with low-level visual features (e.g. edges), high-level visual features (e.g. facial features), and semantic features (e.g. “terrier”) occur throughout the dorsal and ventral visual streams and extend into the frontal cortex. Moreover, we show that reactivation of both low- and high-level visual features correlate with the vividness of the memory, whereas only reactivation of low-level features correlates with recognition accuracy when the lure and target images are semantically similar. In addition to demonstrating the utility of FSIC for mapping feature-specific reactivation, these findings resolve the relative contributions of low- and high-level features to the vividness of visual memories, clarify the role of the frontal cortex during episodic recall, and challenge a strict interpretation the posterior-to-anterior visual hierarchy.
3D Radon Transform for Shape Retrieval Using Bag-of-Visual-Features
