Covariance-based Decoding Reveals Content-specific Feature Integration and Top-down Processing for Imagined Faces versus Places

SUMMARY When we internally generate complex mental images, we need to combine multiple features or parts of a scene into a coherent whole. Direct evidence for such feature integration during visual imagery is still lacking. Moreover, cognitive control mechanisms, including memory and attention, exert top-down influences on the perceptual system when generating mental images. However, it is unclear whether such top-down processing is specific to the content of visual imagery or not. Feature integration and top-down processing involve short-range connectivity within visual areas, and long-range connectivity between control areas and visual areas, respectively. Here, we used a minimally constrained experimental paradigm wherein imagery categories were prompted using visual word cues only, and we decoded face versus place imagery - two object categories that are well-known to be associated with different visual features and neural generators - based on their distinct connectivity patterns. Our results show that face and place imagery can be decoded from both short-range and long-range connections. These findings suggest that feature integration within visual areas does not need an external stimulus to be triggered but occurs also for purely internally generated images. Furthermore, control areas associated with memory and attention provide information that is specific to the imagery content. Mental imagery belongs to a class of internally driven cognitive processes that has been notoriously challenging to decode because the relevant endogenous signals are temporally misaligned across trials. We suggest that our successful application of covariance-based connectivity decoding to visual imagery paves the way for future applications to internally driven cognitive processes such as visual attention, visual working memory and visual prediction