SWIFT: a novel method to track the neural correlates of recognition.

Isolating the neural correlates of object recognition and studying their fine temporal dynamics have been a great challenge in neuroscience. A major obstacle has been the difficulty to dissociate low-level feature extraction from the actual object recognition activity. Here we present a new technique called semantic wavelet-induced frequency-tagging (SWIFT), where cyclic wavelet-scrambling allowed us to isolate neural correlates of object recognition from low-level feature extraction in humans using EEG. We show that SWIFT is insensitive to unrecognized visual objects in natural images, which were presented up to 30s, but is highly selective to the recognition of the same objects after their identity has been revealed. The enhancement of object representations by top-down attention was particularly strong with SWIFT due to its selectivity for high-level representations. Finally, we determined the temporal dynamics of object representations tracked by SWIFT and found that SWIFT can follow a maximum of between 4 and 7 different object representations per second. This result is consistent with a reduction in temporal capacity processing from low to high-level brain areas.