Decoding the categorization of visual motion with magnetoencephalography

摘要: Brain decoding techniques are particularly efficient at deciphering weak and distributed neural patterns. has primarily been used in cognitive neurosciences to predict differences between pairs of stimuli (e.g. faces vs. houses), but how distinct brain/perceptual states can be decoded following the presentation continuous sensory is unclear. Here, we developed a novel approach decode brain activity recorded with magnetoencephalography while participants discriminated coherence two intermingled clouds dots. Seven levels visual motion were tested reported colour most coherent cloud. The was formulated as ranked-classification problem, which model evaluated by its capacity order pair trials, each levels. Two function degree coherence. Importantly, perceptual thresholds found match decoder boundaries fully data-driven way. algorithm revealed earliest categorization hMT+, followed V1/V2, IPS, vlPFC.