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Dynamics of visual category learning with magnetoencephalography

Humans have the remarkable ability to distinguish visual categories rapidly, even when the target categories bear a high degree of similarity. It remains unclear how the brain encodes such categories, and more so, how it obtains adequacy through experience. Most fMRI and ERP studies focus on activities of specific brain regions and do not address the rich dynamics of neural encoding of visual categories. In this work, we pursue the neural underpinnings of visual categorization via magnetoencephalography when participants are trained to categorize two novel and resemblant classes of blob-like objects via feedback. Our analysis unfolds the temporal and spatial dynamics of encoding and finds that significant information about the visual categories is embedded at varying sites over multiple temporal stages termed as M100, M170, M250 and M350. Furthermore, we find support for our hypotheses that the brain becomes more efficient and informative about the categories through learning, with categorical information inferred from the M170 component improving reliably as individuals learn the task with greater accuracy.