This study combines magnetoencephalography and machine learning to investigate the representation and access of concepts in the brain over time, showing dynamic changes in visual and semantic information processing.
Our understanding of the surrounding world and communication with other people are tied to mental representations of concepts. In order for the brain to recognize an object, it must determine which concept to access based on information available from sensory inputs. In this study, we combine magnetoencephalography and machine learning to investigate how concepts are represented and accessed in the brain over time. Using brain responses from a silent picture naming task, we track the dynamics of visual and semantic information processing, and show that the brain gradually accumulates information on different levels before eventually reaching a plateau. The timing of this plateau point varies across individuals and feature models, indicating notable temporal variation in visual object recognition and semantic processing. An MEG study on semantic processing of concepts in a silent picture naming task indicates that the brain gradually accumulates information before reaching a plateau.
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