Neural correlates from the middle temporal gyrus via machine learning decode decision making variables during gambling in humans

The ability to make good decisions is contingent upon extracting the most relevant information from one’s contextual surroundings. The middle temporal gyrus lies at the nexus of visual processing streams and is hypothesized to play a crucial role in the semantic processing of visual information. In this study, stereoelectroencephalography measurements from the middle temporal gyrus were used to train random forest classifiers to decode betting behavior in a financial-decision making task for 10 subjects. Spectral features from the middle temporal gyrus were computed. The performance of these subject-specific classifiers ranged between 75% and 98% accuracy when predicting human betting behavior and provide evidence that the middle temporal gyrus encodes information related to decision-making. This study is the first to use stereoelectroencephalography to explore the functionality of the middle temporal gyrus in decision-making.