The application of machine learning techniques to neuroimaging data (multivoxel pattern classification, MVPA) has provided cognitive neuroscientists a way to decode information from patterns of neural activity and to draw inferences about neural representation. While most of the studies focus on the decoding of sensory stimuli (e.g. pictures) in the visual cortex, I used MVPA to test whether the patterns of activity in the prefrontal cortex, the brain area essential for abstract thinking, contains task rule information. The logic is that if we can successfully decode two different rules using the activity patterns in a particular brain area, then this brain areas cares about rule information.
For the first time, my research showed a neural mechanism that involves the selection of abstract task rules in order to guide goal-directed behavior. In addition, we found inferior frontal gyrus not only being active when there is a conflict in rule selection but also contains rule information.
Machine learning and pattern classification in combination with neuroimaging can help cognitive neuroscientists to make inferences about the representational contents of the brain.
Read more about the study: "A neural mechanism of cognitive control for resolving conflict between abstract task rules"
Click here if you want to try out my analysis code: MVPA python code. It utilizes PyMVPA package