Individual differences in decision making strategies can be predicted by resting-state functional connectivity
As the study of individual differences becomes more widespread, questions arise about the reasons that a particular individual might adopt a particular strategy. Using both the behavioral and functional neuroimaging data of healthy adults from Human Connectome Project (HCP) we examined decision making in an incentive processing task (Delgado et al. 2000). A pair of distinct ACT-R models, representing a Declarative strategy and a Procedural strategy, were used to classify subjects as either Declarative or Procedural decision makers based on their behavioral data. A machine learning Lasso analysis was performed on each subject’s resting state functional connectivity, and was able to match the ACT-R model classifications to a high degree of accuracy. This suggests that the strength of connections between brain regions may play an important role in shaping the decision making process of a given individual.