Predicting human decisions in socioeconomic interaction using real-time functional magnetic resonance imaging (rtfMRI)

A major field in cognitive neuroscience investigates neuronal correlates of human decision-making processes [1, 2]. Is it possible to predict a decision before it is actually revealed by the volunteer? In the presented manuscript we use a standard paradigm from economic behavioral research that proved emotional influences on human decision making: the Ultimatum Game (UG). In the UG, two players have the opportunity to split a sum of money. One player is deemed the proposer and the other, the responder. The proposer makes an offer as to how this money should be split between the two. The second player can either accept or reject this offer. If it is accepted, the money is split as proposed. If rejected, then neither player receives anything. In the presented study a real-time fMRI system was used to derive the brain activation of the responder. Using a Relevance-Vector-Machine classifier it was possible to predict if the responder will accept or reject an offer. The classification result was presented to the operator 1-2 seconds before the volunteer pressed a button to convey his decision. The classification accuracy reached about 70% averaged over six subjects.