Prediction of Decision Outcome via Observation of Brain Activity Signals During Decision-Making Process

Abstract

Making choices and decisions is one of the fundamental human activities. Due to its importance, over the years, different methods, designed to support decision-making process, were developed and used. Field of the decision-making support is still developing, and it uses the latest achievements of many sciences. Tools supporting the decision process fulfill their role in many different ways. Among them we can mention: information management, quantification of data or models manipulation. To this traditional approach, one more element could be added—indication of the conditions in which a better decision can be made. In this context, the cognitive neuroscience techniques can be very helpful. At the basis of this approach is the assumption that knowing the neurological grounds for decision making and decoding the decision-making process in the brain, we will be able to understand and take advantage of factors contributing to making the right decisions. In the presented research, we try to predict decisions on the basis of observations of brain signals activity during the time of the decision-making process. The data are registered with the use of electroencephalogram (EEG) due to its small size and low cost. The decisions made by participants are connected with the choice between two lotteries with different payoffs. The aim of the research is to determine, if by analyzing the brain signals, we are able to predict the decision adequacy.