Neural Correlates of Decision Preferences: a Methodological Validation Study

• Grant number: 15/03931-0
• Support Opportunities: Regular Research Grants
• Start date: September 01, 2015
• End date: August 31, 2017
• Field of knowledge: Interdisciplinary Subjects
• Principal Investigator: Alvaro Machado Dias
• Grantee: Alvaro Machado Dias
• Host Institution: Escola Paulista de Medicina (EPM). Universidade Federal de São Paulo (UNIFESP). Campus São Paulo. São Paulo , SP, Brazil

Abstract

Context. Decision-making is one of the most important cognitive processes to human beings and one of the main differentiators in relation to other species. There's a trend among studies on the matter to be multidisciplinary, through the incorporation of psychology, economics, anthropology and neuroscience. Under such paradigm, several studies on neural correlates have been published. Among with the great advances that this trend allowed, the field became quite fuzzy, polyphonic, increasing methodological uncertainty. Objectives. This study has three goals: (1) to conduct a group of experiments to establish the neural correlates of preferences, using EEG, EMG, GSR and eye tracking; (2) to create a multimodal database of physiological correlates of preference; (3) to introduce a formula defining the ideal combination of physiological variables and their relative weight to the evaluation of preference correlates. Methods. The experimental research will involve three groups of healthy adults (N= 60), which will be submitted to physiological exams, while watching short movies, which they will be asked to rank in order of preference. Then, we will treat the results statistically so as to determine which are the physiological variables (modalities and relative importance) that should be used to predict preference in similar contexts. Expected Results. This study will increase our knowledge about neural correlates of preferences and ease the application of neuroscience and psychology in economics and marketing. Several high impact papers are expected to be published, along with a patent related to the algorithm that best predicts preference. (AU)