Testing computational models of learning from social, real, and fictive feedback in human and nonhuman primates

• Antragsteller: Professor Dr. Markus Ullsperger
• Fachliche Zuordnung: Kognitive, systemische und Verhaltensneurobiologie
• Förderung: Förderung von 2014 bis 2019
• Projektkennung: Deutsche Forschungsgemeinschaft (DFG) - Projektnummer 258026672

The consortium will develop computational models of learning and decision making and test them in two biological systems, humans and macaques. The models shall account for the use of different sources of information on action outcomes to guide future behavior: feedback on real outcomes, fictive feedback on outcomes that would have been obtained had a different action been chosen, and observational feedback on action outcomes seen in other actors in social situations. We expect that the same computational principles apply for all information sources but that the learning parameters differ quantitatively between feedback type and species. Model fits to behavior and model-based analyses of neural data will reveal brain correlates of the computational variables. We expect to find anatomical and functional dissociations during monitoring of the different sources of feedback information and later convergence on a single mechanism implementing changes to future behavior. Using complementary methods in two primate species will lead to better generalizability of the models, better understanding of underlying neural mechanisms, and mutually informed choice of recording sites and analysis.

• DFG-Verfahren: Sachbeihilfen
• Internationaler Bezug: Japan
• Beteiligte Person: Professor Masaki Isoda, Ph.D.