Filiz,G.Son,S.Sayar,A.Ertuğrul,S.Şahin,T.Akyürek,G.Çakar,T.2024-10-152024-10-1520240979-835038896-1https://doi.org/10.1109/SIU61531.2024.10600798https://hdl.handle.net/20.500.12469/6574Berdan Civata B.C.; et al.; Figes; Koluman; Loodos; Tarsus UniversityFunctional near-infrared spectroscopy (fNIRS) has seen increasingly widespread use in examining brain activity and cognitive processes. However, the existing literature provides insufficient information on distinguishing between different decision-making mechanisms. This study explores the application of fNIRS in differentiating between two distinct decision-making processes: third-party punishment decisions and credit decisions. The research includes analyzing fNIRS data collected during these processes and classifying the associated neural patterns using machine learning. The findings reveal that fNIRS, in conjunction with ML, holds substantial potential to enhance the depth of understanding of decision-making processes in neuroscience research. © 2024 IEEE.trinfo:eu-repo/semantics/closedAccessCredit taking decisionsDecision makingFunctional near-infrared spectroscopyMachine learningthird-party punishment decisionsConference Object10.1109/SIU61531.2024.106007982-s2.0-85200849837N/AN/A