Distinguishing Cognitive Processes: A Machine Learning Approach to Decode fNIRS Data for Third-Party Punishment and Credit Decision-Making;

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dc.authorscopusid58634073400
dc.authorscopusid57189076696
dc.authorscopusid57904383300
dc.authorscopusid57905176100
dc.authorscopusid57963550900
dc.authorscopusid58069183200
dc.authorscopusid57190280446
dc.contributor.authorFiliz,G.
dc.contributor.authorSon,S.
dc.contributor.authorSayar,A.
dc.contributor.authorErtuğrul,S.
dc.contributor.authorŞahin,T.
dc.contributor.authorAkyürek,G.
dc.contributor.authorÇakar,T.
dc.date.accessioned2024-10-15T19:42:42Z
dc.date.available2024-10-15T19:42:42Z
dc.date.issued2024
dc.departmentKadir Has Universityen_US
dc.department-tempFiliz G., Fen Bilimleri Enstitüsü, Turkey, MEF Üniversitesi, Turkey, İstanbul, Turkey; Son S., İşletme Bölümü, Turkey, MEF Üniversitesi, Turkey, İstanbul, Turkey; Sayar A., Tam Finans Faktöring A.Ş., Turkey, İstanbul, Turkey; Ertuğrul S., Tam Finans Faktöring A.Ş., Turkey, İstanbul, Turkey; Şahin T., Graduate School of Systemic Neurosciences, Turkey, Ludwig Maximilian University of Munich, Germany, Munich, Germany; Akyürek G., Hukuk Fakültesi, Turkey, MEF Üniversitesi, Turkey, İstanbul, Turkey; Erözden O., Hukuk Fakültesi, Turkey, Kadir Has Üniversitesi, Turkey, İstanbul, Turkey; Girişken Y., İktisadi ve İdari Bilimler Fakültesi, Turkey, Uluslararası Final Üniversitesi, Turkey, İstanbul, Turkey; Çakar T., Bilgisayar Mühendisliği, Turkey, MEF Üniversitesi, Turkey, İstanbul, Turkeyen_US
dc.descriptionBerdan Civata B.C.; et al.; Figes; Koluman; Loodos; Tarsus Universityen_US
dc.description.abstractFunctional 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.en_US
dc.identifier.citation0
dc.identifier.doi10.1109/SIU61531.2024.10600798
dc.identifier.isbn979-835038896-1
dc.identifier.scopus2-s2.0-85200849837
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://doi.org/10.1109/SIU61531.2024.10600798
dc.identifier.urihttps://hdl.handle.net/20.500.12469/6574
dc.identifier.wosqualityN/A
dc.language.isotren_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof32nd IEEE Conference on Signal Processing and Communications Applications, SIU 2024 - Proceedings -- 32nd IEEE Conference on Signal Processing and Communications Applications, SIU 2024 -- 15 May 2024 through 18 May 2024 -- Mersin -- 201235en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectCredit taking decisionsen_US
dc.subjectDecision makingen_US
dc.subjectFunctional near-infrared spectroscopyen_US
dc.subjectMachine learningen_US
dc.subjectthird-party punishment decisionsen_US
dc.titleDistinguishing Cognitive Processes: A Machine Learning Approach to Decode fNIRS Data for Third-Party Punishment and Credit Decision-Making;en_US
dc.title.alternativeBilişsel Süreçlerin Ayırt Edilmesi: Özgeci Cezalandırma ve Kredi Karar Alma Süreçleri için fNIRS Verilerinin Makine Öğrenimi ile Çözümlenmesien_US
dc.typeConference Objecten_US
dspace.entity.typePublication

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