Distinguishing Cognitive Processes: a Machine Learning Approach To Decode Fnirs Data for Third-Party Punishment and Credit Decision-Making;

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dc.authorscopusid 58634073400
dc.authorscopusid 57189076696
dc.authorscopusid 57904383300
dc.authorscopusid 57905176100
dc.authorscopusid 57963550900
dc.authorscopusid 58069183200
dc.authorscopusid 57190280446
dc.contributor.author Filiz,G.
dc.contributor.author Son,S.
dc.contributor.author Sayar,A.
dc.contributor.author Ertuğrul,S.
dc.contributor.author Şahin,T.
dc.contributor.author Akyürek,G.
dc.contributor.author Çakar,T.
dc.date.accessioned 2024-10-15T19:42:42Z
dc.date.available 2024-10-15T19:42:42Z
dc.date.issued 2024
dc.department Kadir Has University en_US
dc.department-temp Filiz 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, Turkey en_US
dc.description Berdan Civata B.C.; et al.; Figes; Koluman; Loodos; Tarsus University en_US
dc.description.abstract Functional 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.citationcount 0
dc.identifier.doi 10.1109/SIU61531.2024.10600798
dc.identifier.isbn 979-835038896-1
dc.identifier.scopus 2-s2.0-85200849837
dc.identifier.uri https://doi.org/10.1109/SIU61531.2024.10600798
dc.identifier.uri https://hdl.handle.net/20.500.12469/6574
dc.language.iso tr en_US
dc.publisher Institute of Electrical and Electronics Engineers Inc. en_US
dc.relation.ispartof 32nd 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 -- 201235 en_US
dc.relation.publicationcategory Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/closedAccess en_US
dc.scopus.citedbyCount 1
dc.subject Credit taking decisions en_US
dc.subject Decision making en_US
dc.subject Functional near-infrared spectroscopy en_US
dc.subject Machine learning en_US
dc.subject third-party punishment decisions en_US
dc.title Distinguishing Cognitive Processes: a Machine Learning Approach To Decode Fnirs Data for Third-Party Punishment and Credit Decision-Making; en_US
dc.title.alternative Bilişsel Süreçlerin Ayırt Edilmesi: Özgeci Cezalandırma ve Kredi Karar Alma Süreçleri için Fnırs Verilerinin Makine Öğrenimi ile Çözümlenmesi en_US
dc.type Conference Object en_US
dspace.entity.type Publication

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