Distinguishing Cognitive Processes: A Machine Learning Approach to Decode fNIRS Data for Third-Party Punishment and Credit Decision-Making;
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Date
2024
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Institute of Electrical and Electronics Engineers Inc.
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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.
Description
Berdan Civata B.C.; et al.; Figes; Koluman; Loodos; Tarsus University
Keywords
Credit taking decisions, Decision making, Functional near-infrared spectroscopy, Machine learning, third-party punishment decisions
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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