Bilgisayar Mühendisliği Bölümü Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.11779/1940
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Article Citation - WoS: 2Citation - Scopus: 4Unlocking the Neural Mechanisms of Consumer Loan Evaluations: an Fnirs and Mlbased Consumer Neuroscience Study(Frontiers Media SA, 2024-02-05) Girişken, Yener; Son, Semen; Demircioğlu, Esin Tuna; Filiz, Gözde; Çakar, Tuna; Ertuğrul, Seyit; Sayar, Alperen; Tuna, Esin; Son-Turan, SemenThis study conducted a comprehensive exploration of the neurocognitive processes underlying consumer credit decision-making using cutting-edge techniques from neuroscience and artificial intelligence (AI). Employing functional Near-Infrared Spectroscopy (fNIRS), the research examines the hemodynamic responses of participants while evaluating diverse credit offers. The study integrates fNIRS data with advanced AI algorithms, specifically Extreme Gradient Boosting, CatBoost, and Light Gradient Boosted Machine, to predict participants' credit decisions based on prefrontal cortex (PFC) activation patterns. Findings reveal distinctive PFC regions correlating with credit behaviors, including the dorsolateral prefrontal cortex (dlPFC) associated with strategic decision-making, the orbitofrontal cortex (OFC) linked to emotional valuations, and the ventromedial prefrontal cortex (vmPFC) reflecting brand integration and reward processing. Notably, the right dorsomedial prefrontal cortex (dmPFC) and the right vmPFC contribute to positive credit preferences. This interdisciplinary approach bridges neuroscience and finance, offering unprecedented insights into the neural mechanisms guiding financial choices. The study's predictive model holds promise for refining financial services and illuminating human financial behavior within the burgeoning field of neurofinance. The work exemplifies the potential of interdisciplinary research to enhance our understanding of human financial decision-making.Article Citation - WoS: 4Citation - Scopus: 8Unraveling Neural Pathways of Political Engagement: Bridging Neuromarketing and Political Science for Understanding Voter Behavior and Political Leader Perception(Frontiers Media SA, 2023-12-21) Çakar, Tuna; Filiz, GözdePolitical neuromarketing is an interdisciplinary field that combines marketing, neuroscience, and psychology to understand voter behavior and political leader perception. This interdisciplinary field offers novel techniques to understand complex phenomena such as voter engagement, political leadership, and party branding. This study aims to understand the neural activation patterns of voters when they are exposed to political leaders using functional near-infrared spectroscopy (fNIRS) and machine learning methods. We recruited participants and recorded their brain activity using fNIRS when they were exposed to images of different political leaders. This neuroimaging method (fNIRS) reveals brain regions central to brand perception, including the dorsolateral prefrontal cortex (dlPFC), the dorsomedial prefrontal cortex (dmPFC), and the ventromedial prefrontal cortex (vmPFC). Machine learning methods were used to predict the participants' perceptions of leaders based on their brain activity. The study has identified the brain regions that are involved in processing political stimuli and making judgments about political leaders. Within this study, the best-performing machine learning model, LightGBM, achieved a highest accuracy score of 0.78, underscoring its efficacy in predicting voters' perceptions of political leaders based on the brain activity of the former. The findings from this study provide new insights into the neural basis of political decision-making and the development of effective political marketing campaigns while bridging neuromarketing, political science and machine learning, in turn enabling predictive insights into voter preferences and behaviorArticle Citation - Scopus: 1A New Benchmark Dataset for P300 Erp-Based Bci Applications(Academic Press Inc Elsevier Science, 2023-04-01) Çakar, Tuna; Özkan, Hüseyin; Musellim, Serkan; Arslan, Suayb S.; Yağan, Mehmet; Alp, NihanBecause of its non-invasive nature, one of the most commonly used event-related potentials in brain -computer interface (BCI) system designs is the P300 electroencephalogram (EEG) signal. The fact that the P300 response can easily be stimulated and measured is particularly important for participants with severe motor disabilities. In order to train and test P300-based BCI speller systems in more realistic high-speed settings, there is a pressing need for a large and challenging benchmark dataset. Various datasets already exist in the literature but most of them are not publicly available, and they either have a limited number of participants or utilize relatively long stimulus duration (SD) and inter-stimulus intervals (ISI). They are also typically based on a 36 target (6 x 6) character matrix. The use of long ISI, in particular, not only reduces the speed and the information transfer rates (ITRs) but also oversimplifies the P300 detection. This leaves a limited challenge to state-of-the-art machine learning and signal processing algorithms. In fact, near-perfect P300 classification accuracies are reported with the existing datasets. Therefore, one certainly needs a large-scale dataset with challenging settings to fully exploit the recent advancements in algorithm design (machine learning and signal processing) and achieve high-performance speller results. To this end, in this article we introduce a new freely-and publicly-accessible P300 dataset obtained using 32-channel EEG, in the hope that it will lead to new research findings and eventually more efficient BCI designs. The introduced dataset comprises 18 participants performing a 40 -target (5 x 8) cued-spelling task, with reduced SD (66.6 ms) and ISI (33.3 ms) for fast spelling. We have also processed, analyzed, and character-classified the introduced dataset and we presented the accuracy and ITR results as a benchmark. The introduced dataset and the codes of our experiments are publicly accessible at https://data .mendeley.com /datasets /vyczny2r4w.(c) 2023 Elsevier Inc. All rights reserved.Article Citation - WoS: 2Citation - Scopus: 2Warning Notes in a Learner’s Dictionary: a Study of the Effectiveness of Different Formats(International Journal of Lexicography, 2022-01-25) Çakar, Tuna; Nesi, Hilary; Nural, ŞükrüThis study used an online correction task to explore the extent to which different types of warning notes in Longman Dictionary of Contemporary English Online were heeded when users tried to correct errors in the use of L2 target words. The task was completed by 332 participants, yielding 1,819 answers produced after clicking on links to relevant entries. Warning notes were categorised in terms of their formatting features, but there were found to be inconsistencies in the way the dictionary associated different categories with different kinds of learner error. Participants judged warning notes with more visual enhancements to be more useful, but in the correction task the position of the warning notes also seemed to affect the degree to which the warnings were successfully applied. Different types of warning notes in learners’ dictionaries have not been examined previously in any depth, and the results suggest that some adjustments to formatting and placement might make them more effective.