PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/20.500.11779/1928

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Now showing 1 - 5 of 5

Citation - WoS: 6
Citation - Scopus: 5
Exploring Attitudes Toward "sugar Relationships" Across 87 Countries: a Global Perspective on Exchanges of Resources for Sex and Companionship
(Springer, 2023) Putz, Adam; Kowal, Marta; Bandi, Szabolcs A; Kocsor, Ferenc; Toplu-Demirtaş, Ezgi; Láng, András; Meskó, Norbert; Han, Hyemin
The current study investigates attitudes toward one form of sex for resources: the so-called sugar relationships, which often involve exchanges of resources for sex and/or companionship. The present study examined associations among attitudes toward sugar relationships and relevant variables (e.g., sex, sociosexuality, gender inequality, parasitic exposure) in 69,924 participants across 87 countries. Two self-report measures of Acceptance of Sugar Relationships (ASR) developed for younger companion providers (ASR-YWMS) and older resource providers (ASR-OMWS) were translated into 37 languages. We tested cross-sex and cross-linguistic construct equivalence, cross-cultural invariance in sex differences, and the importance of the hypothetical predictors of ASR. Both measures showed adequate psychometric properties in all languages (except the Persian version of ASR-YWMS). Results partially supported our hypotheses and were consistent with previous theoretical considerations and empirical evidence on human mating. For example, at the individual level, sociosexual orientation, traditional gender roles, and pathogen prevalence were significant predictors of both ASR-YWMS and ASR-OMWS. At the country level, gender inequality and parasite stress positively predicted the ASR-YWMS. However, being a woman negatively predicted the ASR-OMWS, but positively predicted the ASR-YWMS. At country-level, ingroup favoritism and parasite stress positively predicted the ASR-OMWS. Furthermore, significant cross-subregional differences were found in the openness to sugar relationships (both ASR-YWMS and ASR-OMWS scores) across subregions. Finally, significant differences were found between ASR-YWMS and ASR-OMWS when compared in each subregion. The ASR-YWMS was significantly higher than the ASR-OMWS in all subregions, except for Northern Africa and Western Asia.
Citation - WoS: 3
Citation - Scopus: 3
Mice Make Temporal Inferences About Novel Locations Based on Previously Learned Spatiotemporal Contingencies
(Springer, 2022) Balci, Fuat; Gür, Ezgi; Duyan, Yalçin A.
Animals learn multiple spatiotemporal contingencies and organize their anticipatory responses accordingly. The representational/computational capacity that underlies such spatiotemporally guided behaviors is not fully understood. To this end, we investigated whether mice make temporal inferences of novel locations based on previously learned spatiotemporal contingencies. We trained 18 C57BL/6J mice to anticipate reward after three different intervals at three different locations and tested their temporal expectations of a reward at five locations simultaneously, including two locations that were not previously associated with reward delivery but adjacent to the previously trained locations. If mice made spatiotemporal inferences, they were expected to interpolate between duration pairs associated with previously reinforced hoppers surrounding the novel hopper. We found that the maximal response rate at the novel locations indeed fell between the two intervals reinforced at the surrounding hoppers. We argue that this pattern of responding might be underlain by spatially constrained Bayesian computations.
Citation - WoS: 4
Citation - Scopus: 5
Monitoring of Intracerebral Hemorrhage With a Linear Microwave Imaging Algorithm
(Springer, 2022) Dilman, Ismail; Dogu, Semih; Bilgin, Egemen; Akinci, Mehmet Nuri; Cosgun, Sema; Çayören, Mehmet; Akduman, Ibrahim
Intracerebral hemorrhage is a life-threatening condition where conventional imaging modalities such as CT and MRI are indispensable in diagnosing. Nevertheless, monitoring the evolution of intracerebral hemorrhage still poses a technological challenge. We consider continuous monitoring of intracerebral hemorrhage in this context and present a differential microwave imaging scheme based on a linearized inverse scattering. Our aim is to reconstruct non-anatomical maps that reveal the volumetric evolution of hemorrhage by using the differences between consecutive electric field measurements. This approach can potentially allow the monitoring of intracerebral hemorrhage in a real-time and cost-effective manner. Here, we devise an indicator function, which reveals the position, volumetric growth, and shrinkage of hemorrhage. Later, the method is numerically tested via a 3D anthropomorphic dielectric head model. Through several simulations performed for different locations of intracerebral hemorrhage, the indicator function-based technique is demonstrated to be capable of detecting the changes accurately. Finally, the robustness under noisy conditions is analyzed to assess the feasibility of the method. This analysis suggests that the method can be used to monitor the evolution of intracerebral hemorrhage in real-world scenarios. Graphical abstract: [Figure not available: see fulltext.]. © 2022, International Federation for Medical and Biological Engineering.
Citation - WoS: 2
Citation - Scopus: 4
Unlocking the Neural Mechanisms of Consumer Loan Evaluations: an Fnirs and Mlbased Consumer Neuroscience Study
(Frontiers Media SA, 2024) Girişken, Yener; Son, Semen; Demircioğlu, Esin Tuna; Filiz, Gözde; Çakar, Tuna; Ertuğrul, Seyit; Sayar, Alperen; Tuna, Esin; Son-Turan, Semen
This 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.
Citation - WoS: 3
Citation - Scopus: 5
Unraveling Neural Pathways of Political Engagement: Bridging Neuromarketing and Political Science for Understanding Voter Behavior and Political Leader Perception
(Frontiers Media SA, 2023) Çakar, Tuna; Filiz, Gözde
Political 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 behavior

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