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Reimagining Tourism Research through Neuroscience and Artificial Intelligence a Dual-Model Perspective

Yıl 2025, Cilt: 6 Sayı: 2, 156 - 173, 18.12.2025
https://doi.org/10.58769/joinssr.1794172

Öz

This study aims to provide a holistic approach to the field by examining the rapidly growing but conceptually fragmented interaction between neuroscience and artificial intelligence in tourism and social sciences literature. In recent years, neuroscientific measurement techniques such as EEG, fMRI, and eye tracking have been widely used in artificial intelligence-supported models to understand tourist behavior and personalize experiences. However, the existing literature is largely limited to a one-way interaction model that positions neuroscience as a data provider and artificial intelligence as a classification and prediction tool. This reductionist approach fails to adequately explain the cultural, social, and emotional dimensions of human behavior. In this study, open-access English articles published between 2020 and 2025 in the Web of Science database were systematically scanned; data obtained from a total of 932 publications were analyzed using Biblioshiny (Bibliometrix) software. Bibliometric analyses revealed annual trends in scientific production, thematic clusters in the conceptual structure, and the temporal evolution of key concepts. The findings show that the field has grown rapidly in recent years but is still in the development stage in terms of theoretical and methodological depth. The bidirectional neuroscience–artificial intelligence model proposed in this study contributes to positioning artificial intelligence not only as a data-processing tool but also as a knowledge producer that feeds neuroscientific theories, going beyond reductionist approaches in the social sciences.

Kaynakça

  • Akhtar, Z. B., & Rozario, V. S. (2025, March). AI Perspectives Within Computational Neuroscience: EEG Integrations and the Human Brain. In Artificial Intelligence and Applications (Vol. 3, No. 2, pp. 145-160).
  • Alvino, L., Pavone, L., Abhishta, A. & Robben, H. (2020). Picking Your Brains: Where and How Neuroscience Tools Can Enhance Marketing Research. Frontiers in Neuroscience, 14. https://doi.org/10.3389/fnins.2020.577666
  • Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. https://doi.org/10.1016/j.joi.2017.08.007
  • Azimi, A. & Afshar, M. (2025). Machine Learning Techniques for Eeg-Based Neuromarketing: A Systematic Literature Review. 2025 11th International Conference on Web Research (ICWR). https://doi.org/10.1109/icwr65219.2025.11006237
  • Bhandari, A. (2020). Neuromarketing Trends and Opportunities for Companies. https://doi.org/10.4018/978-1-7998-3126-6.ch005
  • Bin Wang, Bin Liang, Du, J., Yang, M. & Xu, R. (2022). SEMGraph: Incorporating Sentiment Knowledge and Eye Movement into Graph Model for Sentiment Analysis. Conference on Empirical Methods in Natural Language Processing. https://doi.org/10.18653/v1/2022.emnlp-main.510
  • Bolotta, S., Dumas, G., Bolotta, S. & Dumas, G. (2022). Social Neuro AI: Social Interaction as the “Dark Matter” of AI. Frontiers of Computer Science, 4. https://doi.org/10.3389/fcomp.2022.846440
  • Bösel, J., Mathur, R., Cheng, L., Varelas, M. S., Hobert, M. A., & Suarez, J. I. (2025). AI and Neurology. Neurological research and practice, 7(1), 11.
  • Cai, H., Qu, Z., Li, Z., Zhang, Y., Hu, X. & Bin Hu, (2020). Feature-level fusion approaches based on multimodal EEG data for depression recognition. Information Fusion, 59. https://doi.org/10.1016/j.inffus.2020.01.008
  • Chandra, P., Sharma, H. & Sachan, N. (2025). Explainable and Responsible AI in Neuroscience. https://doi.org/10.1002/9781394302444.ch2
  • Du, D. (2019). Experimental Study on Neural Feedback in Embedded System Teaching Processing Based on ERP Signal Analysis. Int. J. Emerg. Technol. Learn., 14. https://doi.org/10.3991/ijet.v14i12.10715
  • Duan, D., Sun, B., Yang, Q., Zhong, W., Ye, L., Zhang, Q. & Zhang, J. (2023). Gender-Sensitive EEG Channel Selection for Emotion Recognition Using Enhanced Genetic Algorithm. IEEE International Conference on Systems, Man and Cybernetics. https://doi.org/10.1109/smc53992.2023.10393902
  • Farisco, M., Baldassarre, G., Cartoni, E., Leach, A., Petrovici, M. A., Rosemann, A., Salles, A., Stahl, B. & van Albada, S. J. (2023). A method for the ethical analysis of brain-inspired AI. Artificial Intelligence Review, 57. https://doi.org/10.48550/arxiv.2305.10938
  • Fazlul, M., Khondakar, K., Sarowar, M. H., Chowdhury, M., Majumder, S., Hossain, M. A., Ali, M., Dewan, A. & Hossain, Q. D. (2024). A systematic review on EEG-based neuromarketing: recent trends and analyzing techniques. Brain informatics. https://doi.org/10.1186/s40708-024-00229-8
  • Gopinath, N. & Gopinath, N. (2023). Artificial intelligence and neuroscience: An update on fascinating relationships. Process Biochemistry, 125. https://doi.org/10.1016/j.procbio.2022.12.011
  • Guerrero, L. E., Castillo, L. F., Arango-Lopez, J., & Moreira, F. (2025). A systematic review of integrated information theory: a perspective from artificial intelligence and the cognitive sciences. Neural Computing and Applications, 37(11), 7575-7607.
  • Hao, Z., Li, H., Guo, J., & Xu, Y. (2025). Advances in artificial intelligence for olfaction and gustation: A comprehensive review. Artificial Intelligence Review, 58(10), 306.
  • Hassabis, D., Kumaran, D., Summerfield, C. & Botvinick, M. (2017). Neuroscience-Inspired Artificial Intelligence. Neuron, 95. https://doi.org/10.1016/j.neuron.2017.06.011
  • He, Z., Li, J., Ma, W., Zhang, M., Liu, Y. & Ma, S. (2024). Introducing EEG Analyses to Help Personal Music Preference Prediction. arXiv.org. https://doi.org/10.48550/arxiv.2404.15753
  • Hong, M. & Wang, H. (2021). Research on customer opinion summarization using topic mining and deep neural network. Mathematics and Computers in Simulation, 185. https://doi.org/10.1016/j.matcom.2020.12.009 Istace, T. (2025). Legal implications of neurotechnology: rethinking human rights to protect the mind (Doctoral dissertation, University of Antwerp).
  • Ito, T. A., & Kubota, J. T. (2025). Social neuroscience. Understanding Biological Behavior 2nd Edition.
  • Kaushik, P., Gupta, A., Roy, P. & Dogra, D. P. (2019). EEG-Based Age and Gender Prediction Using Deep BLSTM-LSTM Network Model. IEEE Sensors Journal. https://doi.org/10.1109/jsen.2018.2885582
  • Khurana, V., Gahalawat, M., Kumar, P., Roy, P., Dogra, D. P., Scheme, E. & Soleymani, M. (2021). A Survey on Neuromarketing Using EEG Signals. IEEE Transactions on Cognitive and Developmental Systems. https://doi.org/10.1109/tcds.2021.3065200
  • Kumar, K. P., Swarubini, P. J., & Ganapathy, N. (2025b). Cognitive Artificial Intelligence. In Artificial Intelligence and Biological Sciences (pp. 301-323). CRC Press.
  • Kumar, M., Khan, L. & Chang, H. (2025a). Evolving techniques in sentiment analysis: a comprehensive review. PeerJ Computer Science. https://doi.org/10.7717/peerj-cs.2592
  • Kumar, S., Gahalawat, M., Roy, P., Dogra, D. P. & Kim, B. (2020). Exploring Impact of Age and Gender on Sentiment Analysis Using Machine Learning. Electronics. https://doi.org/10.3390/electronics9020374
  • Kumar, S., Yadava, M. & Roy, P. P. (2019). Fusion of EEG response and sentiment analysis of products review to predict customer satisfaction. Information Fusion, 52. https://doi.org/10.1016/j.inffus.2018.11.001
  • Malik, M. R., Abdullah, H. O., & Ikram, M. B. (2025). Artificial Intelligence in Witness Credibility Assessment: The Role of Biometrics, Voice Analytics and Machine Learning in Judicial Cross-Examination. Journal of Asian Development Studies, 14(3), 379-396.
  • Onciul, R., Tataru, C., Dumitru, A., Crivoi, C., Serban, M., Covache-Busuioc, R., Radoi, M. & Toader, C. (2025). Artificial Intelligence and Neuroscience: Transformative Synergies in Brain Research and Clinical Applications. Journal of Clinical Medicine. https://doi.org/10.3390/jcm14020550
  • Pei, G. & Li, T. (2021). A Literature Review of EEG-Based Affective Computing in Marketing. Frontiers in Psychology. https://doi.org/10.3389/fpsyg.2021.602843
  • Pelosi, D., Cacciagrano, D., & Piangerelli, M. (2025). Explainability and Interpretability in Concept and Data Drift: A Systematic Literature Review. Algorithms, 18(7), 443.
  • P'erez, M. Q., Beltr'an, E. T. M., Bernal, S. L., Prat, E. H., Del Campo, L. M., Maim'o, L. F. & Celdr'an, A. H. (2022). Data fusion in neuromarketing: Multimodal analysis of biosignals, lifecycle stages, current advances, datasets, trends, and challenges. Information Fusion. https://doi.org/10.1016/j.inffus.2024.102231
  • Pillalamarri, R. & Udhaya Kumar, S. (2025). A review on EEG-based multimodal learning for emotion recognition. Artificial Intelligence Review, 58(5). https://doi.org/10.1007/s10462-025-11126-9
  • Prabha, C. (2025). Brain Networks in Neuroscience: Tailoring Treatments with AI Insights. In Brain Networks in Neuroscience: Personalization Unveiled Via Artificial Intelligence (pp. 97-112). River Publishers.
  • Quinn, C. (2025). AI Utilization in Neurology. In Generative AI for the Medical Student: Core Concepts to Clinical Practice (pp. 145-177). Cham: Springer Nature Switzerland.
  • Ribeiro, A. J., Ruggiero, R. N., & Padovan-Neto, F. E. (2025). Previous neuroscience exposure predicts self-efficacy among undergraduate students. Trends in Neuroscience and Education, 100251.
  • Saban, S. & Dağdevir, E. (2023). Biomedical signal processing methods for neuromarketing: A comparative study. International Conference on Applied Engineering and Natural Sciences. https://doi.org/10.59287/icaens.1108
  • Saleem, S. A. M. & Naseem, S. M. B. (2023). A Case Study of MyntraTM Enhancing E-Commerce Retailing with Multiple AI Solutions. International Conference on Awareness Science and Technology. https://doi.org/10.1109/icast59062.2023.10454917
  • Savage, N. (2019). How AI and neuroscience drive each other forwards. Nature, 571. https://doi.org/10.1038/d41586-019-02212-4
  • Savic, M. (2024). Artificial Companions, Real Connections? M/C Journal, 27(6). https://doi.org/10.5204/mcj.3111
  • Sola, H. M., Qureshi, F. H., & Khawaja, S. (2025). AI and Eye Tracking Reveal Design Elements’ Impact on E-Magazine Reader Engagement. Education sciences, 15(2), 203.
  • Surianarayanan, C., Lawrence, J. J., Chelliah, P., Prakash, E. & Hewage, C. (2023). Convergence of Artificial Intelligence and Neuroscience towards the Diagnosis of Neurological Disorders—A Scoping Review. Italian National Conference on Sensors. https://doi.org/10.3390/s23063062
  • Theotokis, P. (2025). Human Brain Inspired Artificial Intelligence Neural Networks. Journal of Integrative Neuroscience, 24(4). https://doi.org/10.31083/jin26684
  • Uden, L. & Guan, S. (2022). Neuroscience and Artificial Intelligence. https://doi.org/10.4018/978-1-7998-8686-0.ch009
  • Ullman, S. (2019). Using neuroscience to develop artificial intelligence. Science. https://doi.org/10.1126/science.aau6595
  • Usman, S. M., Khalid, S., Tanveer, A., Imran, A. S., & Zubair, M. (2025). Multimodal consumer choice prediction using EEG signals and eye tracking. Frontiers in Computational Neuroscience, 18, 1516440.
  • Wang, N., Li, Z., Di Shi, Chen, P. & Ren, X. (2024). Understanding emotional values of bionic features for educational service robots: A cross-age examination using multi-modal data. Advanced Engineering Informatics. https://doi.org/10.1016/j.aei.2024.102956
  • Wankhade, M., Rao, A. C. S. & Kulkarni, C. (2022). A survey on sentiment analysis methods, applications, and challenges. Artificial Intelligence Review, 55. https://doi.org/10.1007/s10462-022-10144-1
  • Zhang, J., Yin, Z., Chen, P. & Nichele, S. (2020). Emotion recognition using multi-modal data and machine learning techniques: A tutorial and review. Information Fusion. https://doi.org/10.1016/j.inffus.2020.01.011
  • Zhu, S., Qi, J., Hu, J. H. & Hao, S. (2022). A new approach for product evaluation based on integration of EEG and eye-tracking. Advanced Engineering Informatics, 52. https://doi.org/10.1016/j.aei.2022.101601.t-Damm, K. L., & Kulik, J. A. (2005). Volunteer support, marital status, and the survival times of terminally ill patients. Health Psychology, 24(2), 225–229.

Nörobilim ve Yapay Zeka Aracılığıyla Turizm Araştırmalarının Yeniden Tasarlanması: Çift Modelli Bir Perspektif

Yıl 2025, Cilt: 6 Sayı: 2, 156 - 173, 18.12.2025
https://doi.org/10.58769/joinssr.1794172

Öz

Bu çalışma, turizm ve sosyal bilimler literatüründe hızla artan ancak kavramsal çerçevesi dağınık olan nörobilim ve yapay zekâ etkileşimini inceleyerek alana bütüncül bir yaklaşım kazandırmayı amaçlamaktadır. Son yıllarda EEG, fMRI ve göz izleme gibi nörobilimsel ölçüm teknikleri, turist davranışlarını anlamak ve deneyimleri kişiselleştirmek amacıyla yapay zekâ destekli modellerde yaygın biçimde kullanılmaktadır. Ancak mevcut literatür, çoğunlukla nörobilimi veri sağlayıcı, yapay zekâyı ise sınıflandırma ve tahmin aracı olarak konumlandıran tek yönlü bir etkileşim modeli ile sınırlıdır. Bu indirgemeci yaklaşım, insan davranışının kültürel, sosyal ve duygusal boyutlarını yeterince açıklayamamaktadır. Araştırmada, Web of Science veri tabanında 2020–2025 yılları arasında yayımlanmış açık erişim İngilizce makaleler sistematik biçimde taranmış; toplam 932 yayından elde edilen veriler Biblioshiny (Bibliometrix) yazılımı ile analiz edilmiştir. Bibliyometrik analizler yıllık bilimsel üretim eğilimlerini, kavramsal yapıdaki tematik kümelenmeleri ve anahtar kavramların zamansal evrimini ortaya koymuştur. Bulgular, alanın son yıllarda hızlı büyüdüğünü ancak teorik ve metodolojik derinlik açısından hâlen gelişme aşamasında olduğunu göstermektedir. Bu çalışmada önerilen çift yönlü nörobilim–yapay zekâ modeli, sosyal bilimlerde indirgemeci yaklaşımların ötesine geçerek, yapay zekânın yalnızca veri işleyen bir araç değil, aynı zamanda nörobilimsel kuramları besleyen bir bilgi üreticisi olarak konumlanmasına katkı sağlamaktadır.

Kaynakça

  • Akhtar, Z. B., & Rozario, V. S. (2025, March). AI Perspectives Within Computational Neuroscience: EEG Integrations and the Human Brain. In Artificial Intelligence and Applications (Vol. 3, No. 2, pp. 145-160).
  • Alvino, L., Pavone, L., Abhishta, A. & Robben, H. (2020). Picking Your Brains: Where and How Neuroscience Tools Can Enhance Marketing Research. Frontiers in Neuroscience, 14. https://doi.org/10.3389/fnins.2020.577666
  • Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. https://doi.org/10.1016/j.joi.2017.08.007
  • Azimi, A. & Afshar, M. (2025). Machine Learning Techniques for Eeg-Based Neuromarketing: A Systematic Literature Review. 2025 11th International Conference on Web Research (ICWR). https://doi.org/10.1109/icwr65219.2025.11006237
  • Bhandari, A. (2020). Neuromarketing Trends and Opportunities for Companies. https://doi.org/10.4018/978-1-7998-3126-6.ch005
  • Bin Wang, Bin Liang, Du, J., Yang, M. & Xu, R. (2022). SEMGraph: Incorporating Sentiment Knowledge and Eye Movement into Graph Model for Sentiment Analysis. Conference on Empirical Methods in Natural Language Processing. https://doi.org/10.18653/v1/2022.emnlp-main.510
  • Bolotta, S., Dumas, G., Bolotta, S. & Dumas, G. (2022). Social Neuro AI: Social Interaction as the “Dark Matter” of AI. Frontiers of Computer Science, 4. https://doi.org/10.3389/fcomp.2022.846440
  • Bösel, J., Mathur, R., Cheng, L., Varelas, M. S., Hobert, M. A., & Suarez, J. I. (2025). AI and Neurology. Neurological research and practice, 7(1), 11.
  • Cai, H., Qu, Z., Li, Z., Zhang, Y., Hu, X. & Bin Hu, (2020). Feature-level fusion approaches based on multimodal EEG data for depression recognition. Information Fusion, 59. https://doi.org/10.1016/j.inffus.2020.01.008
  • Chandra, P., Sharma, H. & Sachan, N. (2025). Explainable and Responsible AI in Neuroscience. https://doi.org/10.1002/9781394302444.ch2
  • Du, D. (2019). Experimental Study on Neural Feedback in Embedded System Teaching Processing Based on ERP Signal Analysis. Int. J. Emerg. Technol. Learn., 14. https://doi.org/10.3991/ijet.v14i12.10715
  • Duan, D., Sun, B., Yang, Q., Zhong, W., Ye, L., Zhang, Q. & Zhang, J. (2023). Gender-Sensitive EEG Channel Selection for Emotion Recognition Using Enhanced Genetic Algorithm. IEEE International Conference on Systems, Man and Cybernetics. https://doi.org/10.1109/smc53992.2023.10393902
  • Farisco, M., Baldassarre, G., Cartoni, E., Leach, A., Petrovici, M. A., Rosemann, A., Salles, A., Stahl, B. & van Albada, S. J. (2023). A method for the ethical analysis of brain-inspired AI. Artificial Intelligence Review, 57. https://doi.org/10.48550/arxiv.2305.10938
  • Fazlul, M., Khondakar, K., Sarowar, M. H., Chowdhury, M., Majumder, S., Hossain, M. A., Ali, M., Dewan, A. & Hossain, Q. D. (2024). A systematic review on EEG-based neuromarketing: recent trends and analyzing techniques. Brain informatics. https://doi.org/10.1186/s40708-024-00229-8
  • Gopinath, N. & Gopinath, N. (2023). Artificial intelligence and neuroscience: An update on fascinating relationships. Process Biochemistry, 125. https://doi.org/10.1016/j.procbio.2022.12.011
  • Guerrero, L. E., Castillo, L. F., Arango-Lopez, J., & Moreira, F. (2025). A systematic review of integrated information theory: a perspective from artificial intelligence and the cognitive sciences. Neural Computing and Applications, 37(11), 7575-7607.
  • Hao, Z., Li, H., Guo, J., & Xu, Y. (2025). Advances in artificial intelligence for olfaction and gustation: A comprehensive review. Artificial Intelligence Review, 58(10), 306.
  • Hassabis, D., Kumaran, D., Summerfield, C. & Botvinick, M. (2017). Neuroscience-Inspired Artificial Intelligence. Neuron, 95. https://doi.org/10.1016/j.neuron.2017.06.011
  • He, Z., Li, J., Ma, W., Zhang, M., Liu, Y. & Ma, S. (2024). Introducing EEG Analyses to Help Personal Music Preference Prediction. arXiv.org. https://doi.org/10.48550/arxiv.2404.15753
  • Hong, M. & Wang, H. (2021). Research on customer opinion summarization using topic mining and deep neural network. Mathematics and Computers in Simulation, 185. https://doi.org/10.1016/j.matcom.2020.12.009 Istace, T. (2025). Legal implications of neurotechnology: rethinking human rights to protect the mind (Doctoral dissertation, University of Antwerp).
  • Ito, T. A., & Kubota, J. T. (2025). Social neuroscience. Understanding Biological Behavior 2nd Edition.
  • Kaushik, P., Gupta, A., Roy, P. & Dogra, D. P. (2019). EEG-Based Age and Gender Prediction Using Deep BLSTM-LSTM Network Model. IEEE Sensors Journal. https://doi.org/10.1109/jsen.2018.2885582
  • Khurana, V., Gahalawat, M., Kumar, P., Roy, P., Dogra, D. P., Scheme, E. & Soleymani, M. (2021). A Survey on Neuromarketing Using EEG Signals. IEEE Transactions on Cognitive and Developmental Systems. https://doi.org/10.1109/tcds.2021.3065200
  • Kumar, K. P., Swarubini, P. J., & Ganapathy, N. (2025b). Cognitive Artificial Intelligence. In Artificial Intelligence and Biological Sciences (pp. 301-323). CRC Press.
  • Kumar, M., Khan, L. & Chang, H. (2025a). Evolving techniques in sentiment analysis: a comprehensive review. PeerJ Computer Science. https://doi.org/10.7717/peerj-cs.2592
  • Kumar, S., Gahalawat, M., Roy, P., Dogra, D. P. & Kim, B. (2020). Exploring Impact of Age and Gender on Sentiment Analysis Using Machine Learning. Electronics. https://doi.org/10.3390/electronics9020374
  • Kumar, S., Yadava, M. & Roy, P. P. (2019). Fusion of EEG response and sentiment analysis of products review to predict customer satisfaction. Information Fusion, 52. https://doi.org/10.1016/j.inffus.2018.11.001
  • Malik, M. R., Abdullah, H. O., & Ikram, M. B. (2025). Artificial Intelligence in Witness Credibility Assessment: The Role of Biometrics, Voice Analytics and Machine Learning in Judicial Cross-Examination. Journal of Asian Development Studies, 14(3), 379-396.
  • Onciul, R., Tataru, C., Dumitru, A., Crivoi, C., Serban, M., Covache-Busuioc, R., Radoi, M. & Toader, C. (2025). Artificial Intelligence and Neuroscience: Transformative Synergies in Brain Research and Clinical Applications. Journal of Clinical Medicine. https://doi.org/10.3390/jcm14020550
  • Pei, G. & Li, T. (2021). A Literature Review of EEG-Based Affective Computing in Marketing. Frontiers in Psychology. https://doi.org/10.3389/fpsyg.2021.602843
  • Pelosi, D., Cacciagrano, D., & Piangerelli, M. (2025). Explainability and Interpretability in Concept and Data Drift: A Systematic Literature Review. Algorithms, 18(7), 443.
  • P'erez, M. Q., Beltr'an, E. T. M., Bernal, S. L., Prat, E. H., Del Campo, L. M., Maim'o, L. F. & Celdr'an, A. H. (2022). Data fusion in neuromarketing: Multimodal analysis of biosignals, lifecycle stages, current advances, datasets, trends, and challenges. Information Fusion. https://doi.org/10.1016/j.inffus.2024.102231
  • Pillalamarri, R. & Udhaya Kumar, S. (2025). A review on EEG-based multimodal learning for emotion recognition. Artificial Intelligence Review, 58(5). https://doi.org/10.1007/s10462-025-11126-9
  • Prabha, C. (2025). Brain Networks in Neuroscience: Tailoring Treatments with AI Insights. In Brain Networks in Neuroscience: Personalization Unveiled Via Artificial Intelligence (pp. 97-112). River Publishers.
  • Quinn, C. (2025). AI Utilization in Neurology. In Generative AI for the Medical Student: Core Concepts to Clinical Practice (pp. 145-177). Cham: Springer Nature Switzerland.
  • Ribeiro, A. J., Ruggiero, R. N., & Padovan-Neto, F. E. (2025). Previous neuroscience exposure predicts self-efficacy among undergraduate students. Trends in Neuroscience and Education, 100251.
  • Saban, S. & Dağdevir, E. (2023). Biomedical signal processing methods for neuromarketing: A comparative study. International Conference on Applied Engineering and Natural Sciences. https://doi.org/10.59287/icaens.1108
  • Saleem, S. A. M. & Naseem, S. M. B. (2023). A Case Study of MyntraTM Enhancing E-Commerce Retailing with Multiple AI Solutions. International Conference on Awareness Science and Technology. https://doi.org/10.1109/icast59062.2023.10454917
  • Savage, N. (2019). How AI and neuroscience drive each other forwards. Nature, 571. https://doi.org/10.1038/d41586-019-02212-4
  • Savic, M. (2024). Artificial Companions, Real Connections? M/C Journal, 27(6). https://doi.org/10.5204/mcj.3111
  • Sola, H. M., Qureshi, F. H., & Khawaja, S. (2025). AI and Eye Tracking Reveal Design Elements’ Impact on E-Magazine Reader Engagement. Education sciences, 15(2), 203.
  • Surianarayanan, C., Lawrence, J. J., Chelliah, P., Prakash, E. & Hewage, C. (2023). Convergence of Artificial Intelligence and Neuroscience towards the Diagnosis of Neurological Disorders—A Scoping Review. Italian National Conference on Sensors. https://doi.org/10.3390/s23063062
  • Theotokis, P. (2025). Human Brain Inspired Artificial Intelligence Neural Networks. Journal of Integrative Neuroscience, 24(4). https://doi.org/10.31083/jin26684
  • Uden, L. & Guan, S. (2022). Neuroscience and Artificial Intelligence. https://doi.org/10.4018/978-1-7998-8686-0.ch009
  • Ullman, S. (2019). Using neuroscience to develop artificial intelligence. Science. https://doi.org/10.1126/science.aau6595
  • Usman, S. M., Khalid, S., Tanveer, A., Imran, A. S., & Zubair, M. (2025). Multimodal consumer choice prediction using EEG signals and eye tracking. Frontiers in Computational Neuroscience, 18, 1516440.
  • Wang, N., Li, Z., Di Shi, Chen, P. & Ren, X. (2024). Understanding emotional values of bionic features for educational service robots: A cross-age examination using multi-modal data. Advanced Engineering Informatics. https://doi.org/10.1016/j.aei.2024.102956
  • Wankhade, M., Rao, A. C. S. & Kulkarni, C. (2022). A survey on sentiment analysis methods, applications, and challenges. Artificial Intelligence Review, 55. https://doi.org/10.1007/s10462-022-10144-1
  • Zhang, J., Yin, Z., Chen, P. & Nichele, S. (2020). Emotion recognition using multi-modal data and machine learning techniques: A tutorial and review. Information Fusion. https://doi.org/10.1016/j.inffus.2020.01.011
  • Zhu, S., Qi, J., Hu, J. H. & Hao, S. (2022). A new approach for product evaluation based on integration of EEG and eye-tracking. Advanced Engineering Informatics, 52. https://doi.org/10.1016/j.aei.2022.101601.t-Damm, K. L., & Kulik, J. A. (2005). Volunteer support, marital status, and the survival times of terminally ill patients. Health Psychology, 24(2), 225–229.
Toplam 50 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular İnsan Bilgisayar Etkileşimi, Yapay Zeka (Diğer)
Bölüm Derleme
Yazarlar

Büşra Kaya 0000-0002-6133-7518

Gönderilme Tarihi 30 Eylül 2025
Kabul Tarihi 30 Ekim 2025
Yayımlanma Tarihi 18 Aralık 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 6 Sayı: 2

Kaynak Göster

APA Kaya, B. (2025). Reimagining Tourism Research through Neuroscience and Artificial Intelligence a Dual-Model Perspective. Journal of Smart Systems Research, 6(2), 156-173. https://doi.org/10.58769/joinssr.1794172
AMA Kaya B. Reimagining Tourism Research through Neuroscience and Artificial Intelligence a Dual-Model Perspective. JoinSSR. Aralık 2025;6(2):156-173. doi:10.58769/joinssr.1794172
Chicago Kaya, Büşra. “Reimagining Tourism Research through Neuroscience and Artificial Intelligence a Dual-Model Perspective”. Journal of Smart Systems Research 6, sy. 2 (Aralık 2025): 156-73. https://doi.org/10.58769/joinssr.1794172.
EndNote Kaya B (01 Aralık 2025) Reimagining Tourism Research through Neuroscience and Artificial Intelligence a Dual-Model Perspective. Journal of Smart Systems Research 6 2 156–173.
IEEE B. Kaya, “Reimagining Tourism Research through Neuroscience and Artificial Intelligence a Dual-Model Perspective”, JoinSSR, c. 6, sy. 2, ss. 156–173, 2025, doi: 10.58769/joinssr.1794172.
ISNAD Kaya, Büşra. “Reimagining Tourism Research through Neuroscience and Artificial Intelligence a Dual-Model Perspective”. Journal of Smart Systems Research 6/2 (Aralık2025), 156-173. https://doi.org/10.58769/joinssr.1794172.
JAMA Kaya B. Reimagining Tourism Research through Neuroscience and Artificial Intelligence a Dual-Model Perspective. JoinSSR. 2025;6:156–173.
MLA Kaya, Büşra. “Reimagining Tourism Research through Neuroscience and Artificial Intelligence a Dual-Model Perspective”. Journal of Smart Systems Research, c. 6, sy. 2, 2025, ss. 156-73, doi:10.58769/joinssr.1794172.
Vancouver Kaya B. Reimagining Tourism Research through Neuroscience and Artificial Intelligence a Dual-Model Perspective. JoinSSR. 2025;6(2):156-73.