Araştırma Makalesi
BibTex RIS Kaynak Göster

Yapay Zeka ve Bilinç: Anlamsal ve Duygusal/Heyecansal Boyutları Üzerinden Bir Değerlendirme

Yıl 2024, Cilt: 13 Sayı: 4, 192 - 213, 28.10.2024
https://doi.org/10.15869/itobiad.1517371

Öz

Endüstri devrimiyle birlikte makine-insan etkileşimi sibernetik çatısı altında günümüzdeki yapay zeka uygulamalarına kadar uzanan bir süreç dahilinde gittikçe önem kazanmaya başlamış ve bu süreçte psikoloji ve diğer bilişsel bilimler felsefe ve yapay zeka alanlarındaki bilinç meselesi gibi çeşitli tartışmalara, çeşitli bulgular üzerine şekillenen yeni kuramsal açılımlar sağlamıştır. Bu süreçte psikoloji ve diğer bilişsel bilimler de insan bellek sistemi üzerindeki araştırmalarda kodlama, saklama, geri getirme süreçleri ve çalışma belleği gibi doğrudan bilgi işleme sistemimizdeki bir hipotetik merkezi yürütücüye bağlı çalışan bellek modalitelerinin deneysel olarak gösterilmesi gibi hususlarda bilgisayar modellemelerinden yararlanmaya başlamışlardır. Ancak, araştırmaların sonuçları, görece daha mekanik ve duyum-temelli işleyen dikkat süreçlerinden farklı olarak, daha üst düzey kodlama ve işlemlemeler içeren bellek süreçlerinin özellikle saklama ve geri getirme işlemlerinin mantığı ve sistem mimarisi bakımından konvansiyonel bilgisayar teknolojilerinden ayrıştığına işaret edegelmektedirler. Bu bağlamda insan bilgi işleme sisteminin kapasite, esneklik ve yaratıcılık avantajı ile klasik anlamdaki bir bilgisayarın dakikliği veya nesnelliğini biraraya getiren yeni bir teknolojinin elde edilmesine dönük çabalar farklı disiplinlerden araştırmacıların ortak bir hedefi olarak yapay zeka (YZ) kavramının oluşmasına ve olgunlaşmasına zemin teşkil etmiş bulunmaktadır. Bu süreçte bilinç tartışmaları bilincin işlevsel özellikleri üzerinden yeniden tanımlandığı bir yöne doğru evrilirken bu işlevsellik üzerinde düzenleyici bir etken olarak bulunan temel duygusal/heyecansal mekanizmaların rolü, bilinç tartışmalarında geri planda kalma riski taşımaktadır. Mevcut çalışma, yapay zekâ çalışmaları bağlamında, bilincin duygusal temellerini de vurgulayarak gerçek anlamıyla bütünsel olarak bilinçli bir yapay zekanın olamayacağı hakkında genel bir değerlendirme ve tartışma sunmaktadır.

Kaynakça

  • Abioye, S. O., Oyedele, L. O., Akanbi, L., Ajayi, A., Delgado, J. M. D., Bilal, M., ... & Ahmed, A. (2021). Artificial intelligence in the construction industry: A review of present status, opportunities and future challenges. Journal of Building Engineering, 44, 103299.
  • Ahmad, S. F., Han, H., Alam, M. M., Rehmat, M., Irshad, M., Arraño-Muñoz, M., & Ariza-Montes, A. (2023). Impact of artificial intelligence on human loss in decision making, laziness and safety in education. Humanities and Social Sciences Communications, 10(1), 1-14.
  • Aggarwal, N., Saxena, G. J., Singh, S., & Pundir, A. (2023). Can I say, now machines can think?. arXiv preprint arXiv:2307.07526. https://doi.org/10.48550/arxiv.2307.07526
  • Ale, M., Sturdee, M., & Rubegni, E. (2022). A systematic survey on embodied cognition: 11 years of research in child–computer interaction. International Journal of Child-Computer Interaction, 33, 100478.
  • Anderson, M. L. (2003). Embodied cognition: A field guide. Artificial intelligence, 149(1), 91-130.
  • Ayesh, A. (2019). Turing Test Revisited: A Framework for an Alternative. arXiv preprint arXiv:1906.11068. https://doi.org/10.48550/arxiv.1906.11068
  • Baars, B. J. (2005). Global workspace theory of consciousness: toward a cognitive neuroscience of human experience. Progress in brain research, 150, 45-53.
  • Bagheri, E., Esteban, P. G., Cao, H. L., Beir, A. D., Lefeber, D., & Vanderborght, B. (2020). An autonomous cognitive empathy model responsive to users’ facial emotion expressions. ACM Transactions on Interactive Intelligent Systems (TIIS), 10(3), 1-23.
  • Belanche, D., Belk, R. W., Casaló, L. V., & Flavián, C. (2024). The dark side of artificial intelligence in services. The Service Industries Journal, 44(3-4), 149-172.
  • Blaisdell, A. P., Stolyarova, A., & Stahlman, W. D. (2016). The Law of Expect or a Modified Law of Effect?.Conductual, 4(2), 61-90.
  • Cano, S., González, C. S., Gil-Iranzo, R. M., & Albiol-Pérez, S. (2021). Affective communication for socially assistive robots (sars) for children with autism spectrum disorder: A systematic review. Sensors, 21(15), 51- 66. https://doi.org/10.3390/s21155166
  • Chalmers, D. J. (2014). Subsymbolic computation and the Chinese room. In The Symbolic and Connectionist Paradigms (pp. 25-48). Psychology Press.
  • Clark, A. (2012). 14 Embodied, embedded, and extended cognition. The Cambridge handbook of cognitive science, 275-291
  • Coeckelbergh, M. (2019). Artificial Intelligence: Some ethical issues and regulatory challenges. Technology and regulation, 2019, 31-34.
  • Copeland, B. J. (2000). Narrow versus wide mechanism: Including a re-examination of Turing's views on the mind-machine issue. The Journal of Philosophy, 97(1), 5-32.
  • Coronado, E., Kiyokawa, T., Ricardez, G. A. G., Ramirez-Alpizar, I. G., Venture, G., &
  • Dong, Y., Hou, J., Zhang, N., & Zhang, M. (2020). Research on how human intelligence, consciousness, and cognitive computing affect the development of artificial intelligence. Complexity, 2020(1), 1680845.
  • Earl, B. (2014). The biological function of consciousness. Frontiers in psychology, 5(697), 1- 18.https://doi.org/10.3389/fpsyg.2014.00697
  • Enholm, I. M., Papagiannidis, E., Mikalef, P., & Krogstie, J. (2022). Artificial intelligence and business value: A literature review. Information Systems Frontiers, 24(5), 1709-1734.
  • Gamez, D. (2008). Progress in Machine Consciousness. Consiousness and Cognition, 17, 887-910.
  • Gerrig, R. J., Zimbardo, P. G., Campbell, A. J., Cumming, S. R., & Wilkes, F. J. (2015). Psychology and life. Pearson Higher Education AU.
  • Gervasi, R., Barravecchia, F., Mastrogiacomo, L., & Franceschini, F. (2023). Applications of affective computing in human-robot interaction: State-of-art and challenges for manufacturing. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 237(6-7), 815-832.
  • Goni, I. (2020). Machine Learning Algorithms Applied to System Security: A Systematic Review. Asian Journal of Applied Science and Technology, 4(3), 76-81.
  • Gratton, G., Coles, M. G., Sirevaag, E. J., Eriksen, C. W., & Donchin, E. (1988). Pre-and poststimulus activation of response channels: a psychophysiological analysis. Journal of Experimental Psychology: Human perception and performance, 14(3), 331-344
  • Graziano, M. S., & Webb, T. W. (2014). A mechanistic theory of consciousness. International Journal of Machine Consciousness, 6(02), 163-176.
  • Gutnik, L., Hakimzada, A F., Yoskowitz, N A., & Patel, V L. (2006, December 1). The role of emotion in decision- making: A cognitive neuroeconomic approach towards understanding sexual risk behavior. Elsevier BV, 39(6), 720-736. https://doi.org/10.1016/j.jbi.2006.03.002
  • Harkut, D. G., & Kasat, K. (2019). Introductory chapter: artificial intelligence-challenges and applications. Artificial Intelligence-Scope and Limitations. IntechOpen.
  • Hassani, H., Silva, E. S., Unger, S., TajMazinani, M., & Mac Feely, S. (2020). Artificial intelligence (AI) or intelligence augmentation (IA): What is the future?. AI, 1(2),143-155. https://doi.org/10.3390/ai1020008
  • Hoffmann, C. H. (2022). Is AI intelligent? An assessment of artificial intelligence, 70 years after Turing. Technology in Society, 68, 101893.
  • Inui, T. (2006). Experimental approach to embodied cognition. Japanese Psychological Research, 48(3), 123- 125.
  • Jiang, Y., Li, X., Luo, H., Yin, S., & Kaynak, O. (2022). Quo vadis artificial intelligence?. Discover Artificial Intelligence, 2(1), 1-19. https://doi.org/10.1007/s44163-022-00022-8
  • Kadiresan, A., Baweja, Y., & Ogbanufe, O. (2022). Bias in AI-based decision-making. In Bridging Human Intelligence and Artificial Intelligence (pp. 275-285). Cham: Springer International Publishing.
  • Karslı, T. A. (2018). Vygotsky ve Piaget’nin Kuramsal Yaklaşımları Bağlamında İnsan Bilişsel Gelişimi Üzerinde Toplumsallaşma Etkisi Ve Bilişsel Gelişim Sürecinde Aktif Bir Eğitici Yapı Olarak Toplumsallaşma: Eğitim Ve Bilişsel Gelişim Psikolojisindeki Kuramsal Tartışmaların Tarihsel-Toplumsal Perspektifi. Çeşm-i Cihan: Tarih Kültür ve Sanat Araştırmaları Dergisi E-Dergisi, 5(2), 61-70.
  • Karsli, T. A. (2019). Bedenselleşmiş biliş kavramı bağlamında “beden-ötesi biliş”: tarihsel-kültürel psikoloji paradigmasının etkisi. OPUS International Journal of Society Research, 10(17), 2093-2118.
  • Kramer, A., Coles, M., Eriksen, B., Garner, W., Hoffman, J., & Lappin, J. (1994). Charles Eriksen Past, present, and future. Perception & Psychophysics, 55, 1-8.
  • Kulke, L., Feyerabend, D., & Schacht, A. (2020). A comparison of the Affectiva iMotions Facial Expression Analysis Software with EMG for identifying facial expressions of emotion. Frontiers in psychology, 11(329), 1-9. https://doi.org/10.3389/fpsyg.2020.00329
  • Lau, H. (2022). In consciousness we trust: The cognitive neuroscience of subjective experience. Oxford University Press.
  • Lempert, K. M., & Phelps, E. A. (2014). Neuroeconomics of emotion and decision making. Neuroeconomics, 219-236.https://doi.org/10.1016/b978-0-12-416008-8.00012-7
  • Lerner, J S., Li, Y., Valdesolo, P., & Kassam, K S. (2015, January 3). Emotion and Decision Making. Annual Reviews, 66(1), 799-823. https://doi.org/10.1146/annurev-psych-010213-115043
  • Malach, R. (2021). Local neuronal relational structures underlying the contents of human conscious experience. Neuroscience of consciousness, 2021(2), niab028. https://doi.org/10.1093/nc/niab028
  • Mayahi, S., & Vidrih, M. (2022). The impact of generative ai on the future of visual content marketing. arXiv preprint arXiv:2211.12660. https://doi.org/10.48550/arXiv.2211.12660
  • McBride, D. M., Cutting, J. C., & Zimmerman, C. (2022). Cognitive psychology: Theory, process, and methodology. Sage Publications.
  • McCauley, L. (2007). AI armageddon and the three laws of robotics. Ethics and Information Technology, 9, 153-164.
  • McCauley, R. N. (2020). Recent trends in the cognitive science of religion: Neuroscience, religious experience, and the confluence of cognitive and evolutionary research. Zygon:Jorunal of Religion & Science, 55(1), 97- 124.
  • Meneguzzo, P., Tsakiris, M., Schioth, H. B., Stein, D. J., & Brooks, S. J. (2014). Subliminal versus supraliminal stimuli activate neural responses in anterior cingulate cortex, fusiform gyrus and insula: a meta-analysis of fMRI studies. BMC psychology, 2, 1-11. https://doi.org/10.1186/s40359-014-0052-1
  • Mitchell, M. (2021). Why AI is harder than we think. arXiv preprint arXiv:2104.12871. https://doi.org/10.48550/arXiv.2104.12871
  • Oakley, D. A., & Halligan, P. W. (2017). Chasing the rainbow: the non-conscious nature of being. Frontiers in psychology, 8(1924), 1-16. https://doi.org/10.3389/fpsyg.2017.01924
  • Öhman, A. (2021). The orienting response, attention, and learning: An information-processing perspective. In The orienting reflex in humans (pp. 443-471). Routledge.
  • Posner, M. I. (1994). Attention: the mechanisms of consciousness. Proceedings of the National Academy of Sciences, 91(16), 7398-7403.
  • Putchala, S., & Agarwal, N. (2011). Machine vision: an aid in reverse Turing test. AI & society, 26(1), 95-101.
  • Rani, P. (2020). A Comprehensive Survey of Artificial Intelligence (AI): Principles, Techniques, and Applications. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 11(3), 1990-2000.
  • Rigney, J. W. (1978). Learning strategies: A theoretical perspective. Learning strategies, 165-205.
  • Rigney, J. W. (2021). Cognitive learning strategies and dualities in information processing. In Aptitude, learning, and instruction (pp. 315-343). Routledge.
  • Sanchez, G., Hartmann, T., Fuscà, M., Demarchi, G., & Weisz, N. (2020). Decoding across sensory modalities reveals common supramodal signatures of conscious perception. Proceedings of the National Academy of Sciences, 117(13), 7437-7446.
  • Saarimäki, H. (2021). Naturalistic stimuli in affective neuroimaging: A review. Frontiers in human neuroscience, 15, 675068.
  • Schwaninger, A. C. (2022). The Philosophising machine–a specification of the Turing test. Philosophia, 50(3), 1437-1453.
  • Searle, J. R. (2004). Mind: A brief introduction. Oxford university press.
  • Sejnowski, T. J. (2023). Large language models and the reverse turing test. Neural computation, 35(3), 309- 342.
  • Seth, A. K., & Hohwy, J. (2021). Predictive processing as an empirical theory for consciousness science. Cognitive Neuroscience, 12(2), 89-90.
  • Shabbir, J., & Anwer, T. (2018). Artificial intelligence and its role in near future. arXiv preprint arXiv:1804.01396. https://doi.org/10.48550/arxiv.1804.01396
  • Shepherd, J. (2022). Flow and the dynamics of conscious thought. Phenomenology and the Cognitive Sciences, 21(4), 969-988.
  • Sutaria, N. (2022). Bias and ethical concerns in machine learning. ISACA Journal., 4, 1-4.
  • Simic, G., Tkalčić, M., Vukić, V., Mulc, D., Španić, E., Šagud, M., ... & R. Hof, P. (2021). Understanding emotions: origins and roles of the amygdala. Biomolecules, 11(6), 823.
  • Srikanth, K. (2022). Artificial intelligence and human consciousness. Social Science Research Network, 10. http://dx.doi.org/10.2139/ssrn.4070609
  • Stevens, F., & Taber, K. (2021). The neuroscience of empathy and compassion in pro-social behavior. Neuropsychologia, 159, 107925.
  • Strack, F., Martin, L. L., & Stepper, S. (1988). Inhibiting and facilitating conditions of the human smile: a nonobtrusive test of the facial feedback hypothesis. Journal of personality and social psychology, 54(5), 768-777.
  • Tai, M. C. T. (2020). The impact of artificial intelligence on human society and bioethics. Tzu chi medical journal, 32(4), 339-343. https://doi.org/10.4103/tcmj.tcmj_71_20
  • Tononi, G. (2004). An information integration theory of consciousness. BMC neuroscience, 5, 1-22.
  • Turing, A. (1950). Computıng Machinery And Intelligence. Oxford University Press.
  • Van der Maas, H. L., Snoek, L., & Stevenson, C. E. (2021). How much intelligence is there in artificial intelligence? A 2020 update. Intelligence, 87, 101548. https://doi.org/10.1016/j.intell.2021.101548
  • Yan, F., Iliyasu, A. M., & Hirota, K. (2021). Emotion space modelling for social robots. Engineering Applications of Artificial Intelligence, 100, 104178. https://doi.org/10.1016/j.engappai.2021.104178

Artificial Intelligence and Consciousness: An Evaluation on Semantic and Emotional Dimensions

Yıl 2024, Cilt: 13 Sayı: 4, 192 - 213, 28.10.2024
https://doi.org/10.15869/itobiad.1517371

Öz

With the industrial revolution, machine-human interaction has become increasingly important under the umbrella of cybernetics in a process that extends to today's artificial intelligence applications, and in this process, psychology and other cognitive sciences have provided new theoretical expansions shaped on empirical findings to various debates in the fields of philosophy and artificial intelligence, such as the issue of consciousness. In this process, psychology and other cognitive sciences have also begun to make use of computer modeling in research on the human memory system, such as encoding, storage, and retrieval processes, and the experimental demonstration of memory modalities, such as working memory, that directly depends on a hypothetical central executive in our information processing system. However, the results of the research have always pointed out that, unlike relatively more mechanistic and sensation-based attentional processes, memory processes involving higher-level encoding and processing differ from conventional computer technologies, especially in terms of the logic and system architecture of storage and retrieval operations In this context, efforts to achieve a new technology that combines the capacity, flexibility, and creativity advantages of a human information processing system with the punctuality or objectivity of a computer in the classical sense have laid the groundwork for the formation and maturation of the concept of artificial intelligence (AI) as a common goal of researchers from different disciplines. In this process, discussions on consciousness have evolved in a direction where consciousness is redefined in terms of its functional properties. At the same time, the role of basic excitatory mechanisms as a regulating factor on this functionality has the risk of remaining in the background in discussions on consciousness. In the context of artificial intelligence studies, the current study also emphasizes the emotional foundations of consciousness, providing a general assessment and discussion about the inability to be a genuinely conscious AI.

Kaynakça

  • Abioye, S. O., Oyedele, L. O., Akanbi, L., Ajayi, A., Delgado, J. M. D., Bilal, M., ... & Ahmed, A. (2021). Artificial intelligence in the construction industry: A review of present status, opportunities and future challenges. Journal of Building Engineering, 44, 103299.
  • Ahmad, S. F., Han, H., Alam, M. M., Rehmat, M., Irshad, M., Arraño-Muñoz, M., & Ariza-Montes, A. (2023). Impact of artificial intelligence on human loss in decision making, laziness and safety in education. Humanities and Social Sciences Communications, 10(1), 1-14.
  • Aggarwal, N., Saxena, G. J., Singh, S., & Pundir, A. (2023). Can I say, now machines can think?. arXiv preprint arXiv:2307.07526. https://doi.org/10.48550/arxiv.2307.07526
  • Ale, M., Sturdee, M., & Rubegni, E. (2022). A systematic survey on embodied cognition: 11 years of research in child–computer interaction. International Journal of Child-Computer Interaction, 33, 100478.
  • Anderson, M. L. (2003). Embodied cognition: A field guide. Artificial intelligence, 149(1), 91-130.
  • Ayesh, A. (2019). Turing Test Revisited: A Framework for an Alternative. arXiv preprint arXiv:1906.11068. https://doi.org/10.48550/arxiv.1906.11068
  • Baars, B. J. (2005). Global workspace theory of consciousness: toward a cognitive neuroscience of human experience. Progress in brain research, 150, 45-53.
  • Bagheri, E., Esteban, P. G., Cao, H. L., Beir, A. D., Lefeber, D., & Vanderborght, B. (2020). An autonomous cognitive empathy model responsive to users’ facial emotion expressions. ACM Transactions on Interactive Intelligent Systems (TIIS), 10(3), 1-23.
  • Belanche, D., Belk, R. W., Casaló, L. V., & Flavián, C. (2024). The dark side of artificial intelligence in services. The Service Industries Journal, 44(3-4), 149-172.
  • Blaisdell, A. P., Stolyarova, A., & Stahlman, W. D. (2016). The Law of Expect or a Modified Law of Effect?.Conductual, 4(2), 61-90.
  • Cano, S., González, C. S., Gil-Iranzo, R. M., & Albiol-Pérez, S. (2021). Affective communication for socially assistive robots (sars) for children with autism spectrum disorder: A systematic review. Sensors, 21(15), 51- 66. https://doi.org/10.3390/s21155166
  • Chalmers, D. J. (2014). Subsymbolic computation and the Chinese room. In The Symbolic and Connectionist Paradigms (pp. 25-48). Psychology Press.
  • Clark, A. (2012). 14 Embodied, embedded, and extended cognition. The Cambridge handbook of cognitive science, 275-291
  • Coeckelbergh, M. (2019). Artificial Intelligence: Some ethical issues and regulatory challenges. Technology and regulation, 2019, 31-34.
  • Copeland, B. J. (2000). Narrow versus wide mechanism: Including a re-examination of Turing's views on the mind-machine issue. The Journal of Philosophy, 97(1), 5-32.
  • Coronado, E., Kiyokawa, T., Ricardez, G. A. G., Ramirez-Alpizar, I. G., Venture, G., &
  • Dong, Y., Hou, J., Zhang, N., & Zhang, M. (2020). Research on how human intelligence, consciousness, and cognitive computing affect the development of artificial intelligence. Complexity, 2020(1), 1680845.
  • Earl, B. (2014). The biological function of consciousness. Frontiers in psychology, 5(697), 1- 18.https://doi.org/10.3389/fpsyg.2014.00697
  • Enholm, I. M., Papagiannidis, E., Mikalef, P., & Krogstie, J. (2022). Artificial intelligence and business value: A literature review. Information Systems Frontiers, 24(5), 1709-1734.
  • Gamez, D. (2008). Progress in Machine Consciousness. Consiousness and Cognition, 17, 887-910.
  • Gerrig, R. J., Zimbardo, P. G., Campbell, A. J., Cumming, S. R., & Wilkes, F. J. (2015). Psychology and life. Pearson Higher Education AU.
  • Gervasi, R., Barravecchia, F., Mastrogiacomo, L., & Franceschini, F. (2023). Applications of affective computing in human-robot interaction: State-of-art and challenges for manufacturing. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 237(6-7), 815-832.
  • Goni, I. (2020). Machine Learning Algorithms Applied to System Security: A Systematic Review. Asian Journal of Applied Science and Technology, 4(3), 76-81.
  • Gratton, G., Coles, M. G., Sirevaag, E. J., Eriksen, C. W., & Donchin, E. (1988). Pre-and poststimulus activation of response channels: a psychophysiological analysis. Journal of Experimental Psychology: Human perception and performance, 14(3), 331-344
  • Graziano, M. S., & Webb, T. W. (2014). A mechanistic theory of consciousness. International Journal of Machine Consciousness, 6(02), 163-176.
  • Gutnik, L., Hakimzada, A F., Yoskowitz, N A., & Patel, V L. (2006, December 1). The role of emotion in decision- making: A cognitive neuroeconomic approach towards understanding sexual risk behavior. Elsevier BV, 39(6), 720-736. https://doi.org/10.1016/j.jbi.2006.03.002
  • Harkut, D. G., & Kasat, K. (2019). Introductory chapter: artificial intelligence-challenges and applications. Artificial Intelligence-Scope and Limitations. IntechOpen.
  • Hassani, H., Silva, E. S., Unger, S., TajMazinani, M., & Mac Feely, S. (2020). Artificial intelligence (AI) or intelligence augmentation (IA): What is the future?. AI, 1(2),143-155. https://doi.org/10.3390/ai1020008
  • Hoffmann, C. H. (2022). Is AI intelligent? An assessment of artificial intelligence, 70 years after Turing. Technology in Society, 68, 101893.
  • Inui, T. (2006). Experimental approach to embodied cognition. Japanese Psychological Research, 48(3), 123- 125.
  • Jiang, Y., Li, X., Luo, H., Yin, S., & Kaynak, O. (2022). Quo vadis artificial intelligence?. Discover Artificial Intelligence, 2(1), 1-19. https://doi.org/10.1007/s44163-022-00022-8
  • Kadiresan, A., Baweja, Y., & Ogbanufe, O. (2022). Bias in AI-based decision-making. In Bridging Human Intelligence and Artificial Intelligence (pp. 275-285). Cham: Springer International Publishing.
  • Karslı, T. A. (2018). Vygotsky ve Piaget’nin Kuramsal Yaklaşımları Bağlamında İnsan Bilişsel Gelişimi Üzerinde Toplumsallaşma Etkisi Ve Bilişsel Gelişim Sürecinde Aktif Bir Eğitici Yapı Olarak Toplumsallaşma: Eğitim Ve Bilişsel Gelişim Psikolojisindeki Kuramsal Tartışmaların Tarihsel-Toplumsal Perspektifi. Çeşm-i Cihan: Tarih Kültür ve Sanat Araştırmaları Dergisi E-Dergisi, 5(2), 61-70.
  • Karsli, T. A. (2019). Bedenselleşmiş biliş kavramı bağlamında “beden-ötesi biliş”: tarihsel-kültürel psikoloji paradigmasının etkisi. OPUS International Journal of Society Research, 10(17), 2093-2118.
  • Kramer, A., Coles, M., Eriksen, B., Garner, W., Hoffman, J., & Lappin, J. (1994). Charles Eriksen Past, present, and future. Perception & Psychophysics, 55, 1-8.
  • Kulke, L., Feyerabend, D., & Schacht, A. (2020). A comparison of the Affectiva iMotions Facial Expression Analysis Software with EMG for identifying facial expressions of emotion. Frontiers in psychology, 11(329), 1-9. https://doi.org/10.3389/fpsyg.2020.00329
  • Lau, H. (2022). In consciousness we trust: The cognitive neuroscience of subjective experience. Oxford University Press.
  • Lempert, K. M., & Phelps, E. A. (2014). Neuroeconomics of emotion and decision making. Neuroeconomics, 219-236.https://doi.org/10.1016/b978-0-12-416008-8.00012-7
  • Lerner, J S., Li, Y., Valdesolo, P., & Kassam, K S. (2015, January 3). Emotion and Decision Making. Annual Reviews, 66(1), 799-823. https://doi.org/10.1146/annurev-psych-010213-115043
  • Malach, R. (2021). Local neuronal relational structures underlying the contents of human conscious experience. Neuroscience of consciousness, 2021(2), niab028. https://doi.org/10.1093/nc/niab028
  • Mayahi, S., & Vidrih, M. (2022). The impact of generative ai on the future of visual content marketing. arXiv preprint arXiv:2211.12660. https://doi.org/10.48550/arXiv.2211.12660
  • McBride, D. M., Cutting, J. C., & Zimmerman, C. (2022). Cognitive psychology: Theory, process, and methodology. Sage Publications.
  • McCauley, L. (2007). AI armageddon and the three laws of robotics. Ethics and Information Technology, 9, 153-164.
  • McCauley, R. N. (2020). Recent trends in the cognitive science of religion: Neuroscience, religious experience, and the confluence of cognitive and evolutionary research. Zygon:Jorunal of Religion & Science, 55(1), 97- 124.
  • Meneguzzo, P., Tsakiris, M., Schioth, H. B., Stein, D. J., & Brooks, S. J. (2014). Subliminal versus supraliminal stimuli activate neural responses in anterior cingulate cortex, fusiform gyrus and insula: a meta-analysis of fMRI studies. BMC psychology, 2, 1-11. https://doi.org/10.1186/s40359-014-0052-1
  • Mitchell, M. (2021). Why AI is harder than we think. arXiv preprint arXiv:2104.12871. https://doi.org/10.48550/arXiv.2104.12871
  • Oakley, D. A., & Halligan, P. W. (2017). Chasing the rainbow: the non-conscious nature of being. Frontiers in psychology, 8(1924), 1-16. https://doi.org/10.3389/fpsyg.2017.01924
  • Öhman, A. (2021). The orienting response, attention, and learning: An information-processing perspective. In The orienting reflex in humans (pp. 443-471). Routledge.
  • Posner, M. I. (1994). Attention: the mechanisms of consciousness. Proceedings of the National Academy of Sciences, 91(16), 7398-7403.
  • Putchala, S., & Agarwal, N. (2011). Machine vision: an aid in reverse Turing test. AI & society, 26(1), 95-101.
  • Rani, P. (2020). A Comprehensive Survey of Artificial Intelligence (AI): Principles, Techniques, and Applications. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 11(3), 1990-2000.
  • Rigney, J. W. (1978). Learning strategies: A theoretical perspective. Learning strategies, 165-205.
  • Rigney, J. W. (2021). Cognitive learning strategies and dualities in information processing. In Aptitude, learning, and instruction (pp. 315-343). Routledge.
  • Sanchez, G., Hartmann, T., Fuscà, M., Demarchi, G., & Weisz, N. (2020). Decoding across sensory modalities reveals common supramodal signatures of conscious perception. Proceedings of the National Academy of Sciences, 117(13), 7437-7446.
  • Saarimäki, H. (2021). Naturalistic stimuli in affective neuroimaging: A review. Frontiers in human neuroscience, 15, 675068.
  • Schwaninger, A. C. (2022). The Philosophising machine–a specification of the Turing test. Philosophia, 50(3), 1437-1453.
  • Searle, J. R. (2004). Mind: A brief introduction. Oxford university press.
  • Sejnowski, T. J. (2023). Large language models and the reverse turing test. Neural computation, 35(3), 309- 342.
  • Seth, A. K., & Hohwy, J. (2021). Predictive processing as an empirical theory for consciousness science. Cognitive Neuroscience, 12(2), 89-90.
  • Shabbir, J., & Anwer, T. (2018). Artificial intelligence and its role in near future. arXiv preprint arXiv:1804.01396. https://doi.org/10.48550/arxiv.1804.01396
  • Shepherd, J. (2022). Flow and the dynamics of conscious thought. Phenomenology and the Cognitive Sciences, 21(4), 969-988.
  • Sutaria, N. (2022). Bias and ethical concerns in machine learning. ISACA Journal., 4, 1-4.
  • Simic, G., Tkalčić, M., Vukić, V., Mulc, D., Španić, E., Šagud, M., ... & R. Hof, P. (2021). Understanding emotions: origins and roles of the amygdala. Biomolecules, 11(6), 823.
  • Srikanth, K. (2022). Artificial intelligence and human consciousness. Social Science Research Network, 10. http://dx.doi.org/10.2139/ssrn.4070609
  • Stevens, F., & Taber, K. (2021). The neuroscience of empathy and compassion in pro-social behavior. Neuropsychologia, 159, 107925.
  • Strack, F., Martin, L. L., & Stepper, S. (1988). Inhibiting and facilitating conditions of the human smile: a nonobtrusive test of the facial feedback hypothesis. Journal of personality and social psychology, 54(5), 768-777.
  • Tai, M. C. T. (2020). The impact of artificial intelligence on human society and bioethics. Tzu chi medical journal, 32(4), 339-343. https://doi.org/10.4103/tcmj.tcmj_71_20
  • Tononi, G. (2004). An information integration theory of consciousness. BMC neuroscience, 5, 1-22.
  • Turing, A. (1950). Computıng Machinery And Intelligence. Oxford University Press.
  • Van der Maas, H. L., Snoek, L., & Stevenson, C. E. (2021). How much intelligence is there in artificial intelligence? A 2020 update. Intelligence, 87, 101548. https://doi.org/10.1016/j.intell.2021.101548
  • Yan, F., Iliyasu, A. M., & Hirota, K. (2021). Emotion space modelling for social robots. Engineering Applications of Artificial Intelligence, 100, 104178. https://doi.org/10.1016/j.engappai.2021.104178
Toplam 71 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular İletişim Teknolojisi ve Dijital Medya Çalışmaları
Bölüm Makaleler
Yazarlar

Temel Alper Karslı 0000-0002-4837-6213

Yayımlanma Tarihi 28 Ekim 2024
Gönderilme Tarihi 16 Temmuz 2024
Kabul Tarihi 27 Ekim 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 13 Sayı: 4

Kaynak Göster

APA Karslı, T. A. (2024). Yapay Zeka ve Bilinç: Anlamsal ve Duygusal/Heyecansal Boyutları Üzerinden Bir Değerlendirme. İnsan Ve Toplum Bilimleri Araştırmaları Dergisi, 13(4), 192-213. https://doi.org/10.15869/itobiad.1517371
İnsan ve Toplum Bilimleri Araştırmaları Dergisi  Creative Commons Atıf-GayriTicari 4.0 Uluslararası Lisansı (CC BY NC) ile lisanslanmıştır.