Discussions on Neuroscience of Decision-Making

Authors

  • Yang-yang Hou Jiangsu Second Normal University, People's Republic China

DOI:

https://doi.org/10.59388/pm00357

Abstract

Decision neuroscience focuses on the neural mechanisms of the brain involved in decision-making. Researchers in this field observe brain activity in different decision-making conditions to try to understand the fundamentals of decision-making and use this to build models that explain decision-making behavior. Based on the learning and decision neuroscience theory and its research method principle, this paper discusses the basic principle theory of decision neuroscience, analyzes the conditions and characteristics of perception decision and value decision, and probes into the neural biochemical basis of brain decision, including the brain area structure and the mechanism of neurotransmitter's influence on decision. In addition, the research methods of decision neuroscience are further discussed, and the relationship between decision neuroscience and artificial intelligence is discussed, and the future development of the discipline is prospected.

References

Al-Surmi, A., Bashiri, M., & Koliousis, I. (2022). AI based decision making: combining strategies to improve operational performance. International Journal of Production Research, 60(14), 4464-4486. https://doi.org/10.1080/00207543.2021.1966540

Alam, L., & Mueller, S. (2021). Examining the effect of explanation on satisfaction and trust in AI diagnostic systems. BMC Medical Informatics and Decision Making, 21(1), 178. https://doi.org/10.1186/s12911-021-01542-6

Alsamhi, S. H., Ma, O., Ansari, M. S., & Almalki, F. A. (2019). Survey on Collaborative Smart Drones and Internet of Things for Improving Smartness of Smart Cities. IEEE Access, 7, 128125-128152. https://doi.org/10.1109/ACCESS.2019.2934998

Alsharif, A. H., Salleh, N. Z., Baharun, R., Hashem E, A. R., Mansor, A. A., Ali, J., & Abbas, A. F. (2021). Neuroimaging Techniques in Advertising Research: Main Applications, Development, and Brain Regions and Processes. Sustainability, 13(11).

Amo, R., Matias, S., Yamanaka, A., Tanaka, K. F., Uchida, N., & Watabe-Uchida, M. (2022). A gradual temporal shift of dopamine responses mirrors the progression of temporal difference error in machine learning. Nature Neuroscience, 25(8), 1082-1092. https://doi.org/10.1038/s41593-022-01109-2

Aoi, M. C., Mante, V., & Pillow, J. W. (2020). Prefrontal cortex exhibits multidimensional dynamic encoding during decision-making. Nature Neuroscience, 23(11), 1410-1420. https://doi.org/10.1038/s41593-020-0696-5

Araujo, T., Helberger, N., Kruikemeier, S., & de Vreese, C. H. (2020). In AI we trust? Perceptions about automated decision-making by artificial intelligence. AI & SOCIETY, 35(3), 611-623. https://doi.org/10.1007/s00146-019-00931-w

Ashwood, Z. C., Roy, N. A., Stone, I. R., Urai, A. E., Churchland, A. K., Pouget, A., Pillow, J. W., & The International Brain, L. (2022). Mice alternate between discrete strategies during perceptual decision-making. Nature Neuroscience, 25(2), 201-212. https://doi.org/10.1038/s41593-021-01007-z

Begoli, E., Bhattacharya, T., & Kusnezov, D. (2019). The need for uncertainty quantification in machine-assisted medical decision making. Nature Machine Intelligence, 1(1), 20-23. https://doi.org/10.1038/s42256-018-0004-1

Bellini, P., Nesi, P., & Pantaleo, G. (2022). IoT-Enabled Smart Cities: A Review of Concepts, Frameworks and Key Technologies. Applied Sciences, 12(3).

Bhattacharya, S., Somayaji, S. R. K., Gadekallu, T. R., Alazab, M., & Maddikunta, P. K. R. (2022). A review on deep learning for future smart cities (Vol. 5). https://doi.org/10.1002/itl2.187

Bokhari, S. A. A., & Myeong, S. (2022). Use of Artificial Intelligence in Smart Cities for Smart Decision-Making: A Social Innovation Perspective. Sustainability, 14(2).

Bolam, F. C., Grainger, M. J., Mengersen, K. L., Stewart, G. B., Sutherland, W. J., Runge, M. C., & McGowan, P. J. K. (2019). Using the Value of Information to improve conservation decision making (Vol. 94). https://doi.org/10.1111/brv.12471

Cadario, R., Longoni, C., & Morewedge, C. K. (2021). Understanding, explaining, and utilizing medical artificial intelligence. Nature Human Behaviour, 5(12), 1636-1642. https://doi.org/10.1038/s41562-021-01146-0

Chutia, R. (2021). Ranking of Z-numbers based on value and ambiguity at levels of decision making (Vol. 36). https://doi.org/10.1002/int.22301

Claussmann, L., Revilloud, M., Gruyer, D., & Glaser, S. (2020). A Review of Motion Planning for Highway Autonomous Driving. IEEE Transactions on Intelligent Transportation Systems, 21(5), 1826-1848. https://doi.org/10.1109/TITS.2019.2913998

Collins, A. G. E., & Shenhav, A. (2022). Advances in modeling learning and decision-making in neuroscience. Neuropsychopharmacology, 47(1), 104-118. https://doi.org/10.1038/s41386-021-01126-y

Correia, A. S., Cardoso, A., & Vale, N. (2023). Oxidative Stress in Depression: The Link with the Stress Response, Neuroinflammation, Serotonin, Neurogenesis and Synaptic Plasticity. Antioxidants, 12(2).

Coupé, P., Mansencal, B., Clément, M., Giraud, R., de Senneville, B. D., Ta, V.-T., Lepetit, V., & Manjon, J. V. (2019, 2019//). AssemblyNet: A Novel Deep Decision-Making Process for Whole Brain MRI Segmentation. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019, Cham.

da Silva, A. L. C. d. L., Costa, A. P. C. S., & de Almeida, A. T. (2022). Exploring cognitive aspects of FITradeoff method using neuroscience tools. Annals of Operations Research, 312(2), 1147-1169. https://doi.org/10.1007/s10479-020-03894-0

Fisher, Y. E., Marquis, M., D’Alessandro, I., & Wilson, R. I. (2022). Dopamine promotes head direction plasticity during orienting movements. Nature, 612(7939), 316-322. https://doi.org/10.1038/s41586-022-05485-4

Forde, N. J., Jeyachandra, J., Joseph, M., Jacobs, G. R., Dickie, E., Satterthwaite, T. D., Shinohara, R. T., Ameis, S. H., & Voineskos, A. N. (2020). Sex Differences in Variability of Brain Structure Across the Lifespan. Cerebral Cortex, 30(10), 5420-5430. https://doi.org/10.1093/cercor/bhaa123

Friedman, N. P., & Robbins, T. W. (2022). The role of prefrontal cortex in cognitive control and executive function. Neuropsychopharmacology, 47(1), 72-89. https://doi.org/10.1038/s41386-021-01132-0

Gangopadhyay, P., Chawla, M., Dal Monte, O., & Chang, S. W. C. (2021). Prefrontal–amygdala circuits in social decision-making. Nature Neuroscience, 24(1), 5-18. https://doi.org/10.1038/s41593-020-00738-9

Garritsen, O., van Battum, E. Y., Grossouw, L. M., & Pasterkamp, R. J. (2023). Development, wiring and function of dopamine neuron subtypes. Nature Reviews Neuroscience, 24(3), 134-152. https://doi.org/10.1038/s41583-022-00669-3

González Rodríguez, G., Gonzalez-Cava, J. M., & Méndez Pérez, J. A. (2020). An intelligent decision support system for production planning based on machine learning. Journal of Intelligent Manufacturing, 31(5), 1257-1273. https://doi.org/10.1007/s10845-019-01510-y

Guzel, T., & Mirowska-Guzel, D. (2022). The Role of Serotonin Neurotransmission in Gastrointestinal Tract and Pharmacotherapy. Molecules, 27(5).

Hakak, S., Khan, W. Z., Gilkar, G. A., Imran, M., & Guizani, N. (2020). Securing Smart Cities through Blockchain Technology: Architecture, Requirements, and Challenges. IEEE Network, 34(1), 8-14. https://doi.org/10.1109/MNET.001.1900178

Hampel, L., & Lau, T. (2022). Neurobiological Principles: Neurotransmitters. In P. Riederer, G. Laux, T. Nagatsu, W. Le, & C. Riederer (Eds.), NeuroPsychopharmacotherapy (pp. 3-23). Springer International Publishing. https://doi.org/10.1007/978-3-030-62059-2_365

Haque, A. K. M. B., Bhushan, B., & Dhiman, G. (2022). Conceptualizing smart city applications: Requirements, architecture, security issues, and emerging trends (Vol. 39). https://doi.org/10.1111/exsy.12753

Hareesha, N., Manjunatha, J. G., Pushpanjali, P. A., Subbaiah, N. P., Charithra, M. M., Sreeharsha, N., Asdaq, S. M. B., & Anwer, M. K. (2022). Electrochemical sensing of antibiotic drug amoxicillin in the presence of dopamine at simple and selective carbon paste electrode activated with cetyltrimethylammonium bromide surfactant (Vol. 153). https://doi.org/10.1007/s00706-021-02870-z

Hashmi, M. R., Riaz, M., & Smarandache, F. (2020). m-Polar Neutrosophic Topology with Applications to Multi-criteria Decision-Making in Medical Diagnosis and Clustering Analysis. International Journal of Fuzzy Systems, 22(1), 273-292. https://doi.org/10.1007/s40815-019-00763-2

Hodo, T. W., de Aquino, M. T. P., Shimamoto, A., & Shanker, A. (2020). Critical Neurotransmitters in the Neuroimmune Network (Vol. 11) [Review]. https://doi.org/10.3389/fimmu.2020.01869

Hu, R. K., Zuo, Y., Ly, T., Wang, J., Meera, P., Wu, Y. E., & Hong, W. (2021). An amygdala-to-hypothalamus circuit for social reward. Nature Neuroscience, 24(6), 831-842. https://doi.org/10.1038/s41593-021-00828-2

Jagannathan, S. R., Bareham, C. A., & Bekinschtein, T. A. (2022). Decreasing Alertness Modulates Perceptual Decision-Making (Vol. 42). https://doi.org/10.1523/jneurosci.0182-21.2021

Keith, A. J., & Ahner, D. K. (2021). A survey of decision making and optimization under uncertainty. Annals of Operations Research, 300(2), 319-353. https://doi.org/10.1007/s10479-019-03431-8

Kelly, S. P., Corbett, E. A., & O’Connell, R. G. (2021). Neurocomputational mechanisms of prior-informed perceptual decision-making in humans. Nature Human Behaviour, 5(4), 467-481. https://doi.org/10.1038/s41562-020-00967-9

Kenwood, M. M., Kalin, N. H., & Barbas, H. (2022). The prefrontal cortex, pathological anxiety, and anxiety disorders. Neuropsychopharmacology, 47(1), 260-275. https://doi.org/10.1038/s41386-021-01109-z

Kiran, B. R., Sobh, I., Talpaert, V., Mannion, P., Sallab, A. A. A., Yogamani, S., & Pérez, P. (2022). Deep Reinforcement Learning for Autonomous Driving: A Survey. IEEE Transactions on Intelligent Transportation Systems, 23(6), 4909-4926. https://doi.org/10.1109/TITS.2021.3054625

Knapič, S., Malhi, A., Saluja, R., & Främling, K. (2021). Explainable Artificial Intelligence for Human Decision Support System in the Medical Domain. Machine Learning and Knowledge Extraction, 3(3), 740-770.

Kolk, S. M., & Rakic, P. (2022). Development of prefrontal cortex. Neuropsychopharmacology, 47(1), 41-57. https://doi.org/10.1038/s41386-021-01137-9

Krauss, J. K., Lipsman, N., Aziz, T., Boutet, A., Brown, P., Chang, J. W., Davidson, B., Grill, W. M., Hariz, M. I., Horn, A., Schulder, M., Mammis, A., Tass, P. A., Volkmann, J., & Lozano, A. M. (2021). Technology of deep brain stimulation: current status and future directions. Nature Reviews Neurology, 17(2), 75-87. https://doi.org/10.1038/s41582-020-00426-z

Leichtmann, B., Hinterreiter, A., Humer, C., Streit, M., & Mara, M. Explainable Artificial Intelligence Improves Human Decision-Making: Results from a Mushroom Picking Experiment at a Public Art Festival. International Journal of Human–Computer Interaction, 1-18. https://doi.org/10.1080/10447318.2023.2221605

Lin, Z., Nie, C., Zhang, Y., Chen, Y., & Yang, T. (2020). Evidence accumulation for value computation in the prefrontal cortex during decision making (Vol. 117). https://doi.org/10.1073/pnas.2019077117

Litvaj, I., Ponisciakova, O., Stancekova, D., Svobodova, J., & Mrazik, J. (2022). Decision-Making Procedures and Their Relation to Knowledge Management and Quality Management. Sustainability, 14(1).

Liu, W.-Z., Zhang, W.-H., Zheng, Z.-H., Zou, J.-X., Liu, X.-X., Huang, S.-H., You, W.-J., He, Y., Zhang, J.-Y., Wang, X.-D., & Pan, B.-X. (2020). Identification of a prefrontal cortex-to-amygdala pathway for chronic stress-induced anxiety. Nature Communications, 11(1), 2221. https://doi.org/10.1038/s41467-020-15920-7

Lysaght, T., Lim, H. Y., Xafis, V., & Ngiam, K. Y. (2019). AI-Assisted Decision-making in Healthcare. Asian Bioethics Review, 11(3), 299-314. https://doi.org/10.1007/s41649-019-00096-0

M, O. (2008). Models based on value and probability in health improve shared decision making (Vol. 14). https://d.wanfangdata.com.cn/periodical/ChlQZXJpb2RpY2FsRU5HTmV3UzIwMjMwODMxEiBkMmFmMjZhZmFlMDAwMWZlODQwNzJhYjY5NmJlMjcxMRoIdmExZ3NhZjc=

Maksimenko, V. A., Kuc, A., Frolov, N. S., Khramova, M. V., Pisarchik, A. N., & Hramov, A. E. (2020). Dissociating Cognitive Processes During Ambiguous Information Processing in Perceptual Decision-Making (Vol. 14) [Original Research]. https://doi.org/10.3389/fnbeh.2020.00095

Mattam, U., Talari, N. K., Paripati, A. K., Krishnamoorthy, T., & Sepuri, N. B., V. (2021). Kisspeptin preserves mitochondrial function by inducing mitophagy and autophagy in aging rat brain hippocampus and human neuronal cell line (Vol. 1868). https://doi.org/10.1016/j.bbamcr.2020.118852

Mozaffari, S., Al-Jarrah, O. Y., Dianati, M., Jennings, P., & Mouzakitis, A. (2022). Deep Learning-Based Vehicle Behavior Prediction for Autonomous Driving Applications: A Review. IEEE Transactions on Intelligent Transportation Systems, 23(1), 33-47. https://doi.org/10.1109/TITS.2020.3012034

Muhammad, K., Ullah, A., Lloret, J., Ser, J. D., & Albuquerque, V. H. C. d. (2021). Deep Learning for Safe Autonomous Driving: Current Challenges and Future Directions. IEEE Transactions on Intelligent Transportation Systems, 22(7), 4316-4336. https://doi.org/10.1109/TITS.2020.3032227

Nabeeh, N. A., Smarandache, F., Abdel-Basset, M., El-Ghareeb, H. A., & Aboelfetouh, A. (2019). An Integrated Neutrosophic-TOPSIS Approach and Its Application to Personnel Selection: A New Trend in Brain Processing and Analysis. IEEE Access, 7, 29734-29744. https://doi.org/10.1109/ACCESS.2019.2899841

Niyonambaza, S. D., Kumar, P., Xing, P., Mathault, J., De Koninck, P., Boisselier, E., Boukadoum, M., & Miled, A. (2019). A Review of Neurotransmitters Sensing Methods for Neuro-Engineering Research. Applied Sciences, 9(21).

Prat-Ortega, G., Wimmer, K., Roxin, A., & de la Rocha, J. (2021). Flexible categorization in perceptual decision making. Nature Communications, 12(1), 1283. https://doi.org/10.1038/s41467-021-21501-z

Preti, M. G., & Van De Ville, D. (2019). Decoupling of brain function from structure reveals regional behavioral specialization in humans. Nature Communications, 10(1), 4747. https://doi.org/10.1038/s41467-019-12765-7

Preuss, T. M., & Wise, S. P. (2022). Evolution of prefrontal cortex. Neuropsychopharmacology, 47(1), 3-19. https://doi.org/10.1038/s41386-021-01076-5

Rathee, G., Garg, S., Kaddoum, G., Choi, B. J., Hassan, M. M., & AlQahtani, S. A. (2023). TrustSys: Trusted Decision Making Scheme for Collaborative Artificial Intelligence of Things. IEEE Transactions on Industrial Informatics, 19(1), 1059-1068. https://doi.org/10.1109/TII.2022.3173006

Reinert, S., Hübener, M., Bonhoeffer, T., & Goltstein, P. M. (2021). Mouse prefrontal cortex represents learned rules for categorization. Nature, 593(7859), 411-417. https://doi.org/10.1038/s41586-021-03452-z

Rizo, J. (2022). Molecular Mechanisms Underlying Neurotransmitter Release. Annual Review of Biophysics, 51(1), 377-408. https://doi.org/10.1146/annurev-biophys-111821-104732

Salvan, P., Fonseca, M., Winkler, A. M., Beauchamp, A., Lerch, J. P., & Johansen-Berg, H. (2023). Serotonin regulation of behavior via large-scale neuromodulation of serotonin receptor networks. Nature Neuroscience, 26(1), 53-63. https://doi.org/10.1038/s41593-022-01213-3

Sánchez-Corcuera, R., Nuñez-Marcos, A., Sesma-Solance, J., Bilbao-Jayo, A., Mulero, R., Zulaika, U., Azkune, G., & Almeida, A. (2019). Smart cities survey: Technologies, application domains and challenges for the cities of the future. International Journal of Distributed Sensor Networks, 15(6), 1550147719853984. https://doi.org/10.1177/1550147719853984

Sarawagi, A., Soni, N. D., & Patel, A. B. (2021). Glutamate and GABA Homeostasis and Neurometabolism in Major Depressive Disorder (Vol. 12) [Review]. https://doi.org/10.3389/fpsyt.2021.637863

Serra, D. (2021). Decision-making: from neuroscience to neuroeconomics—an overview. Theory and Decision, 91(1), 1-80. https://doi.org/10.1007/s11238-021-09830-3

Silva, A. L. C. d. L. d., Costa, A. P. C. S., & Almeida, A. T. d. (2022). Exploring cognitive aspects of FITradeoff method using neuroscience tools (Vol. 312). https://doi.org/10.1007/s10479-020-03894-0

Šimić, G., Tkalčić, M., Vukić, V., Mulc, D., Španić, E., Šagud, M., Olucha-Bordonau, F. E., Vukšić, M., & R. Hof, P. (2021). Understanding Emotions: Origins and Roles of the Amygdala. Biomolecules, 11(6).

Smith, E. H., Horga, G., Yates, M. J., Mikell, C. B., Banks, G. P., Pathak, Y. J., Schevon, C. A., McKhann, G. M., Hayden, B. Y., Botvinick, M. M., & Sheth, S. A. (2019). Widespread temporal coding of cognitive control in the human prefrontal cortex. Nature Neuroscience, 22(11), 1883-1891. https://doi.org/10.1038/s41593-019-0494-0

Srinivasan, R. (2023). How the Brain Works: Perspectives on the Future of Human Neuroscience Research. In M. Grimaldi, E. Brattico, & Y. Shtyrov (Eds.), Language Electrified: Principles, Methods, and Future Perspectives of Investigation (pp. 29-41). Springer US. https://doi.org/10.1007/978-1-0716-3263-5_2

Stone, M., Aravopoulou, E., Ekinci, Y., Evans, G., Hobbs, M., Labib, A., Laughlin, P., Machtynger, J., & Machtynger, L. (2020). Artificial intelligence (AI) in strategic marketing decision-making: a research agenda. The Bottom Line, 33(2), 183-200. https://doi.org/10.1108/BL-03-2020-0022

Sun, P., Kretzschmar, H., Dotiwalla, X., Chouard, A., Patnaik, V., Tsui, P., Guo, J., Zhou, Y., Chai, Y., Caine, B., Vasudevan, V., Han, W., Ngiam, J., Zhao, H., Timofeev, A., Ettinger, S., Krivokon, M., Gao, A., Joshi, A., & Zha, Y. (2020). Scalability in Perception for Autonomous Driving: Waymo Open Dataset 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2020), 13-19 June 2020, Seattle, WA, USA, Seattle. https://d.wanfangdata.com.cn/conference/ChZDb25mZXJlbmNlTmV3UzIwMjMwOTAxEiAzMmM5OWJiZWNiZGUyYzE4NzllOTg4ODRmMDJjY2IwOBoIeGVoMzlhMXc=

Sun, Y., Gooch, H., & Sah, P. (2020). Fear conditioning and the basolateral amygdala. F1000Res, 9. https://doi.org/10.12688/f1000research.21201.1

Tang, H., Riley, M. R., Singh, B., Qi, X.-L., Blake, D. T., & Constantinidis, C. (2022). Prefrontal cortical plasticity during learning of cognitive tasks. Nature Communications, 13(1), 90. https://doi.org/10.1038/s41467-021-27695-6

Tavakolian-Ardakani, Z., Hosu, O., Cristea, C., Mazloum-Ardakani, M., & Marrazza, G. (2019). Latest Trends in Electrochemical Sensors for Neurotransmitters: A Review. Sensors, 19(9).

Wang, L., McAlonan, K., Goldstein, S., Gerfen, C. R., & Krauzlis, R. J. (2020). A Causal Role for Mouse Superior Colliculus in Visual Perceptual Decision-Making (Vol. 40). https://doi.org/10.1523/jneurosci.2642-19.2020

Wang, S. (2016). Magnetic resonance brain classification by a novel binary particle swarm optimization with mutation and time-varying acceleration coefficients [Article]. Biomedical Engineering-Biomedizinische Technik, 61(4), 431-441. https://doi.org/10.1515/bmt-2015-0152

Wang, S. (2017a). Detection of Dendritic Spines using Wavelet Packet Entropy and Fuzzy Support Vector Machine. CNS & Neurological Disorders - Drug Targets, 16(2), 116-121.

Wang, S. (2017b). Pathological Brain Detection via Wavelet Packet Tsallis Entropy and Real-Coded Biogeography-based Optimization. Fundamenta Informaticae, 151(1-4), 275-291.

Wang, S. (2023). Grad-CAM: understanding AI models. Computers, Materials & Continua, 76(2), 1321-1324.

Wang, S.-H., & Fernandes, S. (2022). AVNC: Attention-based VGG-style network for COVID-19 diagnosis by CBAM. Ieee Sensors Journal, 22(18), 17431 - 17438. https://doi.org/10.1109/JSEN.2021.3062442

Whyte, C. E. (2022, 31 May-3 June 2022). Machine Expertise in the Loop: Artificial Intelligence Decision-Making Inputs and Cyber Conflict. 2022 14th International Conference on Cyber Conflict: Keep Moving! (CyCon),

Wu, Z., Lin, D., & Li, Y. (2022). Pushing the frontiers: tools for monitoring neurotransmitters and neuromodulators. Nature Reviews Neuroscience, 23(5), 257-274. https://doi.org/10.1038/s41583-022-00577-6

Xiao, Y., Codevilla, F., Gurram, A., Urfalioglu, O., & López, A. M. (2022). Multimodal End-to-End Autonomous Driving. IEEE Transactions on Intelligent Transportation Systems, 23(1), 537-547. https://doi.org/10.1109/TITS.2020.3013234

Yavas, E., Gonzalez, S., & Fanselow, M. S. (2019). Interactions between the hippocampus, prefrontal cortex, and amygdala support complex learning and memory. F1000Res, 8. https://doi.org/10.12688/f1000research.19317.1

Yen, C., Lin, C.-L., & Chiang, M.-C. (2023). Exploring the Frontiers of Neuroimaging: A Review of Recent Advances in Understanding Brain Functioning and Disorders. Life, 13(7).

Yigitcanlar, T., Kankanamge, N., & Vella, K. (2021). How Are Smart City Concepts and Technologies Perceived and Utilized? A Systematic Geo-Twitter Analysis of Smart Cities in Australia (Vol. 28). https://doi.org/10.1080/10630732.2020.1753483

Zhang, M., Zhong, H., Cao, T., Huang, Y., Ji, X., Fan, G.-C., & Peng, T. (2022). Gamma-Aminobutyrate Transaminase Protects against Lipid Overload-Triggered Cardiac Injury in Mice. International Journal of Molecular Sciences, 23(4).

Zhang, Y. (2014a). Binary PSO with mutation operator for feature selection using decision tree applied to spam detection. Knowledge-Based Systems, 64, 22-31. http://www.sciencedirect.com/science/article/pii/S095070511400104X

Zhang, Y. (2014b). Classification of Alzheimer Disease based on structural magnetic resonance imaging by kernel support vector machine decision tree. Progress in Electromagnetics Research, 144, 185-191.

Zhang, Y. (2015). Pathological brain detection in MRI scanning by wavelet packet Tsallis entropy and fuzzy support vector machine. SpringerPlus, 4(1), Article 716. http://www.springerplus.com/content/4/1/716

Zhang, Y. (2016). Comparison of machine learning methods for stationary wavelet entropy-based multiple sclerosis detection: decision tree, k-nearest neighbors, and support vector machine. Simulation, 92(9), 861-871. https://doi.org/10.1177/0037549716666962

Zhang, Y. (2017). Pathological brain detection in MRI scanning via Hu moment invariants and machine learning. Journal of Experimental & Theoretical Artificial Intelligence, 29(2), 299-312. https://doi.org/10.1080/0952813X.2015.1132274

Zhang, Y., Liao, Q. V., & Bellamy, R. K. E. (2020). Effect of confidence and explanation on accuracy and trust calibration in AI-assisted decision making Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, Barcelona, Spain. https://doi.org/10.1145/3351095.3372852

Zhong, S., Ding, W., Sun, L., Lu, Y., Dong, H., Fan, X., Liu, Z., Chen, R., Zhang, S., Ma, Q., Tang, F., Wu, Q., & Wang, X. (2020). Decoding the development of the human hippocampus. Nature, 577(7791), 531-536. https://doi.org/10.1038/s41586-019-1917-5

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2024-02-04

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Hou, Y.- yang. (2024). Discussions on Neuroscience of Decision-Making. Psychomachina, 2. https://doi.org/10.59388/pm00357

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