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Modeling mental peculiarities of a decision maker by a Fourier-holography technique
A.V. Pavlov 1, A.O. Gaugel 1

1 ITMO University, 197101, St. Petersburg, Russia, Kronverkskii av. 49

 PDF, 861 kB

DOI: 10.18287/2412-6179-CO-1189

Pages: 398-406.

Full text of article: Russian language.

Abstract:
A task of modeling individual mental features of a decision-maker using a Fourier holography setup is considered. The problem is considered for a situation when current conditions of decision-making contradict to the previously learned rule of decision-making logic modeled by the non-cooperative game "Prisoner's Dilemma". The approach to the problem is based on a hypothesis of the correlation between mental features and the properties of the neural network as a material carrier of intelligence. The 6f Fourier holography scheme of the resonant architecture is considered as a three-layer neural network implementing a neuro-physiologically motivated concept of the "excitation ring" proposed by A.M. Ivanitsky. We analytically assess the dependence of the validity limits of the classical total probability formula for a disjunction of incompatible events on the characteristics of low-frequency filters in holograms and the correlation radii of the training image of the basic decision rule. Analytical results are confirmed by results of the numerical simulation.

Keywords:
Fourier holography, holographic recording medium, exposure characteristics filtration, correlation, decision making, non-cooperative games, Prisoner’s dilemma, logic with exclusion, cognitive dissonance, mental peculiarities.

Citation:
Pavlov AV, Gaugel AO. Modeling mental peculiarities of a decision maker by a Fourier-holography technique. Computer Optics 2023; 47(3): 398-406. DOI: 10.18287/2412-6179-CO-1189.

References:
  1. Samek W, Montavon G, Lapuschkin S, Anders CJ, Müller K-R. Explaining deep neural networks and beyond: a review of methods and applications. Proc IEEE 2021; 109(3): 247-278. DOI: 10.1109/JPROC.2021.3060483.
  2. Gunning D, Aha DW. DARPA’s explainable artificial intelligence program. AI Magazine 2019; 40(2): 44-58. DOI: 10.1609/aimag.v40i2.2850.
  3. Zhou T, Lin X, Wu J, et al. Large-scale neuromorphic optoelectronic computing with a reconfigurable diffractive processing unit. Nat Photonics 2021: 15: 367-373. DOI: 10.1038/s41566-021-00796-w.
  4. Brunner D, Psaltis D. Competitive photonics neural networks. Nat Photonics 2021: 15: 323-324. DOI: 10.1038/s41566-021-00803-0.
  5. Cavaillès A, Boucher P, Daudet L, Carron I, Gigan S, Müller K. High-fidelity and large-scale reconfigurable photonic processor for NISQ applications. Opt Express 2022; 30(17): 30058-30065. DOI: 10.1364/OE.462071.
  6. Yang Y, Zhou P, Chen T, Huang Y, Li N. Optical neuromorphic computing based on a large-scale laterally coupled laser array. Opt Commun 2022; 521: 128599. DOI: 10.1016/j.optcom.2022.128599.
  7. Kanjana G, Sheeja MK. Secure storage and matching of latent fingerprints using phase shifting digital holography. Pattern Recognit Lett 2022; 153: 113-117. DOI: 10.1016/j.patrec.2021.10.017.
  8. Wang W, Wang X, Xu B, Chen J. Optical image encryption and authentication using phase-only computer-generated hologram. Opt Lasers Eng 2021; 146: 106722. DOI: 10.1016/j.optlaseng.2021.106722.
  9. Pribram K. Languages of the brain; experimental paradoxes and principles in neuropsychology. Englewood Cliffs, NJ: Prentice-Hall; 1971. ISBN: 978-0-13-522730-5.
  10. Sudakov KV. Holographic construction of integrative cerebration. Biol Bull Russ Acad Sci 2012; 39(1): 51-59. DOI: 10.1134/S1062359012010098.
  11. Gavrilov DA. Investigation of the applicability of the convolutional neural network U-Net to a problem of segmentation of aircraft images. Computer Optics 2021; 45(4): 575-579. DOI: 10.18287/2412-6179-CO-804.
  12. Goncharov DS, Petrova EK, Ponomarev NM, Starikov RS, Zlokazov EYu. Implementation features of invariant optical correlator based on amplitude LC SLM. Optical Memory and Neural Networks 2020; 29(2): 110-117. DOI: 10.3103/S1060992X20020022.
  13. Goncharov DS, Petrova EK, Ponomarev NM, Rodin VG, StarikovRS,Trocenko NA, Fazliev TS. Features of the invariant correlation filter application for recognition of color subpixel images. Radiophys Quant El+ 2021; 63(8): 605-611. DOI: 10.1007/s11141-021-10083-x.
  14. Meldo A, Utkin L, Kovalev M, Kasimov E. The natural language explanation algorithms for the lung cancer computer-aided diagnosis system. Artif Intell Med 2020; 108(8): 101952. DOI: 10.1016/j.artmed.2020.101952.
  15. Lyakhov PA, Lyakhova UA. Neural network classification system for pigmented skin neoplasms with preliminary hair removal in photographs. Computer Optics 2021; 45(5): 728-735. DOI: 10.18287/2412-6179-CO-863.
  16. Sludnova AA, Shutko VV, Gaidel AV, Zelter PM, Kapishnikov AV, Nikonorov AV. Identification of pathological changes in the lungs using an analysis of radiological reports and tomographic images. Computer Optics 2021; 45(2): 261-266. DOI: 10.18287/2412-6179-CO-793.
  17. Thanh DNH, Hai NH, Hieu LM, Tiwari P, Prasath VBS. Skin lesion segmentation method for dermoscopic images with convolutional neural networks and semantic segmentation. Computer Optics 2021; 45(1): 122-129. DOI: 10.18287/2412-6179-CO-748.
  18. Fida AD, Gaidel AV, Demin NS, Ilyasov NY, Zamytskiy EA. Automated combination of optical coherence tomography images and fundus images. Computer Optics 2021; 45(5): 721-727. DOI: 10.18287/2412-6179-CO-892.
  19. Amiri SS, Mottahedi S, Lee ER, Hoque S. Peeking inside the black-box: Explainable machine learning applied to household transportation energy consumption. Comput Environ Urban Syst 2021; 88: 101647. DOI: 10.1016/j.compenvurbsys.2021.101647.
  20. Rodiah, Madenda S, Susetianingtias DT, Fitrianingsih, Adlina D, Arianty R. Retinal biometric identification using convolutional neural network. Computer Optics 2021; 45(6): 865-872. DOI: 10.18287/2412-6179-CO-890.
  21. Ganeeva YK, Myasnikov EV. Identifying persons from iris images using neural networks for image segmentation and feature extraction. Computer Optics 2022; 46(2): 308-316. DOI: 10.18287/2412-6179-CO-1023.
  22. Conati C, Barra O, Putnam V, Rieger L. Toward personalized XAI: A case study in intelligent tutoring systems. Artif Intell 2021; 298: 103503. DOI: 10.1016/j.artint.2021.103503.
  23. Strimping P, Vartanova I, Jansson F, Eriksson K. The connection between moral positions and moral arguments drives opinion change. Nat Hum Behav 2019; 3: 922-930. DOI: 10.1038/s41562-019-0647-x.
  24. Dazeley R, Vamplew P, Foale C, Young C, Aryala S, Cruz F. Levels of explainable artificial intelligence for human-aligned conversational explanations. Artif Intell 2021; 299: 103525. DOI: 10.1016/j.artint.2021.103525.
  25. Kliegr T, Bahník Š, Fürnkranz J. A review of possible effects of cognitive biases on interpretation of rule-based machine learning models. Artif Intell 2021; 295: 103458. DOI: 10.1016/j.artint.2021.103458.
  26. Tversky A, Kahneman D. Judgment under Uncertainty: Heuristics and Biases. Science. New Series 1974; 185(4157): 1124-1131.
  27. Korteling JE, Brouwer A-M, Toet A. A neural network framework for cognitive bias. Front Psychol 2018; 9: 1561. doi: 10.3389/fpsyg.2018.0156.
  28. Yamamoto H, Okada I, Taguci T, Muto M. Effect of voluntary participation on an alternating and a simultaneous prisoner’s dilemma. Phys Rev E 2019; 100: 032004. DOI: 10.1103/PhysRevE.100.032304.
  29. Menshikov IS, Shklover AS, Babkina TS, Myagkov MG. From rationality to cooperativeness: The totally mixed Nash equilibrium in Markov strategies in the iterated Prisoner’s Dilemma. PLoS ONE 2017; 12(11): e0180754. DOI: 10.1371/journal.pone.0180754.
  30. Tversky A, Shafir E. The disjunction effect in choice under uncertainty. Psychol Sci 1992; 3(5): 305-309. DOI: 10.1111/j.1467-9280.1992.tb00678.x
  31. Crosson R. The disjunction effect and reason-based choice in games. Organ Behav Hum Decis Process 1999; 80: 118-133. DOI: 10.1006/obhd.1999.2846
  32. Li S, Taplin J. Examining whether there is a disjunction effect in Prisoner’s Dilemma games. China Journal of Psychology 2002; 44: 25-46.
  33. Busemeyer JR, Matthew М, Wang ZA. Quantum game theory explanation of disjunction effects. Proc 28th Annual Conf of the Cognition Science Society 2006: 131-135.
  34. Hristova E, Grinberg M. Disjunction effect in prisoner’s dilemma: evidences from an eye-tracking study. Proc 30th Annual Conf of the Cognition Science Society 2008: 1225-1230.
  35. Pavlov AV. Modeling of quantum-like cognitive phenomena by the Fourier-holography technique under the choice of alternatives. Computer Optics 2021; 45(4): 551-561. DOI: 10.18287/2412-6179-CO-830.
  36. Glezer VD. The role of spatial–Frequency analysis, primitives, and interhemispheric asymmetry in the identification of visual images. Hum Physiol 2000; 26(5): 636-640. DOI: 10.1007/BF02760381.
  37. Glezer VD. Matched filtering in the visual system. J Opt Technol 1999; 66(10): 853-856. DOI: 10.1364/JOT.66.000853.
  38. Ivanitskii AM. Information synthesis in key parts of the cerebral cortex as the basis of subjective. Neurosci Behav Physiol 1997; 27: 414-426. DOI: 10.1007/BF02462943.
  39. Ivanitsky AM, Ivanitsky GA, Sysoeva OV. Brain science: On the way to solving the problem of consciousness. Int J Psychophysiol 2009; 73: 101-108. DOI: 10.1016/j.ijpsycho.2009.02.004.
  40. Pavlov AV. Holographic memory updated by contradicted information: influence of low frequency attenuation on response stability. Computer Optics 2020; 44(5): 728-736. DOI: 10.18287/2412-6179-CO-668.
  41. Pavlov AV. The influence of hologram recording conditions and nonlinearity of recording media on the dynamic characteristics of the Fourier holography scheme with resonance architecture. Opt Spectrosc 2015; 119(1): 146-154. DOI: 10.1134/S0030400X1507022X.
  42. Nogin VD. Decision-making in a multi-criteria environment: a quantitative approach [In Russian]. Moscow: “Fizmatlit” Publisher; 2002.
  43. Nash JF Non-cooperative games. Ann Math 1951; 54(2): 286-295. DOI: 10.2307/1969529.
  44. Feinman RF, Leighton RB, Sands M. The Feinman lectures on physics. Vol 3. London: Addison-Wesley Publishing Company Inc; 1965.
  45. Reiter R. A logic for default reasoning. Artif Intell 1980; 13(1-2): 81-132.
  46. Shubnikov EI. Signal to noise ratio under correlation comparison of images. Opt Spectrosc 1987; 62(2): 268-272.
  47. Pavlov AV, Gaugel AO, Alekseev AM. On the approximation of transfer characteristic and correlation response of the Fourier-holography scheme. Opt Spectrosc 2022; 130(9): 1389-1396. DOI: 10.21883/OS.2022.09.53300.3478-22.
  48. Shoidin SA, Kovalev MS. Spatial photoresponse, formfactor, and requirements to holographic materials. Opt Spectrosc 2020; 128(7): 885-896. DOI: 10.1134/S0030400X20070206.

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