1. На главную
  2. Электронные версии
  3. Успехи физиологических наук
  4. T. 54, № 3, 2023

Успехи физиологических наук, 2023, T. 54, № 3, стр. 90-104

Когнитивная архитектура познавательной деятельности при ее моделировании и психофизиологической оценке

О. М. Разумникова *

Новосибирский государственный технический университет
630073 г. Новосибирск, Россия

* E-mail: razoum@mail.ru

Поступила в редакцию 20.12.2022
После доработки 14.01.2023
Принята к публикации 09.03.2023

Аннотация

Описаны основные подходы к моделированию познавательной деятельности человека и нейронных механизмов, лежащих в ее основе. Приведена систематизация когнитивных архитектур и дан обзор таких популярных моделей как ACT-R, SOAR, CLARION и CHREST с примерами их практического применения в психологии и нейрофизиологии. Разработанные модели когнитивных функций позволяют давать прогнозы эффективности восприятия и селекции информации, какие знания и процедуры требуются для оптимального решения задачи, ожидаемую частоту ошибок при выполнении задания и какая функциональная система мозга используется для организации поведения. Совершенствование и дополнение существующих моделей когнитивной архитектуры рассматривается как перспектива развития когнитивной нейронауки, понимания закономерностей формирования естественного интеллекта и разработки искусственного интеллекта.

Ключевые слова: когнитивная архитектура, восприятие, память, принятие решения, эмоциональная регуляция, искусственный интеллект

Список литературы

  1. Бондарко В.М., Данилова М.В., Красильников Н.Н., Леушина Л.И., Невская А.А., Шелепин Ю.Е. Пространственное зрение. Спб: Наука, 1999. 224 с.

  2. Глезер В.Д. Механизмы опознания зрительных образов. Л: Наука, 1966. 203 с.

  3. Глезер В.Д. Зрение и мышление. Спб: Наука, 1993. 248 с.

  4. Красильников Н.Н. Передача, прием и восприятие изображений. М.: Радио и связь, 1986. 246 с.

  5. Малашин Р.О. Принцип наименьшего действия в динамически конфигурируемых системах анализа изображений // Оптический журнал. 2019. Т. 86. № 11. С. 5–13.

  6. Шелепин Ю.Е. Введение в нейроиконику: СПб: Троицкий мост, 2017. 352 с.

  7. Anderson J.R. How can the human mind exist in the physical universe? New York, NY: Oxford University Press. 2007.

  8. Anderson J.R., Bothell D., Byrne M.D., Douglass S., Lebiere C., Qin Y. An integrated theory of the mind // Psychological Review. 2004. V. 111. № 4. P. 1036–1060

  9. Anderson J.R., Farrell R., Sauers R. Learning to program in LISP // Cognitive Science. 1984. V. 8. P. 87–129.

  10. Anderson J.R., Lebiere C. The Atomic Components of Thought. Mahwah, NJ: Erlbaum. 1998.

  11. Anderson J.R., Reder L.M. The fan effect: New results and new theories // J. Experimental Psychology: General. 1999. V. 128. P. 186–197.

  12. Bakaev M., Razumnikova O. What makes a UI Simple? Difficulty and complexity in tasks engaging visual-spatial working memory // Future Internet. 2021. V. 13. № 1. Ar. 21. https://doi.org/10.3390/fi13010021

  13. Borst J.P., Nijboer M., Taatgen N.A., van Rijn H., Anderson J.R. Using data-driven model-brain mappings to constrain formal models of cognition // PLoS One. 2015. V. 10. № 3. Ar. e0119673. https://doi.org/10.1371/journal.pone.0119673

  14. Bourguignon N.J., Braem S., Hartstra E., De Houwer J., Brass M. Encoding of novel verbal instructions for prospective action in the lateral prefrontal cortex: evidence from univariate and multivariate functional magnetic resonance imaging analysis // J. Cogn. Neurosci. 2018. V. 30. P. 1170–1184.

  15. Brass M., Liefooghe B., Braem S., De Houwer J. Following new task instructions: Evidence for a dissociation between knowing and doing // Neurosci. Biobehav. Rev. 2017. V. 81. P. 16–28.

  16. Bullmore E, Sporns O. Complex brain networks: graph theoretical analysis of structural and functional systems // Nat Rev Neurosci. 2009. V. 10. № 3. P. 186–198.

  17. Czerwinski M., Horvitz E., Wilhite S. A diary study of task switching and interruptions // In Proceedings of the Conference on Applications, Technologies, Architectures, and Protocols for Computer Communications. 2004. P. 175–182.

  18. De Groot A.D., Gobet F. Perception and memory in chess: Heuristics of the professional eye. Assen: Van Gorcum. 1996. 346 p.

  19. Demanet J., Liefooghe B., Hartstra E., Wenke D., De Houwer J., Brass M. There is more into ‘doing’ than ‘knowing’: the function of the right inferior frontal sulcus is specific for implementing versus memorising verbal instructions // Neuroimage. 2016. V. 141. P. 350–356.

  20. Duch W., Oentaryo R., Pasquier M. Cognitive Architectures: Where do we go from here? Frontiers in Artificial Intelligence and Applications Conference: Artificial General Intelligence. 2008. V. 171. P. 122–136.

  21. Engelhardt L.E., K. Harden P., Tucker-Drob E.M., Church J.A. The neural architecture of executive functions is established by middle childhood // Neuroimage. 2019. V. 185. P. 479–489.

  22. Everaert T., Theeuwes M., Liefooghe B., De Houwer J. Automatic motor activation by mere instruction // Cogn. Affect. Behav. Neurosci. 2014. V. 14. P. 1300–1309.

  23. Gobet F. Chunking Models of Expertise: Implications for Education // Appl. Cognit. Psychol. 2005. V. 19. P. 183–204.

  24. Gobet F., Lane P.C.R., Croker S., Cheng P.C.H., Jones G., Oliver I., Pine J.M. Chunking mechanisms in human learning // Trends in Cognitive Sciences. 2001. V. 5. P. 236–243.

  25. Gobet F., Lane P.C.R., Lloyd-Kelly M. Chunks, Schemata, and Retrieval Structures: Past and Current // Computational Models Front Psychol. 2015. V. 6. Ar. 1785. https://doi.org/10.3389/fpsyg.2015.01785

  26. Gobet F., Simon H.A. Five seconds or sixty? Presentation time in expert memory // Cognitive Science. 2000. V. 24. № 4. P. 651–682.

  27. González-García C., Formica S., Wisniewski D., Brass M. Frontoparietal action-oriented codes support novel instruction implementation // NeuroImage. 2021. 226. Ar. 117608

  28. González-García C., Formica S., Liefooghe B., Brass M. Attentional prioritization reconfigures novel instructions into action-oriented task sets // Cognition 2020. V. 194. Ar. 104059. https://doi.org/10.1016/j.cognition.2019.104059

  29. González-García C., Mas-Herrero E., de Diego-Balaguer R., Ruz M. Task-specific preparatory neural activations in low-interference contexts // Brain Struct. Funct. 2016. V. 221. № 8. P. 3997–4006.

  30. Hartstra E., Waszak F., Brass M. The implementation of verbal instructions: Dissociating motor preparation from the formation of stimulus–response associations // Neuroimage. 2012. V. 63. P. 1143–1153.

  31. Harrison J. Handbook of practical logic and automated reasoning. Cambridge University Press, 2009. 1305 p.

  32. Jones G., Gobet F., Pine J.M. Linking working memory and long-term memory: a computational model of the learning of new words. Dev Sci. 2007. V. 10. № 6. P. 853–873.

  33. Jones R.M., Laird J.E., Nielsen P.E., Coulter K., Kenny P., Koss F. Automated Intelligent Pilots for Combat Flight Simulation // AI Magazine. 1999. V. 20. № 1. P. 27–42.

  34. Jones G., Gobet F., Pine J.M. Linking working memory and long-term memory: A computational model of the learning of new words // Developmental Science. 2007. V. 10. № 6. P. 853–873.

  35. Jones G., Gobet F., Pine J.M. Computer simulations of developmental change: The contributions of working memory capacity and long-term knowledge // Cognitive Science. 2008. V. 32. № 7. P. 1148–1176.

  36. Katidioti I., Borst J.P., Taatgen N.A. What happens when we switch tasks: Pupil dilation in multitasking // J. Exp. Psychol. Appl. 2014. V. 20. P. 380–396

  37. Kahneman D.A. perspective on judgment and choice: mapping bounded rationality // American psychologist. 2003. V. 58. № 9. P. 697–720.

  38. Kim N.Y., House R., Yun M.H., Nam C.S. Neural Correlates of Workload Transition in Multitasking: An ACT-R Model of Hysteresis Effect // Front Hum Neurosci. 2019. V. 12. Ar. 535.

  39. Kotseruba I., Tsostsos J.K. A review of 40 years of cognitive architecture research: Focus on perception, attention, learning and applications // arXiv preprint: 1610.08602. 2016. https://doi.org/10.48550/arXiv.1610.08602

  40. Lane P.C.R., Gobet F., Smith R.L. Attention Mechanisms in the CHREST Cognitive Architecture. In: Paletta L., Tsotsos J.K. (eds) Attention in Cognitive Systems. WAPCV 2008. Lecture Notes in Computer Science. 2009. V. 5395. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00582-4_14

  41. Lane P.C.R., Gobet F. Perception in chess and beyond: Commentary on Linhares and Freitas (2010)' // New Ideas in Psychology. 2011. V. 29. P. 156–161.

  42. Lebiere C., O’Reilly R., Jilk D., Taatgen N.A., Anderson J.R. The SAL integrated cognitive architecture. In A. Samsonovich (Ed.). Biologically inspired cognitive architectures: Papers from the AAAI 2008 Fall Symposium Menlo Park, CA: AAAI Press. 2009. P. 98–104.

  43. Lehman J.F., Laird J.E., Rosenbloom P.A. Gentle Introduction to Soar, An Architecture for Human Cognition // An Invitation to Cognitive Science. MIT Press. 2006. V. 4.

  44. Li Y., Wang Y., Yu F., Chen A. Large-scale reconfiguration of connectivity patterns among attentional networks during context-dependent adjustment of cognitive control // Hum Brain Mapp. 2021. V. 42. P. 3821–3832.

  45. Malakhova E.Yu. Information representation space in artificial and biological neural networks // J. Optical Technology. V. 87. № 10. P. 598–603.

  46. Malashin R. Principle of least action in dynamically configured image analysis systems // J. Optical Technology. 2019. V. 86. № 11. P. 678–685.

  47. Malashin R.O. Training an improved recurrent attention model using an alternative reward function // J. Optical Technology. 2021. V. 88. № 3. P. 127–130.

  48. Malashin D.O., Malashin R.O. Efficient hardware implementation of neural networks // Neural Networks and Neurotechnologies. Aleksandrov A.A., Alizade M.R., Andreeva G.O., Andreeva I.G., (Eds). St. Petersburg, 2019. P. 187–192.

  49. Martin L., Rosales J.H., Jaime K., Ramos F. Affective episodic memory system for virtual creatures: The first step of emotion-oriented memory // Comput Intell Neurosci. 2021. Ar. 7954140. https://doi.org/10.1155/2021/7954140

  50. Maxwell A., Thomas B.B.T., Yeo and M. D’Esposito. The modular and integrative functional architecture of the human brain // PNAS. 2015. Ar. E6798–E6807. https://doi.org/10.1073/pnas.1510619112

  51. Mill R.D., Ito T., Cole M. W. From connectome to cognition: The search for mechanism in human functional brain networks // Neuroimage. 2017. V. 160. P. 124–139.

  52. Mishkin M., Ungerleider L.G., Macko K.A. Object vision and spatial vision: two cortical pathways // Trends in Neurosci. 1983. V. 6. P. 414–417.

  53. Moreira J.F.G., McLaughlin K.A., Silvers J.A. Characterizing the Network Architecture of Emotion Regulation Neurodevelopment // Cerebral Cortex. 2021. V. 31. P. 4140–4150.

  54. Muhle-Karbe P.S., Duncan J., Baene W.De, Mitchell D.J., Brass M. Neural coding for instruction-based task sets in human frontoparietal and visual cortex // Cereb. Cortex. 2017. V. 27. P. 1891–1905.

  55. Newell A., Simon H. GPS, A Program that Simulates Human Thought. In: Computers and Thought, E.A. Feigenbaum, J. Feldman (Eds.), R. Oldenbourg KG., 1963. P. 109–124.

  56. Neves D.M., Anderson J.R. Knowledge compilation: Mechanisms for the automatization of cognitive skills. In: J. R. Anderson (Ed.) Cognitive skills and their acquisition Hillsdale, NJ: Erlbaum. 1981. P. 57–84.

  57. Oh H., Yun Y., Myung R. Cognitive Modeling of Task Switching in Discretionary Multitasking Based on the ACT-R Cognitive Architecture // Appl. Sci. 2021. V. 11. Ar. 3967. https://doi.org/10.3390/app11093967

  58. Palenciano A.F., González-García C., Arco J.E., Pessoa L., Ruz M. Representational organization of novel task sets during proactive encoding // J. Neurosci. 2019. V. 39. № 42. P. 8386–8397.https://doi.org/10.1523/JNEUROSCI.0725-19.2019

  59. Palenciano A.F., González-García C., Arco J.E., Ruz M. Transient and sustained control mechanisms supporting novel instructed behavior // Cereb. Cortex. 2019. V. 29. P. 3948–3960.

  60. Pearl J. Probabilistic reasoning in intelligent systems: networks of plausible inference. 1988. Elsevier Inc. 152 p.

  61. Prezenski S., Brechmann A., Wolff S., Russwinkel N.A. Cognitive Modeling Approach to Strategy Formation in Dynamic Decision Making // Front Psychol. 2017. V. 8. Ar.1335. https://doi.org/10.3389/fpsyg.2017.01335

  62. Razumnikova O., Bakaev M. Ontology of frequency-spatial organization of brain activity reflecting the cognitive reserves // International Multi-Conference on Engineering, Computer and Information Sciences (SIBIRCON). 2019. P. 0950–0954.

  63. Richards J.M., Gross J.J. Emotion regulation and memory: the cognitive costs of keeping one’s cool // J. Pers Soc. Psychol. 2000. V. 79. № 3. P. 410–424.

  64. Ritter F.E. Tehranchi F., Oury J.D. ACT-R: A cognitive architecture for modeling cognition WIREs // Cogn Sci. 2018. Ar. e1488 https://doi.org/10.1002/wcs.1488

  65. Rumelhart D., McClelland J. Parallel distributed processing: Psychological and biological models. The MIT press. 1986. V. 2. 581 p.

  66. Schultz D.H., Cole M.W. Integrated Brain Network Architecture Supports Cognitive Task Performance // Neuron. 2016. V. 92. P. 278–279.

  67. Shine J.M., Bissett P.G., Bell P.T., Koyejo O., Balsters J.H., Gorgolewski K.J., Moodie C.A., Poldrack R.A. The dynamics of functional brain networks: Integrated network states during cognitive task performance // Neuron. 2016. V. 92. № 2. P. 544–554.

  68. Sun R. Anatomy of the Mind: Exploring Psychological Mechanisms and Processes with the Clarion Cognitive Architecture. Oxford University Press. 2016. 481 p.

  69. Sun R. The importance of cognitive architectures: An analysis based on clarion // J. Experimental & Theoretical Artificial Intelligence. 2007. V. 19. № 2. P. 159–193

  70. Slovic P. Affect, Reason, Risk and Rationality // Notas Económicas. 2018. № 46. P. 1–10.

  71. Strannegård C., von Haugwitz R., Wessberg J., Balkenius C.A. Cognitive Architecture Based on Dual Process Theory. In: Kühnberger K.U., Rudolph S., Wang P. (eds) Artificial General Intelligence. AGI 2013. Lecture Notes in Computer Science. 2013. V. 7999. Springer, Berlin, Heidelberg. Doi:https://doi.org/10.1007/978-3-642-39521-5_15

  72. Turner B.M., Forstmann B.U., Love B.C., Palmeri T.J., Van Maanen L. Approaches to Analysis in Model-based Cognitive Neuroscience // J Math Psychol. 2017. V. 76 (B). P. 65–79.

  73. van Vugt M.K. Cognitive architectures as a tool for investigating the role of oscillatory power and coherence in cognition // Neuroimage. 2014. V. 85. Pt 2. P. 685–693.

  74. Wickens C.D., Gutzwiller R.S., Santamaria A. Discrete task switching in overload: A meta-analyses and a model // Int. J. Hum. Comp. Stud. 2015. V. 79. P. 79–84.

  75. Woolgar A., Afshar S., Williams M.A., Rich A.N. Flexible coding of task rules in frontoparietal cortex: an adaptive system for flexible cognitive control // J. Cogn. Neurosci. 2015. V. 27. P. 1895–1911.

Дополнительные материалы отсутствуют.

Инструменты

предыдущая статья выпуска содержание выпуска

Успехи физиологических наук

Архивы выпусков Информация о журнале Отправить рукопись в журнал