ISSN 0869-6632 (Print)
ISSN 2542-1905 (Online)

For citation:

Kuc A. К., Maksimenko V. A., Hramov A. E. Influence of «sensory prehistory» on the ambiguous stimuli processing in the human brain. Izvestiya VUZ. Applied Nonlinear Dynamics, 2022, vol. 30, iss. 1, pp. 57-75. DOI: 10.18500/0869-6632-2022-30-1-57-75

This is an open access article distributed under the terms of Creative Commons Attribution 4.0 International License (CC-BY 4.0).
Full text PDF(Ru):
(downloads: 741)
Article type: 

Influence of «sensory prehistory» on the ambiguous stimuli processing in the human brain

Kuc Alexander Константинович, Immanuel Kant Baltic Federal University
Maksimenko Vladimir Aleksandrovich, Immanuel Kant Baltic Federal University
Hramov Aleksandr Evgenevich, Immanuel Kant Baltic Federal University

Рurpose of this work is to study the effect of previous sensory information on the brain’s processing of current visual stimuli. Bistable images (Necker cubes) with a high degree of ambiguity (HA) and a low degree of ambiguity (LA) were used as visual stimuli. Methods. In this paper, we used wavelets to identify features of the brain activity signals. A multivariate analysis of variance was used to compare behavioral characteristics. Spectral power and event-related spectral perturbations were compared via a cluster-based permutation test using the FieldTrip package for Matlab. Results. We found that when the HA stimuli followed the LA stimuli, the activity of neurons in the sensory areas decreased in the early processing stage but increased in the later stages. This result confirmed the hierarchical organization of processing, where the low levels processed the details of the stimulus, and the high levels represented its interpretation. We supposed that processing of HA and LA stimuli was similar at low levels due to their similar morphology. Therefore, the brain might use the LA stimulus template at low levels to reduce the demands when processing the details of the HA stimulus. When the LA stimulus followed the HA stimulus, a weakened response in the sensory regions accompanied a high response in the frontal cortex. It reflected activation of the top-down cognitive functions, detecting a mismatch between the LA stimulus and the HA stimulus template. Conclusion. These results expanded the existing knowledge about the sensory processing mechanisms.

Alexander Hramov was supported by the Council for Grants of the President of the Russian Federation (NSh-2594.2020.2) in formulating the hypothesis. Vladimir Maksimenko received support from the Russian Foundation for Basic Research (19-32-60042) in the development of an experimental paradigm and analysis of behavioral data. Alexander Kuc was supported by the Council for Grants of the President of the Russian Federation (MK-1760.2020.2) in the EEG data analysis
  1. Rauss K, Pourtois G. What is bottom-up and what is top-down in predictive coding? Frontiers in Psychology. 2013;4:276. DOI:10.3389/fpsyg.2013.00276.
  2. Teufel C, Fletcher P. Forms of prediction in the nervous system. Nature Reviews Neuroscience. 2020;21(4):231–242. DOI:10.1038/s41583-020-0275-5.
  3. Kok P, Failing M, de Lange F. Prior expectations evoke stimulus templates in the primary visual cortex. Journal of Cognitive Neuroscience. 2014;26(7):1546–1554. DOI:10.1162/jocn_a_00562. 
  4. Kok P, Mostert P, de Lange F. Prior expectations induce prestimulus sensory templates. Proceedings of the National Academy of Sciences of the United States of America. 2017;114(39):10473–10478. DOI:10.1073/pnas.1705652114.
  5. Teufel C, Dakin S, Fletcher P. Prior object-knowledge sharpens properties of early visual feature detectors. Scientific Reports. 2018;8(1):10853. DOI:10.1038/s41598-018-28845-5.
  6. Heekeren H, Marrett S, Ungerleider L. The neural systems that mediate human perceptual decision making. Nature Reviews Neuroscience. 2008;9(6):467–479. DOI:10.1038/nrn2374.
  7. Friston K. The free-energy principle: a rough guide to the brain? Trends in Cognitive Sciences. 2009;13(7):293–301. DOI:10.1016/j.tics.2009.04.005.
  8. the National Academy of Sciences of the United States of America. 2011;108(51):20754–20759. DOI:10.1073/pnas.1117807108.
  9. Henson R, Rugg M. Neural response suppression, haemodynamic repetition effects, and behavioural priming. Neuropsychologia. 2003;41(3):263–270. DOI:10.1016/S0028-3932(02)00159-8.
  10. Vogels R. Sources of adaptation of inferior temporal cortical responses. Cortex. 2016;80:185–195. DOI:10.1016/j.cortex.2015.08.024.
  11. Vinken K, Op de Beeck H, Vogels R. Face repetition probability does not affect repetition suppression in macaque inferotemporal cortex. Journal of Neuroscience. 2018;38(34):7492–7504. DOI:10.1523/JNEUROSCI.0462-18.2018.
  12. Gilbert C, Li W. Top-down influences on visual processing. Nature Reviews Neuroscience. 2013;14(5):350–363. DOI:10.1038/nrn3476.
  13. Schwiedrzik C, Freiwald W. High-level prediction signals in a low-level area of the macaque face-processing hierarchy. Neuron. 2017;96(1):89–97. DOI:10.1016/j.neuron.2017.09.007.
  14. Maksimenko V, Kuc A, Frolov N, Kurkin S, Hramov A. Effect of repetition on the behavioral and neuronal responses to ambiguous Necker cube images. Scientific Reports. 2021;11(1):3454. DOI:10.1038/s41598-021-82688-1.
  15. Maksimenko V, Frolov N, Hramov A, Runnova A, Grubov V, Kurths J, Pisarchik A. Neural interactions in a spatially-distributed cortical network during perceptual decision-making. Frontiers in Behavioral Neuroscience. 2019;13:220. DOI:10.3389/fnbeh.2019.00220.
  16. Maksimenko V, Runnova A, Zhuravlev M, Makarov V, Nedayvozov V, Grubov V, Pchelintceva S, Hramov A, Pisarchik A. Visual perception affected by motivation and alertness controlled by a noninvasive brain-computer interface. PLoS ONE. 2017;12(12):e0188700. DOI:10.1371/journal.pone.0188700.
  17. Kornmeier J, Friedel E, Wittmann M, Atmanspacher H. EEG correlates of cognitive time scales in the Necker-Zeno model for bistable perception. Consciousness and Cognition. 2017;53:136–150. DOI:10.1016/j.concog.2017.04.011.
  18. Hramov A, Maksimenko V, Pchelintseva S, Runnova A, Grubov V, Musatov V, Zhuravlev M, Koronovskii A, Pisarchik A. Classifying the perceptual interpretations of a bistable image using EEG and artificial neural networks. Frontiers in Neuroscience. 2017;11:674. DOI:10.3389/fnins.2017.00674.
  19. Delorme A, Makeig S. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of Neuroscience Methods. 2004;134(1):9–21. DOI:10.1016/j.jneumeth.2003.10.009.
  20. Pavlov A, Hramov A, Koronovskii A, Sitnikova E, Makarov V, Ovchinnikov A. Wavelet analysis in neurodynamics. Physics-Uspekhi. 2012;55(9):845–875. DOI:10.3367/UFNe.0182.201209a.0905.
  21. Hramov A, Koronovskii A, Makarov V, Maksimenko V, Pavlov A, Sitnikova E. Wavelets in Neuroscience. Cham: Springer; 2021. 384 p. DOI:10.1007/978-3-030-75992-6.
  22. Bakdash J, Marusich L. Repeated measures correlation. Frontiers in Psychology. 2017;8:456. DOI:10.3389/fpsyg.2017.00456. 
  23. Summerfield C, de Lange F. Expectation in perceptual decision making: neural and computational mechanisms. Nature Reviews Neuroscience. 2014;15(11):745–756. DOI:10.1038/nrn3838.
  24. Tseng P, Iu K, Juan C. The critical role of phase difference in theta oscillation between bilateral parietal cortices for visuospatial working memory. Scientific Reports. 2018;8(1):349. DOI:10.1038/s41598-017-18449-w.
  25. Berger B, Griesmayr B, Minarik T, Biel A, Pinal D, Sterr A, Sauseng P. Dynamic regulation of interregional cortical communication by slow brain oscillations during working memory. Nature Communications. 2019;10(1):4242. DOI:10.1038/s41467-019-12057-0.
  26. Engel A, Fries P. Beta-band oscillations – signalling the status quo? Current Opinion in Neurobiology. 2010;20(2):156–165. DOI:10.1016/j.conb.2010.02.015.
  27. Okazaki M, Kaneko Y, Yumoto M, Arima K. Perceptual change in response to a bistable picture increases neuromagnetic beta-band activities. Neuroscience Research. 2008;61(3):319–328. DOI:10.1016/j.neures.2008.03.010.
  28. Folstein J, Van Petten C. Influence of cognitive control and mismatch on the N2 component of the ERP: A review. Psychophysiology. 2008;45(1):152–170. DOI:10.1111/j.1469-8986.2007.00602.x.
  29. Nigbur R, Ivanova G, Sturmer B. Theta power as a marker for cognitive interference. Clinical ¨ Neurophysiology. 2011;122(11):2185–2194. DOI:10.1016/j.clinph.2011.03.030.
  30. Wagner J, Wessel J, Ghahremani A, Aron A. Establishing a right frontal beta signature for stopping action in scalp EEG: Implications for testing inhibitory control in other task contexts. Journal of Cognitive Neuroscience. 2018;30(1):107–118. DOI:10.1162/jocn_a_01183.
  31. Dehais F, Dupres A, Di Flumeri G, Verdiere K, Borghini G, Babiloni F, Roy R. Monitoring pilot’s cognitive fatigue with engagement features in simulated and actual flight conditions using an hybrid fNIRS-EEG passive BCI. In: 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC). New York: IEEE; 2019. P. 544–549. DOI:10.1109/SMC.2018.00102.
  32. Gateau T, Ayaz H, Dehais F. In silico vs. over the clouds: On-the-fly mental state estimation of aircraft pilots, using a functional near infrared spectroscopy based passive-BCI. Frontiers in Human Neuroscience. 2018;12:187. DOI:10.3389/fnhum.2018.00187.
  33. Maksimenko V, Hramov A, Grubov V, Nedaivozov V, Makarov V, Pisarchik A. Nonlinear effect of biological feedback on brain attentional state. Nonlinear Dynamics. 2019;95(3):1923–1939. DOI:10.1007/s11071-018-4668-1.
  34. Hramov A, Maksimenko V, Pisarchik A. Physical principles of brain-computer interfaces and their applications for rehabilitation, robotics and control of human brain states. Physics Reports. 2021;918:1–133. DOI:10.1016/j.physrep.2021.03.002.