Components of Evoked Potentials in Frontal Cortex Areas Associated with Image Classification and Independent of Physical Characteristics of Stimuli

Cover Page

Cite item

Full Text

Open Access Open Access
Restricted Access Access granted
Restricted Access Subscription Access

Abstract

Currently, there is a problem of increasing the objectivity of electrophysiological methods for assessment of visual acuity. The purpose of this work: to study the characteristics of cognitive evoked potentials associated with events in the frontal areas of the brain in the tasks of images classification of objects by semantic features. We used visual stimuli, divided into the following classes: by semantic features – into living and nonliving objects, and by spatial frequency ranges – into broadband contour images (white on a black background) and narrowband, in which the low-frequency or high-frequency ranges were isolated by digital filtration. The prepared images were presented to the subjects on the display. In each series of studies, the subjects were instructed to classify the images by the features of “living/nonliving” object, regardless of the physical characteristics of the stimuli. It was shown that the P200 component of evoked potentials in the ventrolateral areas of the frontal cortex depends on the semantic properties of the stimuli – images of animate and inanimate objects and does not depend on such physical characteristics as the presence/absence of high-frequency or low-frequency filtering. In this paper, as a result of the analysis of individual data in two series of studies, the results of measurements of the amplitudes and latent periods for the P200 component of evoked potentials for different (by semantics) classes of contour images with high-frequency and low-frequency filtering at selected several individual spatial frequencies and contour unfiltered images with different instructions to the subjects are presented. The obtained results may be used in the development of a new additional method for assessing visual acuity using visual evoked potentials.

Full Text

Restricted Access

About the authors

G. А. Moiseenko

I. P. Pavlov Institute of Physiology RAS

Author for correspondence.
Email: MoiseenkoGA@infran.ru
Russian Federation, St. Petersburg

S. А. Koskin

I. P. Pavlov Institute of Physiology RAS; Military Medical Academy named after S. M. Kirov

Email: MoiseenkoGA@infran.ru
Russian Federation, St. Petersburg; St. Petersburg

S. V. Pronin

I. P. Pavlov Institute of Physiology RAS

Email: MoiseenkoGA@infran.ru
Russian Federation, St. Petersburg

V. N. Chikhman

I. P. Pavlov Institute of Physiology RAS

Email: MoiseenkoGA@infran.ru
Russian Federation, St. Petersburg

Е. А. Vershinina

I. P. Pavlov Institute of Physiology RAS

Email: MoiseenkoGA@infran.ru
Russian Federation, St. Petersburg

О. V. Zhukova

I. P. Pavlov Institute of Physiology RAS

Email: volgazhukova@gmail.com
Russian Federation, St. Petersburg

References

  1. Kostandov E.A. [Psychophysiology of consciousness and the unconscious]. St. Petersburg: Piter, 2004. 167 p.
  2. Bonin P., Gelin M., Bugaiska A. Animates are better remembered than inanimates: Further evidence from word and picture stimuli // Mem. Cogn. 2014. V. 42. № 3. P. 370.
  3. Yang J., Wang A., Yan M. et al. Distinct processing for pictures of animals and objects: Evidence from eye movements // Emotion. 2012. V. 12. № 3. P. 540.
  4. Pauen S. Evidence for knowledge–based category discrimination in infancy // Child Dev. 2002. V. 73. № 4. P. 1016.
  5. Taniguchi K., Tanabe-Ishibashi A., Itakura S. The categorization of objects with uniform texture at superordinate and living/non-living levels in infants: An exploratory study // Front. Psychol. 2020. V. 11. P. 2009.
  6. Marchenko O.P. [Electrical potentials of the brain associated with categorization of labels of animate and inanimate objects] // Exp. Psychol. 2010. V. 3. № 1. P. 5.
  7. Gerasimenko N.Yu., Slavutskaya A.V., Kalinin S.A. et al. [Recognition of visual objects under forward masking. Effects of cathegorial similarity of test and masking stimuli] // Zh. Vyssh. Nerv. Deyat. Im. I.P. Pavlova. 2013. V. 63. № 4. P. 419.
  8. Mikhailova E.S., Gerasimenko N.Yu., Avsienko A.V. Recognition of forward-masked complex and simple images // Human Physiology. 2009. V. 35. № 3. P. 267.
  9. Verkhlyutov V.M., Ushakov V.L., Strelets V.B. [Decreased latency of the evoked potential component N170 during repeated presentation of face images] // Zh. Vyssh. Nerv. Deyat. Im. I.P. Pavlova. 2009. V. 50. № 3. P. 307.
  10. Ponomarev V.A., Kropotov Yu.D. Improving source localization of event-related potentials in the GO/NOGO task by modeling their cross-covariance structure // Human Physiology. 2013. V. 39. № 1. P. 27.
  11. Ponomarev V.A., Pronina M.V., Kropotov Yu.D. Latent components of event-related potentials in a visual cued Go/NoGo task // Human Physiology. 2019. V. 45. № 5. P. 474.
  12. Glezer V.D. Vision and Thinking. Leningrad: Nauka, 1993. 284 p.
  13. Shelepin Yu.E. Introduction to Neuroiconics: Monograph. St. Petersburg: Troitsky Most, 2017. 352 p.
  14. Chikhman V.N., Bondarko V.M., Danilova M.V. et al. Complexity of images: Experimental and computational estimates compared // Perception. 2012. V. 41. № 6. P. 631.
  15. Attneave F. Physical determinants of the judged complexity of shapes // J. Exp. Psychol. 1957. V. 53. № 4. P. 221.
  16. Long B., Störmer V.S., Alvarez G.A. Mid-level perceptual features contain early cues to animacy // J. Vis. 2017. V. 17. № 6. P. 20.
  17. Yetter M., Robert S., Mammarella G. et al. Curvilinear features are important for animate/inanimate categorization in macaques // J. Vis. 2021. V. 21. № 4. P. 3.
  18. Moiseenko G.A., Shelepin Yu.E., Kharauzov A.K. et al. Classification and recognition of images of animate and inanimate objects // J. Opt. Technol. 2015. V. 82. № 10. P. 685.
  19. Moiseenko G.A., Pronin S.V., Shelepin Yu.E. Investigation of scale-invariant image classification mechanisms // J. Opt. Technol. 2019. V. 86. № 11. P. 729.
  20. Chuprov A.D., Zhedyale N.A., Voronina A.E. [Methods for investigation of central department of a visual analyzer (review)] // Saratov J. Med. Sci. Res. 2021. V. 17 № 2. P. 396.
  21. Ophthalmology: national guidelines / Eds. Avetisov S.E., Egorov E.A., Moshetov L.K., Neroev V.V., Takhchidi H.P. 2nd ed., revised. and add. Moscow: GEOTAR-Media, 2022. Ser.: National Guidelines. 904 p.
  22. Moiseenko G.A., Vershinina E.A., Pronin S.V. et al. Latency of evoked potentials in the tasks involving classification of images after wavelet filtration // Human Physiology. 2016. V. 42. № 6. P. 615.
  23. Kutsenko M.A. History and methods of visometry // Bulletin of the Council of Young Scientists and Specialists of the Chelyabinsk Region. 2018. V. 2. № 3. P. 32.
  24. Moiseenko G.A., Pronin S.V., Zhil’chuk D.I. et al. Vanishing optotypes and objective measurement of human visual acuity // J. Opt. Technol. 2020. V. 87. № 12. P. 761.
  25. Harauzov A.K., Shelepin Y.E., Noskov Y.A. et al. The time course of pattern discrimination in the human brain // Vision Res. 2016. V. 125. P. 55.
  26. Kozlovskiy S., Kashirin V., Glazkova A. Electrophysiological differences in perception of animate and inanimate objects // Int. J. Psychophysiol. 2023. V. 188. P. 116.
  27. Mikhailova E.S., Mayorova L.A., Gerasimenko N.Yu. et al. [Sex differences in working memory for simple visual features. Analysis of event-related potentials in the process and space of sensors and dipole sources] // Zh. Vyssh. Nerv. Deyat. Im. I.P. Pavlova. 2022. V. 72. № 6. P. 836.
  28. Gerasimenko N.Yu., Kushnir A.B., Mikhailova E.S. Masking effects of irrelevant visual information inder conditions of basic and superordinate categorization of complex images // Human Physiology. 2019. V. 45. № 1. P. 1.
  29. Lee G., Blumenfeld R.S., D'Esposito M. Disruption of dorsolateral but not ventrolateral prefrontal cortex improves unconscious perceptual memories // J. Neurosci. 2013. V. 33. № 32. P. 13233.
  30. Vakhrameeva O.A., Sukhinin M.V., Moiseenko G.A. et al. [Investigation of dependence of perception thresholds on fovea geometry] // Sensory Systems. 2013. V. 27. № 2. P. 122.
  31. Chan A.W.-Y. Functional organization and visual representations of human ventral lateral prefrontal cortex // Front. Psychol. 2013. V. 4. P. 371.
  32. Radtke E.L., Martens U., Gruber T. The steady‐state visual evoked potential (SSVEP) reflects the activation of cortical object representations: evidence from semantic stimulus repetition // Exp. Brain Res. 2021. V. 239. № 2. P. 545.
  33. Badre D., Wagner A.D. Left ventrolateral prefrontal cortex and the cognitive control of memory // J. Neuropsychol. 2007. V. 45. № 13. P. 2883.
  34. Farzmahdi A.J., Fallah F., Rajimehr R., Ebrahimpour R. Task-dependent neural representations of visual object categories // Eur. J. Neurosci. 2021. V. 54. № 7. P. 6445.
  35. Kravitz D.J., Saleem K.S., Baker C.I. et al. The ventral visual pathway: an expanded neural framework for the processing of object quality // Trends Cogn. Sci. 2013. V. 17. № 1. P. 26.

Supplementary files

Supplementary Files
Action
1. JATS XML
Download
2. Fig. 1. The methodology of the study. In the first series of the study, images from a single set of stimuli were presented, consisting of objects of living and inanimate nature, which were subjected to wavelet filtering in the region of low (1 cycle/deg) and high (10 cycles/deg) spatial frequencies (A). In the second series, images of living and inanimate nature were presented without preliminary wavelet filtering, the image size was 3 and 0.4 degrees (B).

Download (24KB)
Indexing metadata
3. Fig. 2. Comparative analysis of significant differences between the amplitudes of the components of evoked potentials (VP) in the F7 lead to images of objects of living (a – light curve) and inanimate (b – dark curve) nature in two series of studies. A – in the classification of images subjected to wavelet filtering in the region of high spatial frequencies. B – when classifying images subjected to wavelet filtering in the low spatial frequency region. B – when classifying images without wavelet filtering. The data are averaged over a group of subjects.

Download (21KB)
Indexing metadata

Copyright (c) 2024 Russian Academy of Sciences