Advances in Applied Physiology

| Peer-Reviewed |

Specific Features of Perception of Semantically Equivalent Stimuli in the Verbal and Visual Form

Received: 21 May 2018    Accepted: 06 June 2018    Published: 06 July 2018
Views:       Downloads:

Share This Article

Abstract

Response time and evoked potentials were registered for visual images related to two categories fruit and tableware as well as their verbal representations. The stimuli were presented randomly. The subjects were to attribute them regardless of the form (a word or image) to one of the categories. 11 female and 10 male subjects (average age 21.9±2.9 years) participated in the tests. 6 components of the evoked potentials were singled out: Р1 (Р66), N1 (N124), Р2 (Р180), N2 (N248), Р3 (Р331) and N3 (N456). Analysis showed that both female and male subjects demonstrated reliably longer response time for words as compared to those for corresponding images. For words, evoked potentials were registered in more complex configurations and with a shorter latency period for the early components (P1, N1) and longer latency period for the late ones (P2, N2, P3, N3). The evoked potential amplitude in response to verbal stimuli was smaller than that for visual ones. Evoked potential components in response to target stimuli (both images and words) had, in general, shorter latency. The amplitude of N1, Р2 and N2 components was lower, while that of P3 and N3 was higher for target stimuli rather than a non-target. The obtained results allow us to assume that evaluation of the type of information (verbal or visual) can be performed on early stages of stimulus perception (up to 120-150 ms). Further analysis includes either more detailed description of spatial features of the visual stimuli in parietal and occipital lobes or estimation of the semantics of a word employing the frontal and temporal areas. Decision-making on formulating a response barely depends on the manner of information presentation (visual and verbal).

DOI 10.11648/j.aap.20180301.13
Published in Advances in Applied Physiology (Volume 3, Issue 1, June 2018)
Page(s) 14-25
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Visual Stimuli, Verbal Stimuli, EP Components, Response Time, Gender-Related Differences

References
[1] Beteleva, T. G., & Sinitsyn, S. V. (2008). Event-related potentials at different stages of the operation of visual working memory. Human Physiology, 34 (3), 265. DOI: 10.1134/S0362119708030018.
[2] Mar'ina, I. V., & Strelets, V. B. (2010). Verbal stimuli semantics and relevance of ERPs. Zhurnal vysshei nervnoi deiatelnosti imeni IP Pavlova, 60 (1), 22-31.
[3] Ganin, I. P., & AIa, K. (2014). The P300-based brain-computer interface: presentation of the complex" flash+ movement" stimuli. Zhurnal vysshei nervnoi deiatelnosti imeni IP Pavlova, 64 (1), 32-40. DOI: 10.7868/S0044467714010067.
[4] Saltykov, K. A., Bark, E. D., & Koulikov, M. A. (2014). Characteristics of event-related potentials in response to symbolical and alphabetical stimulation matrices used in a P300-based brain-computer interface. Human Physiology, 40 (4), 367-374. DOI: 10.1134/S0362119714030141.
[5] Andrews, S., Burton, A. M., Schweinberger, S. R., & Wiese, H. (2017). Event-related potentials reveal the development of stable face representations from natural variability. Quarterly Journal of Experimental Psychology, 70 (8), 1620-1632.
[6] Mikhailova, E. S., Gerasimenko, N. Y., Slavutskaya, A. V., Krylova, M. A., & Izyurov, I. V. (2017). Temporal and topographic characteristics of evoked potentials in the conflict of two consecutive visual stimuli in a working memory task. Human Physiology, 43 (3), 248-258. DOI: 10.1134/S0362119717030148.
[7] Nguyen, T., Potter, T., Grossman, R., & Zhang, Y. (2018). Characterization of dynamic changes of current source localization based on spatiotemporal fMRI constrained EEG source imaging. Journal of neural engineering, 15 (3), 036017.
[8] Fokin, V. A., Shelepin, Y. E., Kharauzov, A. K., Trufanov, G. E., Sevost’yanov, A. V., Pronin, S. V., & Koskin, S. A. (2008). Localization of human cortical areas activated on perception of ordered and chaotic images. Neuroscience and behavioral physiology, 38 (7), 677-685. DOI: 10.1007/s11055-008-9033-2.
[9] Ivanitskii, A. M., Portnova, G. V., Martynova, O. V., Maiorova, L. A., Fedina, O. N., & Petrushevskii, A. G. (2015). Brain Mapping in Verbal and Spatial Thinking. Neuroscience and Behavioral Physiology, 45 (2), 146-153. DOI: 10.1007/s11055-015-0052-5.
[10] Lawrence, S. J., Formisano, E., Muckli, L., & de Lange, F. P. (2017). Laminar fMRI: applications for cognitive neuroscience. Neuroimage. pp. 1-7. https://doi.org/10.1016/j.neuroimage.2017.07.004.
[11] Podladchikova, L. N., Bondar, G. G., Ivlev, S. A., Tikidzhi-Khambur'ian, R. A., & Dunin-Barkovskiĭ, V. L. (2008). Dynamics of the activity of cerebellar Purkinje cells induced by changes in the duration of complex spikes. Biofizika, 53 (3), 488-494.
[12] Mazzucato, L., Fontanini, A., & La Camera, G. (2015). Dynamics of multistable states during ongoing and evoked cortical activity. Journal of Neuroscience, 35 (21), 8214-8231. DOI: 10.1523/JNEUROSCI.4819-14.2015.
[13] Schmidt, H., Avitabile, D., Montbrió, E., & Roxin, A. (2018). Network mechanisms underlying the role of oscillations in cognitive tasks. bioRxiv, 271973.
[14] Kiroi, V. N., & Aslanyan, E. V. (2006). General laws for the formation of the state of monotony. Neuroscience and behavioral physiology, 36 (9), 921-928. DOI: 10.1007/s11055-006-0108-7.
[15] Hramov, A. E., Pchelintseva, S. V., Runnova, A. E., Musatov, V. Y., Grubov, V. V., Zhuravlev, M. O., Pisarchik, A. N. (2017). Classifying the perceptual interpretations of a bistable image using EEG and artificial neural networks. Frontiers in neuroscience, 11, 674.
[16] Kazantsev, V. B., Gordleeva, S. Y., Stasenko, S. V., & Dityatev, A. E. (2013). Appearance of multistability in a neuron model with network feedback. JETP letters, 96 (11), 739-742. DOI: 10.1134/S0021364012230087.
[17] Testolin, A., De Filippo De Grazia, M., & Zorzi, M. (2017). The role of architectural and learning constraints in neural network models: a case study on visual space coding. Frontiers in computational neuroscience, 11, 13.
[18] Pisarchik, A. N., Bashkirtseva, I. A., & Ryashko, L. B. (2015). Controlling bistability in a stochastic perception model. The European Physical Journal Special Topics, 224 (8), 1477-1484. DOI: 10.1140/epjst/e2015-02473-0.
[19] Breakspear, M. (2017). Dynamic models of large-scale brain activity. Nature neuroscience, 20 (3), 340.
[20] Verkhliutov, V. M., Ushakov, V. L., & Strelets, V. B. (2009). Decrease in N170 evoked potential component latency during repeated presentation of face images. Zhurnal vysshei nervnoi deiatelnosti imeni IP Pavlova, 59 (3), 307-317.
[21] Pivik, R. T., Andres, A., Cleves, M. A., & Badger, T. M. (2016). Gamma EEG Activation During Picture-Word Semantic Processing in 3 Year Olds Varies As A Function of Gender And Infant Diet. The FASEB Journal, 30 (1 Supplement), 671-15.
[22] Zhu, C., Ma, X., Ji, L., Chen, S., & Cao, X. (2017). Sex differences in categorical adaptation for faces and Chinese characters during early perceptual processing. Frontiers in Human Neuroscience, 11, 656.
[23] Razumnikova, O. M., Volf, N. V., & Tarasova, I. V. (2009). Strategy and results: Sex differences in electrographic correlates of verbal and figural creativity. Human physiology, 35 (3), 285-294. DOI: 10.1134/S0362119709030049.
[24] Slavutzkaya, A. V., Gerasimenko, N. Y., & Mikhailova, E. S. (2012). Recognition of spatially transformed objects in men and women: analysis of behavior and evoked potentials. Human Physiology, 38 (3), 238-248. DOI: 10.1134/S0362119712030139.
[25] Conejero, Á., Guerra, S., Abundis‐Gutiérrez, A., & Rueda, M. R. (2018). Frontal theta activation associated with error detection in toddlers: influence of familial socioeconomic status. Developmental science, 21 (1). https://doi.org/10.1111/desc.12494.
[26] Amenta, S., & Crepaldi, D. (2012). Morphological processing as we know it: an analytical review of morphological effects in visual word identification. Frontiers in psychology, 3, 232. DOI: 10.3389/fpsyg.2012.0023.
[27] Pammer, K., Hansen, P. C., Kringelbach, M. L., Holliday, I., Barnes, G., Hillebrand, A., Cornelissen, P. L. (2004). Visual word recognition: the first half second. Neuroimage, 22 (4), 1819-1825. DOI: 10.1016/j.neuroimage.2004.05.004.
[28] Gagl, B., Richlan, F., Ludersdorfer, P., Sassenhagen, J., & Fiebach, C. J. (2016). The lexical categorization model: A computational model of left ventral occipito-temporal cortex activation in visual word recognition. bioRxiv, 085332.
[29] Rosch, E., Mervis, C. B., Gray, W. D., Johnson, D. M., & Boyes-Braem, P. (1976). Basic objects in natural categories. Cognitive psychology, 8 (3), 382-439. DOI: 10.1016/0010-0285 (76) 90013-X.
[30] Wiegand, I., Lauritzen, M. J., Osler, M., Mortensen, E. L., Rostrup, E., Rask, L., & Petersen, A. (2018). EEG correlates of visual short-term memory in older age vary with adult lifespan cognitive development. Neurobiology of aging, 62, 210-220.
[31] Pearson, J., Naselaris, T., Holmes, E. A., & Kosslyn, S. M. (2015). Mental imagery: functional mechanisms and clinical applications. Trends in cognitive sciences, 19 (10), 590-602. DOI: 10.1016/j.tics.2015.08.003.
[32] Robinson, A. K., Venkatesh, P., Boring, M. J., Tarr, M. J., Grover, P., & Behrmann, M. (2017). Very high density EEG elucidates spatiotemporal aspects of early visual processing. Scientific reports, 7 (1), 16248.
[33] Paivio, A. (2014). Intelligence, dual coding theory, and the brain. Intelligence, 47, 141-158. DOI: 10.1016/j.intell.2014.09.002.
[34] Gutnisky, D. A., Beaman, C. B., Lew, S. E., & Dragoi, V. (2017). Spontaneous fluctuations in visual cortical responses influence population coding accuracy. Cerebral cortex, 27 (2), 1409-1427.
[35] Harter, M. R., & White, C. T. (1968). Effects of contour sharpness and check-size on visually evoked cortical potentials. Vision Research, 8 (6), 701-711.
[36] Il’yuchenok, I. R., Sysoeva, O. V., & Ivanitskii, A. M. (2008). Two semantic systems in the brain for rapid and slow differentiation of abstract and concrete words. Neuroscience and behavioral physiology, 38 (9), 963-970. DOI: 10.1007/s11055-008-9083-5.
[37] Shtyrov, Y., Kujala, T., & Pulvermüller, F. (2010). Interactions between language and attention systems: early automatic lexical processing?. Journal of Cognitive Neuroscience, 22 (7), 1465-1478. DOI: 10.1162/jocn.2009.21292.
[38] Han, S., Yund, E. W., & Woods, D. L. (2003). An ERP study of the global precedence effect: the role of spatial frequency. Clinical Neurophysiology, 114 (10), 1850-1865. DOI: 10.1016/S1388-2457 (03) 00196-2.
[39] Banich, M. T. (2018). Emerging themes in cognitive control: Commentary on the special issue of Psychophysiology entitled “Dynamics of Cognitive Control: A View Across Methodologies”. Psychophysiology, 55 (3), e13060.
[40] Folstein, J. R., & Van Petten, C. (2008). Influence of cognitive control and mismatch on the N2 component of the ERP: a review. Psychophysiology, 45 (1), 152-170. DOI: 10.1111/j.1469-8986.2007.00602.x.
[41] Moore, R. D., Pindus, D. M., Drolette, E. S., Scudder, M. R., Raine, L. B., & Hillman, C. H. (2015). The persistent influence of pediatric concussion on attention and cognitive control during flanker performance. Biological psychology, 109, 93-102.
[42] Polich, J. (2007). Updating P300: an integrative theory of P3a and P3b. Clinical neurophysiology, 118 (10), 2128-2148. DOI: 10.1016/j.clinph.2007.04.019.
[43] Perri, R. L., & Di Russo, F. (2017). Executive Functions and Performance Variability Measured by Event-Related Potentials to Understand the Neural Bases of Perceptual Decision-Making. Frontiers in human neuroscience, 11, 556.
[44] Amin, H. U., Malik, A. S., Kamel, N., Chooi, W. T., & Hussain, M. (2015). P300 correlates with learning & memory abilities and fluid intelligence. Journal of neuroengineering and rehabilitation, 12 (1), 87.
[45] Causse, M., Peysakhovich, V., & Fabre, E. F. (2016). High working memory load impairs language processing during a simulated piloting task: an ERP and pupillometry study. Frontiers in human neuroscience, 10, 240.
[46] Kostandov, E. A. (2007). Significance of the context of cognitive activity in the formation of unconscious visual sets. Neuroscience and behavioral physiology, 37 (4), 321-329. DOI: 10.1007/s11055-007-0017-4.
[47] Morioka, S., Osumi, M., Shiotani, M., Nobusako, S., Maeoka, H., Okada, Y., Matsuo, A. (2016). Incongruence between Verbal and Non-Verbal Information Enhances the Late Positive Potential. PloS one, 11 (10), e0164633.
[48] Grandchamp, R., Rapin, L., Lœvenbruck, H., Perrone-Bertolotti, M., Pichat, C., Lachaux, J. P., & Baciu, M. (2016). Inner Speech with your own or someone else’s voice: cerebral correlates assessed with fMRI. Nat. Rev. Neurosci, 13, 556-571.
[49] van Schie, H. T., Wijers, A. A., Kellenbach, M. L., & Stowe, L. A. (2003). An event-related potential investigation of the relationship between semantic and perceptual levels of representation. Brain and language, 86 (2), 300-325. DOI: 10.1016/S0093-934X (02) 00546-1.
[50] Rao, R. P., & Ballard, D. H. (1997). Dynamic model of visual recognition predicts neural response properties in the visual cortex. Neural computation, 9 (4), 721-763. DOI: 10.1162/neco.1997.9.4.721.
[51] Talsma, D. (2015). Predictive coding and multisensory integration: an attentional account of the multisensory mind. Frontiers in integrative neuroscience, 9, 19.
[52] Bar M., Kassam K. S., Ghuman A. S., Boshyan J., Schmidt A. M., Dale A. M., Hämäläinen M. S., Marinkovic K., Schacter D. L., Rosen B. R., Halgren E. (2006). Top-down facilitation of visual recognition. Proceedings of the National Academy of Sciences of the United States of America, 103 (2), 449-454. DOI: 10.1073/pnas.0507062103.
[53] O’Callaghan, C., Kveraga, K., Shine, J. M., Adams Jr, R. B., & Bar, M. (2017). Predictions penetrate perception: Converging insights from brain, behaviour and disorder. Consciousness and cognition, 47, 63-74.
[54] Schendan, H. E., & Kutas, M. (2007). Neurophysiological evidence for the time course of activation of global shape, part, and local contour representations during visual object categorization and memory. Journal of Cognitive Neuroscience, 19 (5), 734-749. DOI: 10.1162/jocn.2007.19.5.734.
[55] Bar, M., Aminoff, E., & Ishai, A. (2007). Famous faces activate contextual associations in the parahippocampal cortex. Cerebral Cortex, 18 (6), 1233-1238. DOI: 10.1093/cercor/bhm170.
[56] Stothart, G., Quadflieg, S., & Milton, A. (2017). A fast and implicit measure of semantic categorisation using steady state visual evoked potentials. Neuropsychologia, 102, 11-18.
[57] Cabeza, R. (2008). Role of parietal regions in episodic memory retrieval: the dual attentional processes hypothesis. Neuropsychologia, 46 (7), 1813-1827. DOI: 10.1016/j.neuropsychologia.2008.03.019.
[58] Meijs, E. L., Slagter, H. A., de Lange, F. P., & van Gaal, S. (2018). Dynamic Interactions between Top–Down Expectations and Conscious Awareness. Journal of Neuroscience, 38 (9), 2318-2327.
[59] Kutas, M., & Federmeier, K. D. (2011). Thirty years and counting: finding meaning in the N400 component of the event-related brain potential (ERP). Annual review of psychology, 62, 621-647. DOI: 10.1146/annurev.psych.093008.131123.
[60] Nunez, M. D., Vandekerckhove, J., & Srinivasan, R. (2017). How attention influences perceptual decision making: Single-trial EEG correlates of drift-diffusion model parameters. Journal of mathematical psychology, 76, 117-130.
[61] Nicolae, I. E., Acqualagna, L., & Blankertz, B. (2017). Assessing the Depth of Cognitive Processing as the Basis for Potential User-State Adaptation. Frontiers in Neuroscience, 11, 548. DOI: 10.3389/fnins.2017.00548.
[62] Kiroĭ, V. N. (1987). Space-time organization of the electrical activity of the human brain in a state of calm wakefulness and during the solution of intellectual tasks. Zhurnal vysshei nervnoi deiatelnosti imeni IP Pavlova, 37 (6), 1025-1033.
[63] Vartanov, A. V., & Pasechnik, I. V. (2005). Brain mechanisms of the semantic analysis of words-homonyms. Zhurnal vysshei nervnoi deiatelnosti imeni IP Pavlova, 55 (2), 197-201.
[64] Baranov-Krylov, I. N., & Astashchenko, A. P. (2007). Visual search and event-related potentials in the extrastriate cortical areas in humans. Rossiiskii fiziologicheskii zhurnal imeni IM Sechenova, 93 (9), 1001-1011.
[65] Medvedev L., Shoshina I., Fedorova E. (2011). Reflection of Processing of Visually Presented Poggendorff Figure in ERP. Zhurnal Vysshei Nervnoi Deyatelnosti Imeni I. P. Pavlova, 61 (1), 61-66.
[66] Huang, J., Hensch, T., Ulke, C., Sander, C., Spada, J., Jawinski, P., & Hegerl, U. (2017). Evoked potentials and behavioral performance during different states of brain arousal. BMC neuroscience, 18 (1), 21.
[67] Dippel, G., & Beste, C. (2015). A causal role of the right inferior frontal cortex in implementing strategies for multi-component behaviour. Nature communications, 6, 6587.
[68] Clawson, A., Clayson, P. E., Keith, C. M., Catron, C., & Larson, M. J. (2017). Conflict and performance monitoring throughout the lifespan: An event-related potential (ERP) and temporospatial component analysis. Biological psychology, 124, 87-99.
[69] Kiroy V. N., Belova E. I. (2000). Mechanisms of formation and role of the oscillatory activity of neeuronal populations in brain state and information processing // Zhurnal Vysshei Nervnoi Deyatelnosti Imeni I. P. Pavlova, 50 (2), 189-191.
[70] Kiroĭ, V. N., & Choraian, O. G. (2000). The neuronal ensembles of the brain. Uspekhi fiziologicheskikh nauk, 31 (3), 23-38.
[71] Dumenko, V. N. (2014). The functional role of neocortical gamma activity in the process of interrelations between different areas. Zhurnal vysshei nervnoi deiatelnosti imeni IP Pavlova, 64 (1), 3-20. DOI: 10.7868/S0044467714010055.
[72] Marshall, T. R., den Boer, S., Cools, R., Jensen, O., Fallon, S. J., & Zumer, J. M. (2018). Occipital Alpha and Gamma Oscillations Support Complementary Mechanisms for Processing Stimulus Value Associations. Journal of cognitive neuroscience, 30 (1), 119-129.
[73] Hillyard, S. A., Teder-Sälejärvi, W. A., & Münte, T. F. (1998). Temporal dynamics of early perceptual processing. Current opinion in neurobiology, 8 (2), 202-210. DOI: 10.1016/S0959-4388 (98) 80141-4.
[74] Keitel, C., Thut, G., & Gross, J. (2017). Visual cortex responses reflect temporal structure of continuous quasi-rhythmic sensory stimulation. NeuroImage, 146, 58-70.
[75] Beteleva, T. G., & Petrenko, N. E. (2006). Mechanisms of selective attention in adults and children as reflected by evoked potentials to warning stimuli. Human Physiology, 32 (5), 509-516. DOI: 10.1134/S0362119706050021.
[76] Baumgartner, H. M., Graulty, C. J., Hillyard, S. A., & Pitts, M. A. (2018). Does spatial attention modulate the earliest component of the visual evoked potential?. Cognitive neuroscience, 9 (1-2), 4-19.
[77] Rousselet, G. A., Husk, J. S., Bennett, P. J., & Sekuler, A. B. (2008). Time course and robustness of ERP object and face differences. Journal of Vision, 8 (12), 3-3. DOI: 10.1167/8.12.3.
[78] Contini, E. W., Wardle, S. G., & Carlson, T. A. (2017). Decoding the time-course of object recognition in the human brain: From visual features to categorical decisions. Neuropsychologia, 105, 165-176.
[79] Fu, S., Zinni, M., Squire, P. N., Kumar, R., Caggiano, D. M., & Parasuraman, R. (2008). When and where perceptual load interacts with voluntary visuospatial attention: An event-related potential and dipole modeling study. Neuroimage, 39 (3), 1345-1355. DOI: 10.1016/j.neuroimage.2007.09.068.
[80] Schneider, D., Barth, A., Getzmann, S., & Wascher, E. (2017). On the neural mechanisms underlying the protective function of retroactive cuing against perceptual interference: Evidence by event-related potentials of the EEG. Biological psychology, 124, 47-56.
[81] Müller-Bardorff, M., Schulz, C., Peterburs, J., Bruchmann, M., Mothes-Lasch, M., Miltner, W., & Straube, T. (2016). Effects of emotional intensity under perceptual load: an event-related potentials (ERPs) study. Biological psychology, 117, 141-149.
[82] Krusienski, D. J., Sellers, E. W., McFarland, D. J., Vaughan, T. M., & Wolpaw, J. R. (2008). Toward enhanced P300 speller performance. Journal of neuroscience methods, 167 (1), 15-21. DOI: 10.1016/j.jneumeth.2007.07.017.
[83] Baranov-Krylov, I. N., & Astashchenko, A. P. (2008). Characteristics of visual seeking and evoked potentials in the extrastriate areas of the cortex in humans. Neuroscience and behavioral physiology, 38 (7), 661-668. DOI: 10.1007/s11055-008-9030-5.
[84] Zhu, C., He, W., Qi, Z., Wang, L., Song, D., Zhan, L., Luo, W. (2015). The time course of emotional picture processing: an event-related potential study using a rapid serial visual presentation paradigm. Frontiers in psychology, 6, 954.
[85] Brunia, C. H. M. (2016). Brain potentials related to preparation and action. In Perspectives on perception and action (pp. 119-144). Routledge.
Author Information
  • The Center of Neurotechnologies, Southern Federal University, Rostov-on-Don, Russia

  • The Center of Neurotechnologies, Southern Federal University, Rostov-on-Don, Russia

  • The Center of Neurotechnologies, Southern Federal University, Rostov-on-Don, Russia

  • The Center of Neurotechnologies, Southern Federal University, Rostov-on-Don, Russia

Cite This Article
  • APA Style

    Valery Nikolaevich Kiroy, Yelena Vlasovna Aslanyan, Dmitry Mikhailovich Lazurenko, Oleg Marksovich Bakhtin. (2018). Specific Features of Perception of Semantically Equivalent Stimuli in the Verbal and Visual Form. Advances in Applied Physiology, 3(1), 14-25. https://doi.org/10.11648/j.aap.20180301.13

    Copy | Download

    ACS Style

    Valery Nikolaevich Kiroy; Yelena Vlasovna Aslanyan; Dmitry Mikhailovich Lazurenko; Oleg Marksovich Bakhtin. Specific Features of Perception of Semantically Equivalent Stimuli in the Verbal and Visual Form. Adv. Appl. Physiol. 2018, 3(1), 14-25. doi: 10.11648/j.aap.20180301.13

    Copy | Download

    AMA Style

    Valery Nikolaevich Kiroy, Yelena Vlasovna Aslanyan, Dmitry Mikhailovich Lazurenko, Oleg Marksovich Bakhtin. Specific Features of Perception of Semantically Equivalent Stimuli in the Verbal and Visual Form. Adv Appl Physiol. 2018;3(1):14-25. doi: 10.11648/j.aap.20180301.13

    Copy | Download

  • @article{10.11648/j.aap.20180301.13,
      author = {Valery Nikolaevich Kiroy and Yelena Vlasovna Aslanyan and Dmitry Mikhailovich Lazurenko and Oleg Marksovich Bakhtin},
      title = {Specific Features of Perception of Semantically Equivalent Stimuli in the Verbal and Visual Form},
      journal = {Advances in Applied Physiology},
      volume = {3},
      number = {1},
      pages = {14-25},
      doi = {10.11648/j.aap.20180301.13},
      url = {https://doi.org/10.11648/j.aap.20180301.13},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.aap.20180301.13},
      abstract = {Response time and evoked potentials were registered for visual images related to two categories fruit and tableware as well as their verbal representations. The stimuli were presented randomly. The subjects were to attribute them regardless of the form (a word or image) to one of the categories. 11 female and 10 male subjects (average age 21.9±2.9 years) participated in the tests. 6 components of the evoked potentials were singled out: Р1 (Р66), N1 (N124), Р2 (Р180), N2 (N248), Р3 (Р331) and N3 (N456). Analysis showed that both female and male subjects demonstrated reliably longer response time for words as compared to those for corresponding images. For words, evoked potentials were registered in more complex configurations and with a shorter latency period for the early components (P1, N1) and longer latency period for the late ones (P2, N2, P3, N3). The evoked potential amplitude in response to verbal stimuli was smaller than that for visual ones. Evoked potential components in response to target stimuli (both images and words) had, in general, shorter latency. The amplitude of N1, Р2 and N2 components was lower, while that of P3 and N3 was higher for target stimuli rather than a non-target. The obtained results allow us to assume that evaluation of the type of information (verbal or visual) can be performed on early stages of stimulus perception (up to 120-150 ms). Further analysis includes either more detailed description of spatial features of the visual stimuli in parietal and occipital lobes or estimation of the semantics of a word employing the frontal and temporal areas. Decision-making on formulating a response barely depends on the manner of information presentation (visual and verbal).},
     year = {2018}
    }
    

    Copy | Download

  • TY  - JOUR
    T1  - Specific Features of Perception of Semantically Equivalent Stimuli in the Verbal and Visual Form
    AU  - Valery Nikolaevich Kiroy
    AU  - Yelena Vlasovna Aslanyan
    AU  - Dmitry Mikhailovich Lazurenko
    AU  - Oleg Marksovich Bakhtin
    Y1  - 2018/07/06
    PY  - 2018
    N1  - https://doi.org/10.11648/j.aap.20180301.13
    DO  - 10.11648/j.aap.20180301.13
    T2  - Advances in Applied Physiology
    JF  - Advances in Applied Physiology
    JO  - Advances in Applied Physiology
    SP  - 14
    EP  - 25
    PB  - Science Publishing Group
    SN  - 2471-9714
    UR  - https://doi.org/10.11648/j.aap.20180301.13
    AB  - Response time and evoked potentials were registered for visual images related to two categories fruit and tableware as well as their verbal representations. The stimuli were presented randomly. The subjects were to attribute them regardless of the form (a word or image) to one of the categories. 11 female and 10 male subjects (average age 21.9±2.9 years) participated in the tests. 6 components of the evoked potentials were singled out: Р1 (Р66), N1 (N124), Р2 (Р180), N2 (N248), Р3 (Р331) and N3 (N456). Analysis showed that both female and male subjects demonstrated reliably longer response time for words as compared to those for corresponding images. For words, evoked potentials were registered in more complex configurations and with a shorter latency period for the early components (P1, N1) and longer latency period for the late ones (P2, N2, P3, N3). The evoked potential amplitude in response to verbal stimuli was smaller than that for visual ones. Evoked potential components in response to target stimuli (both images and words) had, in general, shorter latency. The amplitude of N1, Р2 and N2 components was lower, while that of P3 and N3 was higher for target stimuli rather than a non-target. The obtained results allow us to assume that evaluation of the type of information (verbal or visual) can be performed on early stages of stimulus perception (up to 120-150 ms). Further analysis includes either more detailed description of spatial features of the visual stimuli in parietal and occipital lobes or estimation of the semantics of a word employing the frontal and temporal areas. Decision-making on formulating a response barely depends on the manner of information presentation (visual and verbal).
    VL  - 3
    IS  - 1
    ER  - 

    Copy | Download

  • Sections