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Psychophysiological Study of the Basic and Superordinate Categorization of Objects Complicated by the Influence of the Previous Irrelevant Stimulus

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Abstract

On 20 healthy subjects, the behavioral characteristics and event-related potentials (ERPs) were studied during categorization of everyday objects at the basic (BC) and superordinate (SC) levels while the representation of the targeted object was preceded by an irrelevant mask, which may be congruent or incongruent with the target. It was shown that SC is faster than BC, and the incongruent mask slows down the categorization. The amplitude of the early caudal component N50 and frontal P50 depended on the interaction between categorization level and the mask congruence. When the stimulus and the mask were congruent, the amplitude of the early components was higher in SC, while under incongruent condition, on the contrary, the amplitude was higher in BC. The amplitudes of the P130 components in the occipital and temporal regions and N150 in the frontal one depended on the mask and were smaller when the mask was congruent to the target stimulus. The main effect of the categorization level was manifested as an increase in the frontal N400 and central late positivity (LP) (400–500 ms) in SC compared with BC. Thus, it was found that the categorization level influenced at both the early perceptual and late cognitive stages of processing. It is assumed that the effects of the categorization level on the early stage of processing may be associated with the unequal contribution of the parvo- and magnocellular pathways in SC and BC. It is supposed that the high amplitude of late components in the anterior cortex observed in SC may reflect the involvement of additional memory resources for the image semantic analysis.

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REFERENCES

  1. Abisheva, K.M., Categorization and its main principles, Vopr. Kognit. Linguist., 2013, vol. 2, no. 35, p. 21.

    Google Scholar 

  2. Westermann, G. and Mareschal, D., From perceptual to language-mediated categorization, Philos. Trans. R. Soc., B, 2014, vol. 369, no. 1634, p. 20120391.

  3. Fabre-Thorpe, M., The characteristics and limits of rapid visual categorization, Front. Psychol., 2011, vol. 2, p. 243.

    Article  Google Scholar 

  4. Large, M.E., Kiss, I., and McMullen, P.A., Electrophysiological correlates of object categorization: back to basics, Cogn. Brain Res., 2004, vol. 20, no. 3, p. 415.

    Article  Google Scholar 

  5. Banno, H. and Saiki, J., The processing speed of scene categorization at multiple levels of description: the superordinate advantage revisited, Perception, 2015, vol. 44, no. 3, p. 269.

    Article  Google Scholar 

  6. Ashtiani, M.N., Kheradpisheh, S.R., Masquelier, T., and Ganjtabesh, M., Object categorization in finer levels relies more on higher spatial frequencies and takes longer, Front. Psychol., 2017, vol. 8, p. 1261.

    Article  Google Scholar 

  7. Taniguchi, K., Kuraguchi, K., Takano, Y., and Itakura, S., Object categorization processing differs according to category level: comparing visual information between the basic and superordinate levels, Front. Psychol., 2020, vol. 11, p. 501.

    Article  Google Scholar 

  8. Rosch, E., Mervis, C.B., Gray, W.D., et al., Basic objects in natural categories, Cogn. Psychol., 1976, vol. 8, no. 3, p. 382.

    Article  Google Scholar 

  9. Tanaka, J., Luu, P., Weisbrod, M., and Kiefer, M., Tracking the time course of object categorization using event-related potentials, NeuroReport, 1999, vol. 10, no. 4, p. 829.

    Article  CAS  Google Scholar 

  10. Vanmarcke, S., Calders, F., and Wagemans, J., The time-course of ultrarapid categorization: the influence of scene congruency and top-down processing, I-Perseption, 2016, vol. 7, no. 5, p. 2041669516673384.

  11. Mace, M.J.M., Joubert, O.R., Nespoulous, J.L., and Fabre-Thorpe, M., The time-course of visual categorizations: you spot the animal faster than the bird, PLoS One, 2009, vol. 4, no. 6, p. e5927.

    Article  Google Scholar 

  12. Wu, C.T., Crouzet, S.M., Thorpe, S.J., and Fabre-Thorpe, M., At 120 msec you can spot the animal but you donʼt yet know itʼs a dog, J. Cogn. Neurosci., 2015, vol. 27, no. 1, p. 141.

    Article  Google Scholar 

  13. Kalenine, S., Bonthoux, F., and Borghi, A.M., How action and context priming influence categorization: a developmental study, Br. J. Dev. Psychol., 2009, vol. 27, part 3, p. 717.

    Article  Google Scholar 

  14. Roelofs, A. and Piai, V., Distributional analysis of semantic interference in picture naming, Q. J. Exp. Psychol., 2017, vol. 70, no. 4, p. 782.

    Article  Google Scholar 

  15. Leroy, A., Faure, S., and Spotorno, S., Reciprocal semantic predictions drive categorization of scene contexts and objects even when they are separate, Sci. Rep., 2020, vol. 10, no. 1, p. 8447.

    Article  CAS  Google Scholar 

  16. Gerasimenko, N.Yu., Kushnir, A.B., and Mikhailova, E.S., Masking effects of irrelevant visual information under conditions of basic and superordinate categorization of complex images, Hum. Physiol., 2019, vol. 45, no. 1, p. 1.

    Article  Google Scholar 

  17. Poncet, M., Fabre-Thorpe, M., and Chakravarthi, R., A simple rule to describe interactions between visual categories, Eur. J. Neurosci., 2020, vol. 52, no. 12, p. 4639.

    Article  Google Scholar 

  18. Eddy, M.D. and Holcomb, P.J., The temporal dynamics of masked repetition picture priming effects: manipulations of stimulus-onset asynchrony (SOA) and prime duration, Brain Res., 2010, vol. 1340, p. 24.

    Article  CAS  Google Scholar 

  19. Bognar, A., Csete, G., Németh, M., et al., Transcranial stimulation of the orbitofrontal cortex affects decisions about magnocellular optimized stimuli, Front. Neurosci., 2017, vol. 11, p. 234.

    Article  Google Scholar 

  20. Grill-Spector, K. and Weiner, K.S., The functional architecture of the ventral temporal cortex and its role in categorization, Nat. Rev. Neurosci., 2014, vol. 15, no. 8, p. 536.

    Article  CAS  Google Scholar 

  21. Rajalingham, R. and DiCarlo, J.J., Reversible inactivation of different millimeter-scale regions of primate IT results in different patterns of core object recognition deficits, Neuron, 2019, vol. 102, no. 2, p. 493.

    Article  CAS  Google Scholar 

  22. Margalit, E., Jamison, K.W., Weiner, K.S., et al., Ultra-high-resolution fMRI of human ventral temporal cortex reveals differential representation of categories and domains, J. Neurosci., 2020, vol. 40, no. 15, p. 3008.

    Article  CAS  Google Scholar 

  23. Weber, M., Thompson-Schill, S.L., Osherson, D., et al., Predicting judged similarity of natural categories from their neural representations, Neuropsychology, 2009, vol. 47, no. 3, p. 859.

    Article  Google Scholar 

  24. Connolly, A.C., Guntupalli, J.S., Gors, J., et al., The representation of biological classes in the human brain, J. Neurosci., 2012, vol. 32, no. 8, p. 2608.

    Article  CAS  Google Scholar 

  25. Huth, A.G., Nishimoto, S., Vu, A.T., and Gallant, J., A continuous semantic space describes the representation of thousands of object and action categories across the human brain, Neuron, 2012, vol. 76, no. 6, p. 1210.

    Article  CAS  Google Scholar 

  26. Cohen, M.A., Alvarez, G.A., Nakayama, K., and Konkle, T., Visual search for object categories is predicted by the representational architecture of high-level visual cortex, J. Neurophysiol., vol. 117, no. 1, p. 388.

  27. Yee, E. and Thompson-Schill, S.L., Putting concepts into context, Psychon. Bull. Rev., 2016, vol. 23, no. 4, p. 1015.

    Article  Google Scholar 

  28. Matheson, H.E., Garcea, F.E., and Buxbaum, L.J., Scene context shapes category representational geometry during processing of tools, Cortex, 2021, vol. 141, p. 1.

    Article  Google Scholar 

  29. Kauffmann, L., Bourgin, J., Guyader, N., and Peyrin, C., The neural bases of the semantic interference of spatial frequency-based information in scenes, J. Cogn. Neurosci., 2015, vol. 27, no. 12, p. 2394.

    Article  Google Scholar 

  30. Long, B., Yu, C.P., and Konkle, T., Mid-level visual features underlie the high-level categorical organization of the ventral stream, Proc. Natl. Acad. Sci. U.S.A., 2018, vol. 115, no. 38, p. E9015.

    Article  CAS  Google Scholar 

  31. Gibbons, H., Bachmann, O., and Stahl, J., The more you ignore me the closer I get: an ERP study of evaluative priming, Cogn. Affect. Behav. Neurosci., 2014, vol. 14, no. 4, p. 1467.

    Article  Google Scholar 

  32. Jost, K., Wendt, M., Luna-Rodriguez, A., et al., Strategic control over extent and timing of distractor-based response activation, J. Exp. Psychol. Learn. Mem. Cogn., 2017, vol. 43, no. 2, p. 326.

    Article  Google Scholar 

  33. Foxe, J.J. and Simpson, G.V., Flow of activation from V1 to frontal cortex in humans: a framework for defining “early” visual processing, Exp. Brain Res., 2002, vol. 142, no. 1, p. 139.

    Article  Google Scholar 

  34. Sysoeva, O.V., Ilyuchenok, I.R., and Ivanitsky, A.M., Rapid and slow brain systems of abstract and concrete words differentiation, Int. J. Psychophysiol., 2007, vol. 65, no. 3, p. 272.

    Article  Google Scholar 

  35. Zachariou, V., Del Giacco, A.C., Ungerleider, L.G., and Yue, X., Bottom-up processing of curvilinear visual features is sufficient for animate/inanimate object categorization, J. Vision, 2018, vol. 18, no. 12, p. 3.

    Article  Google Scholar 

  36. Kutas, M. and Federmeier, K.D., Thirty years and counting: finding meaning in the N400 component of the event-related brain potential (ERP), Annu. Rev. Psychol., 2011, vol. 62, no. 1, p. 621.

    Article  Google Scholar 

  37. Perez-Gay Juarez, F., Sicotte, T., Theriault, C., and Harnad, S., Category learning can alter perception and its neural correlates, PLoS One, 2019, vol. 14, no. 12. e0226000.

    Article  CAS  Google Scholar 

  38. Rabi, R., Joanisse, M.F., Zhu, T., and Minda, J.P., Cognitive changes in conjunctive rule-based category learning: an ERP approach, Cogn. Affect. Behav. Neurosci., 2018, vol. 18, no. 5, p. 1034.

    Article  Google Scholar 

  39. Codispoti, M., Ferrari, V., De Cesarei, A., and Cardinale, R., Implicit and explicit categorization of natural scenes, Prog. Brain Res., 2006, vol. 156, p. 53.

    Article  Google Scholar 

  40. Jiang, Z., Qu, Y., **ao, Y., et al., Comparison of affective and semantic priming in different SOA, Cogn. Process, 2016, vol. 17, no. 4, p. 357.

    Article  Google Scholar 

  41. Koifman, A.Ya., Solution of the categorization problem under different forms of categorical priming, Psikhologiya, 2011, vol. 8, no. 3, p. 102.

    Google Scholar 

  42. Ko, P.C., Duda, B., Husseya, E.P., et al., The temporal dynamics of visual object priming, Brain Cogn., 2014, vol. 91, p. 11.

    Article  Google Scholar 

  43. Li, B., Gao, C., and Wang, J., Electrophysiological correlates of masked repetition and conceptual priming for visual objects, Brain Behav., 2019, vol. 9, no. 10. e01415

    Article  Google Scholar 

  44. Freunberger, R., Klimesch, W., Doppelmayr, M., and Höller, Y., Visual P2 component is related to theta phase-locking, Neurosci. Lett., 2007, vol. 426, no. 3, p. 181.

    Article  CAS  Google Scholar 

  45. Eddy, M., Schmid, A., and Holcomb, P.J., Masked repetition priming and event-related brain potentials: a new approach for tracking the time-course of object perception, Psychophysiology, 2006, vol. 43, no. 6, p. 564.

    Article  Google Scholar 

  46. Eddy, M.D. and Holcomb, P.J., Electrophysiological evidence for size invariance in masked picture repetition priming, Brain Cogn., 2009, vol. 71, no. 3, p. 397.

    Article  Google Scholar 

  47. Folstein, J.R. and Van Petten, C., Influence of cognitive control and mismatch on the N2 component of the ERP: a review, Psychophysiology, 2008, vol. 45, no. 1, p. 152.

    Google Scholar 

  48. Ortells, J.J., Kiefer, M., Castillo, A., et al., The semantic origin of unconscious priming: behavioral and event-related potential evidence during category congruency priming from strongly and weakly related masked words, Cognition, 2016, vol. 146, p. 143.

    Article  Google Scholar 

  49. Bensmann, W., Vahid, A., Beste, C., and Stock, A.K., The intensity of early attentional processing, but not conflict monitoring, determines the size of subliminal response conflicts, Front. Hum. Neurosci., 2019, vol. 13, p. 53.

    Article  Google Scholar 

  50. Kalinin, S.A., Gerasimenko, N.Yu., Slavutskaya, A.V., and Mikhailova, E.S., Behavioral and ERP characteristics of recognition of complex images under forward masking: the influence of categorical similarity of target and masking stimuli, Hum. Physiol., 2014, vol. 40, no. 4, p. 355.

    Article  Google Scholar 

  51. Frings, C. and Groh-Bordin, C., Electrophysiological correlates of visual identity negative priming, Brain Res., 2007, vol. 1176, p. 82.

    Article  CAS  Google Scholar 

  52. Henson, R.N., Mouchlianitis, E., Matthews, W.J., and Kouider, S., Electrophysiological correlates of masked face priming, NeuroImage, 2008, vol. 40, no. 2, p. 884.

    Article  CAS  Google Scholar 

  53. Andres, A.J.D., Oram Cardy, J.E., and Joanisse, M.F., Congruency of auditory sounds and visual letters modulates mismatch negativity and P300 event-related potentials, Int. J. Psychophysiol., 2011, vol. 79, no. 2, p. 137.

    Article  Google Scholar 

  54. Carreiras, M., Perea, M., Gil-López, C., et al., Neural correlates of visual versus abstract letter processing in Roman and Arabic scripts, J. Cogn. Neurosci., 2013, vol. 25, no. 11, p. 1975.

    Article  Google Scholar 

  55. Drewes, J., Trommershäuser, J., and Gegenfurt-ner, K.R., Parallel visual search and rapid animal detection in natural scenes, J. Vision, 2011, vol. 11, no. 2, p. 20.

    Article  Google Scholar 

  56. Kojima, K., Brown, E.C., Matsuzaki, N., and Asano, E., Animal category-preferential gamma-band responses in the lower- and higher-order visual areas: intracranial recording in children, Clin. Neurophysiol., 2013, vol. 124, no. 12, p. 2368.

    Article  Google Scholar 

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Funding

The study was supported by the state budget under the state order of the Ministry of Education and Science of the Russian Federation for 2021–2023. Electrophysiological studies were carried out on the basis of the Center for the Collective Use of Scientific Equipment for Functional Brain Map** at the Institute of High-Technology and Scientific Research of the Russian Academy of Sciences (Moscow).

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  1. Institute of Higher Nervous Activity and Neurophysiology, Russian Academy of Sciences, Moscow, Russia

    N. Yu. Moshnikova, A. B. Kushnir & E. S. Mikhailova

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  1. N. Yu. Moshnikova
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  2. A. B. Kushnir
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  3. E. S. Mikhailova
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Correspondence to N. Yu. Moshnikova.

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All studies were carried out in accordance with the principles of biomedical ethics formulated in the Declaration of Helsinki of 1964 and its subsequent updates and were approved by the local bioethical committee of the Institute of Higher Nervous Activity and Neurophysiology of the Russian Academy of Sciences (Moscow).

Informed consent. Each participant in the study provided a voluntary written informed consent signed by him after explaining to him the potential risks and benefits, as well as the nature of the upcoming study.

Conflict of interest. The authors declare no obvious or potential conflict of interest related to the publication of this article.

Contribution of authors to the publication. N.Yu. Moshnikova and E.S. Mikhailova planned the study. A.B. Kushnir created the image library. N.Yu. Moshnikova programmed the experimental series in the E-Prime program. N.Yu. Moshnikova and A.B. Kushnir conducted experiments, processed and analyzed the data obtained. N.Yu. Moshnikova and E.S. Mikhailova wrote the article. E.S. Mikhailova edited the manuscript. All authors contributed and approved the final version of the manuscript.

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Translated by A. Deryabina

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Moshnikova, N.Y., Kushnir, A.B. & Mikhailova, E.S. Psychophysiological Study of the Basic and Superordinate Categorization of Objects Complicated by the Influence of the Previous Irrelevant Stimulus. Hum Physiol 48, 656–666 (2022). https://doi.org/10.1134/S036211972260028X

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