Физиологические корреляты математической тревожности в состоянии покоя и при ожидании решения математических задач
Ключевые слова:
математическая тревожность, физиологические показатели, электрическая активность кожи, частота сердечных сокращений, вариабельность сердечного ритма
Аннотация
Введение. Математическая тревожность (МТ) – это чувство дискомфорта или страха при выполнении любых математических задач. Показано, что МТ влияет на успеваемость по математике. Люди с высокой математической тревожностью имеют умеренную реакцию мозговой активности даже не на кожи (ЭАК), частота сердечных сокращений (ЧСС) и вариабельность сердечного ритма являются чувствительными индикаторами стресса. Цель. Нашей целью было исследовать изменения физиологических показателей, таких как: электрическая активность кожи, частота сердечных сокращений и вариабельность сердечного ритма в состоянии покоя и при ожидании решения математических задач у участников с разным уровнем математической тревожности. Материалы и методы. В нашу выборку вошли 84 участника с высоким и низким уровнем математической тревожности. Экспериментальная процедура включала запись физиологических показателей в состоянии покоя, без специальных указаний и во время ожидания математических задач, когда участникам сообщалось, что далее они будут выполнять математические вычисления. Результаты. Исследование показало, что частота сердечных сокращений была значительно выше при ожидании решения математических задач у всех участников, независимо от уровня математической тревожности. Однако данный эффект был небольшой. Также были обнаружены различия в амплитуде электрической активности кожи у участников с разным уровнем математической тревожности. Заключение. Исследование показало, что частота сердечных сокращений чувствительна к такому эмоциональному состоянию, как ожидание решения математических задач, а изменение электрической активности кожи может выступать одним из индикаторов математической тревожности.
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Литература
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2. Núñez-Peña M.I., Suárez-Pellicioni M., Bono R. Effects of Math Anxiety on Student Success in Higher Education. International Journal of Educational Research. 2013;58:36–43. DOI: https://doi.org/10.1016/J.IJER.2012.12.004
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10. Matsepuro D.M., Esipenko Е.А., Terekhina О.V. Essential methods of math anxiety regulation. Nauchno-pedagogicheskoe obozrenie = Pedagogical Review. 2021;2(36):189–198. (in Russ.).
11. Cipora K., Santos F.H., Kucian K., Dowker A. Mathematics anxiety – where are we and where shall we go? PsyArXiv 2021. DOI: https://doi.org/10.31234/osf.io/2xpcg
12. Ganley C.M., Conlon R.A., McGraw A.L. et al. The Effect of Brief Anxiety Interventions on Reported Anxiety and Math Test Performance. Journal of Numerical Cognition. 2021;7:4–19. DOI: https://doi.org/10.5964/jnc.6065.
13. Mathematics anxiety: what is known and what is still to be understood / Ed. by Mammarella C.I., Caviola S., Dowker A. Publ. Routledge. London and New York. 238. DOI: https://doi.org/ 10.1080/00071005.2019.1622307
14. Lyons I.M., Beilock S.L. When Math Hurts: Math Anxiety Predicts Pain Network Activation in Anticipation of Doing Math. PLOS ONE. 2012;7(10):e48076. DOI: https://doi.org/10.1371/journal.pone.0048076
15. Boucsein W., Fowles D.C., Grimnes S. et. al. Publication recommendations for electrodermal measurements. Society for Psychophysiological Research Ad Hoc Committee on Electro-dermal Measures. Psychophysiology. 2012;49(8):1017–1034. DOI: https://doi.org/10.1111/j.1469-8986.2012.01384.x
16. Crider A. Personality and Electrodermal Response Lability: An Interpretation. Applied psychophysiology and biofeedback. 2008;33:141–148. DOI: https://doi.org/10.1007/s10484-008-9057-y.
17. Gertler J., Novotny S., Poppe A. et al. Neural correlates of non-specific skin conductance responses during resting state fMRI. NeuroImage. 2020;214:116721. DOI: https://doi.org/10.1016/ j.neuroimage.2020.116721
18. Sarchiapone M., Gramaglia C., Iosue M. et al. The association between electrodermal activity (EDA), depression and suicidal behaviour: A systematic review and narrative synthesis. BMC psychiatry. 2018;18(1):22. DOI: https://doi.org/10.1186/s12888-017-1551-4
19. Botvinick M.M., Rosen Z.B. Anticipation of cognitive demand during decision-making. Psychological Research. 2009;73(6):835–842. DOI: https://doi.org/10.1007/s00426-008-0197-8
20. Miu A., Heilman R., Houser D. Anxiety impairs decision-making: psychophysiological ev-idence from an Iowa gambling task. Biological psychology. 2008;77:353–358. DOI: https://doi.org/10.1016/j.biopsycho.2007.11.010.
21. Eidlin Levy H., Rubinsten O. Numbers (but not words) make math anxious individuals sweat: Physiological evidence. Biological psychology. 2021;165:108187. DOI: https://doi.org/10.1016/ j.biopsycho.2021.108187
22. Trotman G., Veldhuijzen van Zanten J., Davies J. et al. Associations between heart rate, perceived heart rate, and anxiety during acute psychological stress. Anxiety, Stress and Coping. 2019;32:1–17. DOI: https://doi.org/10.1080/10615806.2019.1648794.
23. Pham T., Lau Z. J., Chen S., Makowski D. Heart Rate Variability in Psychology: A Review of HRV Indices and an Analysis Tutorial. Sensors (Basel, Switzerland). 2021;21(12):3998. DOI: https://doi.org/10.3390/s21123998
24. Castaldo R., Montesinos L., Melillo P. et al. Ultra-short term HRV features as surrogates of short term HRV: A case study on mental stress detection in real life. BMC Medical Informatics and Decision Making. 2019;19. DOI: https://doi.org/10.1186/s12911-019-0742-y.
25. Shaffer F., Ginsberg J.P. An Overview of Heart Rate Variability Metrics and Norms. Frontiers in public health. 2017;5(258). DOI: https://doi.org/10.3389/fpubh.2017.00258
26. Francesco B., Maria Grazia B., Emanuele G. et al. Linear and nonlinear heart rate variabil-ity indexes in clinical practice. Computational and mathematical methods in medicine. 2012;219080. DOI: https://doi.org/10.1155/2012/219080
27. Strohmaier A.R., Schiepe-Tiska A., Reiss K.M. A Comparison of Self-Reports and Electrodermal Activity as Indicators of Mathematics State Anxiety. An Application of the Control-Value Theory. Frontline Learning Research. 2020;8(1):16–32. DOI: https://doi.org/10.14786/flr.v8i1.427
28. Qu Z., Chen J., Li B. et al. Measurement of High-School Students Trait Math Anxiety Using Neurophysiological Recordings During Math Exam. IEEE Access. 2020;8:57460-57471. DOI: https://doi.org/10.1109/ACCESS.2020.2982198.
29. Tang J., Su Y., Yao Y. et al. Respiratory Sinus Arrhythmia Mediates the Relation Between “Specific Math Anxiety” and Arithmetic Speed. Frontiers in psychology. 2021;12:615601. DOI: https://doi.org/10.3389/fpsyg.2021.615601
30. Hopko D.R., Mahadevan R., Bare R.L., Hunt M.K. The Abbreviated Math Anxiety Scale (AMAS): Construction, Validity, and Reliability. Assessment. 2003;10(2):178–182. DOI: https://doi.org/ 10.1177/1073191103010002008
31. Gramfort A., Luessi M., Larson E. et al. MEG and EEG data analysis with MNE-Python. Frontiers in Neuroscience. 2013;7(267). DOI: https://doi.org/10.3389/fnins.2013.00267
32. Makowski D., Pham T., Lau Z.J. et al. NeuroKit2: A Python toolbox for neurophysiological signal processing. Behavior research methods. 2021;53(4):1689–1696. DOI: https://doi.org/ 10.3758/s13428-020-01516-y
33. de Pedro-Carracedo J., Fuentes-Jimenez D., Ugena A.M., Gonzalez-Marcos A.P. Trans-cending Conventional Biometry Frontiers: Diffusive Dynamics PPG Biometry. Sensors. 2021;21(16):5661. DOI: https://doi.org/https://doi.org/10.3390/s21165661
2. Núñez-Peña M.I., Suárez-Pellicioni M., Bono R. Effects of Math Anxiety on Student Success in Higher Education. International Journal of Educational Research. 2013;58:36–43. DOI: https://doi.org/10.1016/J.IJER.2012.12.004
3. Sarkar A., Dowker A., Cohen Kadosh R. Cognitive enhancement or cognitive cost: trait-specific outcomes of brain stimulation in the case of mathematics anxiety. The Journal of neuroscience: the official journal of the Society for Neuroscience. 2014;34(50):16605–16610. DOI: https://doi.org/ 10.1523/JNEUROSCI.3129-14.2014
4. Malanchini M., Rimfeld K., Wang Z. et al. Genetic factors underlie the association between anxiety, attitudes and performance in mathematics. Translational Psychiatry. 2020;10(12). DOI: https://doi.org/10.1038/s41398-020-0711-3
5. Chang H., Beilock S.L. The math anxiety-math performance link and its relation to individual and environmental factors: A review of current behavioral and psychophysiological research. Current Opinion in Behavioral Sciences. 2016;10:33–38. DOI: https://doi.org/10.1016/j.cobeha.2016.04.011
6. Johnston-Wilder S., Brindley J., Dent P. A survey of mathematics anxiety and mathematical resilience among existing apprentices. London, Gatsby Charitable Foundation. 2014:48.
7. Dowker A., Sarkar A., Looi C.Y. Mathematics Anxiety: What Have We Learned in 60 Years? Frontiers in psychology. 2016;7(508). DOI: https://doi.org/10.3389/fpsyg.2016.00508
8. Organization for Economic Cooperation and Development. PISA 2012 results: Ready to learn: Students engagement, drive and self-beliefs (Vol. III). Paris, France. 2013. DOI: https://doi.org/ 10.1787/9789264201170-en
9. Pizzie R. G., Kraemer D. The Association between Emotion Regulation, Physiological Arousal, and Performance in Math Anxiety. Frontiers in psychology. 2021;12(639448). DOI: https://doi.org/10.3389/fpsyg.2021.639448
10. Matsepuro D.M., Esipenko Е.А., Terekhina О.V. Essential methods of math anxiety regulation. Nauchno-pedagogicheskoe obozrenie = Pedagogical Review. 2021;2(36):189–198. (in Russ.).
11. Cipora K., Santos F.H., Kucian K., Dowker A. Mathematics anxiety – where are we and where shall we go? PsyArXiv 2021. DOI: https://doi.org/10.31234/osf.io/2xpcg
12. Ganley C.M., Conlon R.A., McGraw A.L. et al. The Effect of Brief Anxiety Interventions on Reported Anxiety and Math Test Performance. Journal of Numerical Cognition. 2021;7:4–19. DOI: https://doi.org/10.5964/jnc.6065.
13. Mathematics anxiety: what is known and what is still to be understood / Ed. by Mammarella C.I., Caviola S., Dowker A. Publ. Routledge. London and New York. 238. DOI: https://doi.org/ 10.1080/00071005.2019.1622307
14. Lyons I.M., Beilock S.L. When Math Hurts: Math Anxiety Predicts Pain Network Activation in Anticipation of Doing Math. PLOS ONE. 2012;7(10):e48076. DOI: https://doi.org/10.1371/journal.pone.0048076
15. Boucsein W., Fowles D.C., Grimnes S. et. al. Publication recommendations for electrodermal measurements. Society for Psychophysiological Research Ad Hoc Committee on Electro-dermal Measures. Psychophysiology. 2012;49(8):1017–1034. DOI: https://doi.org/10.1111/j.1469-8986.2012.01384.x
16. Crider A. Personality and Electrodermal Response Lability: An Interpretation. Applied psychophysiology and biofeedback. 2008;33:141–148. DOI: https://doi.org/10.1007/s10484-008-9057-y.
17. Gertler J., Novotny S., Poppe A. et al. Neural correlates of non-specific skin conductance responses during resting state fMRI. NeuroImage. 2020;214:116721. DOI: https://doi.org/10.1016/ j.neuroimage.2020.116721
18. Sarchiapone M., Gramaglia C., Iosue M. et al. The association between electrodermal activity (EDA), depression and suicidal behaviour: A systematic review and narrative synthesis. BMC psychiatry. 2018;18(1):22. DOI: https://doi.org/10.1186/s12888-017-1551-4
19. Botvinick M.M., Rosen Z.B. Anticipation of cognitive demand during decision-making. Psychological Research. 2009;73(6):835–842. DOI: https://doi.org/10.1007/s00426-008-0197-8
20. Miu A., Heilman R., Houser D. Anxiety impairs decision-making: psychophysiological ev-idence from an Iowa gambling task. Biological psychology. 2008;77:353–358. DOI: https://doi.org/10.1016/j.biopsycho.2007.11.010.
21. Eidlin Levy H., Rubinsten O. Numbers (but not words) make math anxious individuals sweat: Physiological evidence. Biological psychology. 2021;165:108187. DOI: https://doi.org/10.1016/ j.biopsycho.2021.108187
22. Trotman G., Veldhuijzen van Zanten J., Davies J. et al. Associations between heart rate, perceived heart rate, and anxiety during acute psychological stress. Anxiety, Stress and Coping. 2019;32:1–17. DOI: https://doi.org/10.1080/10615806.2019.1648794.
23. Pham T., Lau Z. J., Chen S., Makowski D. Heart Rate Variability in Psychology: A Review of HRV Indices and an Analysis Tutorial. Sensors (Basel, Switzerland). 2021;21(12):3998. DOI: https://doi.org/10.3390/s21123998
24. Castaldo R., Montesinos L., Melillo P. et al. Ultra-short term HRV features as surrogates of short term HRV: A case study on mental stress detection in real life. BMC Medical Informatics and Decision Making. 2019;19. DOI: https://doi.org/10.1186/s12911-019-0742-y.
25. Shaffer F., Ginsberg J.P. An Overview of Heart Rate Variability Metrics and Norms. Frontiers in public health. 2017;5(258). DOI: https://doi.org/10.3389/fpubh.2017.00258
26. Francesco B., Maria Grazia B., Emanuele G. et al. Linear and nonlinear heart rate variabil-ity indexes in clinical practice. Computational and mathematical methods in medicine. 2012;219080. DOI: https://doi.org/10.1155/2012/219080
27. Strohmaier A.R., Schiepe-Tiska A., Reiss K.M. A Comparison of Self-Reports and Electrodermal Activity as Indicators of Mathematics State Anxiety. An Application of the Control-Value Theory. Frontline Learning Research. 2020;8(1):16–32. DOI: https://doi.org/10.14786/flr.v8i1.427
28. Qu Z., Chen J., Li B. et al. Measurement of High-School Students Trait Math Anxiety Using Neurophysiological Recordings During Math Exam. IEEE Access. 2020;8:57460-57471. DOI: https://doi.org/10.1109/ACCESS.2020.2982198.
29. Tang J., Su Y., Yao Y. et al. Respiratory Sinus Arrhythmia Mediates the Relation Between “Specific Math Anxiety” and Arithmetic Speed. Frontiers in psychology. 2021;12:615601. DOI: https://doi.org/10.3389/fpsyg.2021.615601
30. Hopko D.R., Mahadevan R., Bare R.L., Hunt M.K. The Abbreviated Math Anxiety Scale (AMAS): Construction, Validity, and Reliability. Assessment. 2003;10(2):178–182. DOI: https://doi.org/ 10.1177/1073191103010002008
31. Gramfort A., Luessi M., Larson E. et al. MEG and EEG data analysis with MNE-Python. Frontiers in Neuroscience. 2013;7(267). DOI: https://doi.org/10.3389/fnins.2013.00267
32. Makowski D., Pham T., Lau Z.J. et al. NeuroKit2: A Python toolbox for neurophysiological signal processing. Behavior research methods. 2021;53(4):1689–1696. DOI: https://doi.org/ 10.3758/s13428-020-01516-y
33. de Pedro-Carracedo J., Fuentes-Jimenez D., Ugena A.M., Gonzalez-Marcos A.P. Trans-cending Conventional Biometry Frontiers: Diffusive Dynamics PPG Biometry. Sensors. 2021;21(16):5661. DOI: https://doi.org/https://doi.org/10.3390/s21165661
2. Núñez-Peña M.I., Suárez-Pellicioni M., Bono R. Effects of Math Anxiety on Student Success in Higher Education. International Journal of Educational Research. 2013;58:36–43. DOI: https://doi.org/10.1016/J.IJER.2012.12.004
3. Sarkar A., Dowker A., Cohen Kadosh R. Cognitive enhancement or cognitive cost: trait-specific outcomes of brain stimulation in the case of mathematics anxiety. The Journal of neuroscience: the official journal of the Society for Neuroscience. 2014;34(50):16605–16610. DOI: https://doi.org/ 10.1523/JNEUROSCI.3129-14.2014
4. Malanchini M., Rimfeld K., Wang Z. et al. Genetic factors underlie the association between anxiety, attitudes and performance in mathematics. Translational Psychiatry. 2020;10(12). DOI: https://doi.org/10.1038/s41398-020-0711-3
5. Chang H., Beilock S.L. The math anxiety-math performance link and its relation to individual and environmental factors: A review of current behavioral and psychophysiological research. Current Opinion in Behavioral Sciences. 2016;10:33–38. DOI: https://doi.org/10.1016/j.cobeha.2016.04.011
6. Johnston-Wilder S., Brindley J., Dent P. A survey of mathematics anxiety and mathematical resilience among existing apprentices. London, Gatsby Charitable Foundation. 2014:48.
7. Dowker A., Sarkar A., Looi C.Y. Mathematics Anxiety: What Have We Learned in 60 Years? Frontiers in psychology. 2016;7(508). DOI: https://doi.org/10.3389/fpsyg.2016.00508
8. Organization for Economic Cooperation and Development. PISA 2012 results: Ready to learn: Students engagement, drive and self-beliefs (Vol. III). Paris, France. 2013. DOI: https://doi.org/ 10.1787/9789264201170-en
9. Pizzie R. G., Kraemer D. The Association between Emotion Regulation, Physiological Arousal, and Performance in Math Anxiety. Frontiers in psychology. 2021;12(639448). DOI: https://doi.org/10.3389/fpsyg.2021.639448
10. Matsepuro D.M., Esipenko Е.А., Terekhina О.V. Essential methods of math anxiety regulation. Nauchno-pedagogicheskoe obozrenie = Pedagogical Review. 2021;2(36):189–198. (in Russ.).
11. Cipora K., Santos F.H., Kucian K., Dowker A. Mathematics anxiety – where are we and where shall we go? PsyArXiv 2021. DOI: https://doi.org/10.31234/osf.io/2xpcg
12. Ganley C.M., Conlon R.A., McGraw A.L. et al. The Effect of Brief Anxiety Interventions on Reported Anxiety and Math Test Performance. Journal of Numerical Cognition. 2021;7:4–19. DOI: https://doi.org/10.5964/jnc.6065.
13. Mathematics anxiety: what is known and what is still to be understood / Ed. by Mammarella C.I., Caviola S., Dowker A. Publ. Routledge. London and New York. 238. DOI: https://doi.org/ 10.1080/00071005.2019.1622307
14. Lyons I.M., Beilock S.L. When Math Hurts: Math Anxiety Predicts Pain Network Activation in Anticipation of Doing Math. PLOS ONE. 2012;7(10):e48076. DOI: https://doi.org/10.1371/journal.pone.0048076
15. Boucsein W., Fowles D.C., Grimnes S. et. al. Publication recommendations for electrodermal measurements. Society for Psychophysiological Research Ad Hoc Committee on Electro-dermal Measures. Psychophysiology. 2012;49(8):1017–1034. DOI: https://doi.org/10.1111/j.1469-8986.2012.01384.x
16. Crider A. Personality and Electrodermal Response Lability: An Interpretation. Applied psychophysiology and biofeedback. 2008;33:141–148. DOI: https://doi.org/10.1007/s10484-008-9057-y.
17. Gertler J., Novotny S., Poppe A. et al. Neural correlates of non-specific skin conductance responses during resting state fMRI. NeuroImage. 2020;214:116721. DOI: https://doi.org/10.1016/ j.neuroimage.2020.116721
18. Sarchiapone M., Gramaglia C., Iosue M. et al. The association between electrodermal activity (EDA), depression and suicidal behaviour: A systematic review and narrative synthesis. BMC psychiatry. 2018;18(1):22. DOI: https://doi.org/10.1186/s12888-017-1551-4
19. Botvinick M.M., Rosen Z.B. Anticipation of cognitive demand during decision-making. Psychological Research. 2009;73(6):835–842. DOI: https://doi.org/10.1007/s00426-008-0197-8
20. Miu A., Heilman R., Houser D. Anxiety impairs decision-making: psychophysiological ev-idence from an Iowa gambling task. Biological psychology. 2008;77:353–358. DOI: https://doi.org/10.1016/j.biopsycho.2007.11.010.
21. Eidlin Levy H., Rubinsten O. Numbers (but not words) make math anxious individuals sweat: Physiological evidence. Biological psychology. 2021;165:108187. DOI: https://doi.org/10.1016/ j.biopsycho.2021.108187
22. Trotman G., Veldhuijzen van Zanten J., Davies J. et al. Associations between heart rate, perceived heart rate, and anxiety during acute psychological stress. Anxiety, Stress and Coping. 2019;32:1–17. DOI: https://doi.org/10.1080/10615806.2019.1648794.
23. Pham T., Lau Z. J., Chen S., Makowski D. Heart Rate Variability in Psychology: A Review of HRV Indices and an Analysis Tutorial. Sensors (Basel, Switzerland). 2021;21(12):3998. DOI: https://doi.org/10.3390/s21123998
24. Castaldo R., Montesinos L., Melillo P. et al. Ultra-short term HRV features as surrogates of short term HRV: A case study on mental stress detection in real life. BMC Medical Informatics and Decision Making. 2019;19. DOI: https://doi.org/10.1186/s12911-019-0742-y.
25. Shaffer F., Ginsberg J.P. An Overview of Heart Rate Variability Metrics and Norms. Frontiers in public health. 2017;5(258). DOI: https://doi.org/10.3389/fpubh.2017.00258
26. Francesco B., Maria Grazia B., Emanuele G. et al. Linear and nonlinear heart rate variabil-ity indexes in clinical practice. Computational and mathematical methods in medicine. 2012;219080. DOI: https://doi.org/10.1155/2012/219080
27. Strohmaier A.R., Schiepe-Tiska A., Reiss K.M. A Comparison of Self-Reports and Electrodermal Activity as Indicators of Mathematics State Anxiety. An Application of the Control-Value Theory. Frontline Learning Research. 2020;8(1):16–32. DOI: https://doi.org/10.14786/flr.v8i1.427
28. Qu Z., Chen J., Li B. et al. Measurement of High-School Students Trait Math Anxiety Using Neurophysiological Recordings During Math Exam. IEEE Access. 2020;8:57460-57471. DOI: https://doi.org/10.1109/ACCESS.2020.2982198.
29. Tang J., Su Y., Yao Y. et al. Respiratory Sinus Arrhythmia Mediates the Relation Between “Specific Math Anxiety” and Arithmetic Speed. Frontiers in psychology. 2021;12:615601. DOI: https://doi.org/10.3389/fpsyg.2021.615601
30. Hopko D.R., Mahadevan R., Bare R.L., Hunt M.K. The Abbreviated Math Anxiety Scale (AMAS): Construction, Validity, and Reliability. Assessment. 2003;10(2):178–182. DOI: https://doi.org/ 10.1177/1073191103010002008
31. Gramfort A., Luessi M., Larson E. et al. MEG and EEG data analysis with MNE-Python. Frontiers in Neuroscience. 2013;7(267). DOI: https://doi.org/10.3389/fnins.2013.00267
32. Makowski D., Pham T., Lau Z.J. et al. NeuroKit2: A Python toolbox for neurophysiological signal processing. Behavior research methods. 2021;53(4):1689–1696. DOI: https://doi.org/ 10.3758/s13428-020-01516-y
33. de Pedro-Carracedo J., Fuentes-Jimenez D., Ugena A.M., Gonzalez-Marcos A.P. Trans-cending Conventional Biometry Frontiers: Diffusive Dynamics PPG Biometry. Sensors. 2021;21(16):5661. DOI: https://doi.org/https://doi.org/10.3390/s21165661
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33. de Pedro-Carracedo J., Fuentes-Jimenez D., Ugena A.M., Gonzalez-Marcos A.P. Trans-cending Conventional Biometry Frontiers: Diffusive Dynamics PPG Biometry. Sensors. 2021;21(16):5661. DOI: https://doi.org/https://doi.org/10.3390/s21165661
Опубликован
2022-04-03
Как цитировать
Есипенко, Е., Мацепуро, Д., Архипова, О., Шамаков, В., & Будакова, А. (2022). Физиологические корреляты математической тревожности в состоянии покоя и при ожидании решения математических задач. Психология. Психофизиология, 15(1), 131-141. https://doi.org/10.14529/jpps220112
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Психофизиология
Copyright (c) 2022 Психология. Психофизиология
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