JCERJournal of Contemporary Educational Research2208-84662208-8474Bio-Byword Scientific Publishing Pty. Ltd.10.26689/jcer.v8i6.7049ArticleLearning of Concepts: A Review of Relevant Advances Since 2010 and Its Inspirations for Teachinghttps://artdesignp.com/journal/JCER/8/6/10.26689/jcer.v8i6.7049WangZhong2024862024-06-20This article reviews the psychological and neuroscience achievements in concept learning since 2010 from the perspectives of individual learning and social learning, and discusses several issues related to concept learning, including the assistance of machine learning about concept learning. In terms of individual learning, current evidence shows that the brain tends to process concrete concepts through typical features (shared features); and for abstract concepts, semantic processing is the most important cognitive way. In terms of social learning, interpersonal neural synchrony (INS) is considered the main indicator of efficient knowledge transfer (such as teaching activities between teachers and students), but this phenomenon only broadens the channels for concept sources and does not change the basic mode of individual concept learning. Ultimately, this article argues that the way the human brain processes concepts depends on the concept’s own characteristics, so there are no “better” strategies in teaching, only more “suitable” strategies.

Hurd PD, 1969, New Directions in Teaching Secondary School Science, Rand McNally, Chicago.

Zimbardo PG, Johnson RL, Hamilton VM, 2017, Psychology: Core Concepts, 5th Edition, China Machine Press, Beijing, 164, 220, 351, 358.

Galotti KM, 2017, Cognitive Psychology: In and Out of the Laboratory, China Machine Press, Beijing, 84, 104, 113, 117, 118, 120

Taylor JE, Cortese A, Barron HC, et al., 2022, How Do We Generalize? NBDT, 2022(01). https://doi.org/10.51628/001C.27687

Zeithamova D, Mack ML, Braunlich K, et al., 2019, Brain Mechanisms of Concept Learning, The Journal of Neuroscience, 39(42): 8259–8266. https://doi.org/10.1523/JNEUROSCI.1166-19.2019

Woollams AM, 2012, Apples Are Not the Only Fruit: The Effects of Concept Typicality on Semantic Representation in the Anterior Temporal Lobe. Front. Hum. Neurosci, 2012(6). https://doi.org/10.3389/fnhum.2012.00085

Shtyrov Y, Efremov A, Kuptsova A, et al., 2023, Breakdown of Category-Specific Word Representations in a Brain-Constrained Neurocomputational Model of Semantic Dementia. Sci Rep, (13): 19572. https://doi.org/10.1038/s41598-023-41922-8

Iordan MC, Greene MR, Beck DM, et al., 2016, Typicality Sharpens Category Representations in Object-Selective Cortex. NeuroImage, 134(01): 170–179. https://doi.org/10.1016/j.neuroimage.2016.04.012

Mack ML, Preston AR, Love BC, et al., 2013, Decoding the Brain’s Algorithm for Categorization from Its Neural Implementation. Current Biology, 23(20): 2023–2027. https://doi.org/10.1016/j.cub.2013.08.035

Bowman CR, Zeithamova D, 2018, Abstract Memory Representations in the Ventromedial Prefrontal Cortex and Hippocampus Support Concept Generalization. The Journal of Neuroscience, 38(10): 2605–2614. https://doi.org/10.1523/JNEUROSCI.2811-17.2018

Apostel A, Hahn LA, Rose J, 2013, Jackdaws form Categorical Prototypes Based on Experience with Category Exemplars. Brain Struct Funct, (229): 593–608. https://doi.org/10.1007/s00429-023-02651-w

Bowman CR, Iwashita T, Zeithamova D, 2020, Model-Based fMRI Reveals Co-Existing Specific and Generalized Concept Representations. bioRxiv. https://doi.org/10.1101/2020.05.26.117507

Davis CP, Paz-Alonso PM, Altmann GTM, et al., 2022, Encoding and Inhibition of Arbitrary Episodic Context with Abstract Concepts. Mem Cogn, (50): 546–563. https://doi.org/10.3758/s13421-021-01212-y

Na Z, 2015, A Brief History of Arabia (Volume 1), The Commercial Press, Beijing.

Khodayarifard M, Pourhosein R, Pakdaman S, et al., 2023, Iranian Children’s Drawings of God: Demographic and Contextual Considerations, in When Children Draw Gods: A Multicultural and Interdisciplinary Approach to Children’s Representations of Supernatural Agents, Springer, Berlin.

Wang Z, Zhang Y, Wang P, et al., 2023, Using Drawing to Diagnose Children’s Scientific Concept Learning: A Study Based on Several Preferences of Children’s Scientific Drawing. PsyArXiv. https://doi.org/10.31234/osf.io/umxfb

Straube B, Wolk D, Chatterjee A, 2011, The Role of the Right Parietal Lobe in the Perception of Causality: A tDCS Study. Exp Brain Res, (215): 315–325. https://doi.org/10.1007/s00221-011-2899-1

Fugelsang JA, Roser ME, Corballis PM, et al., 2005, Brain Mechanisms Underlying Perceptual Causality. Cognitive Brain Research, 24(1): 41–47. https://doi.org/10.1016/j.cogbrainres.2004.12.001

Striem-Amit E, Wang X, Bi Y, et al., 2018, Neural Representation of Visual Concepts in People Born Blind. Nat Commun, (9): 5250. https://doi.org/10.1038/s41467-018-07574-3

Wang X, Wu W, Ling Z, et al., 2018, Organizational Principles of Abstract Words in the Human Brain. Cerebral Cortex, 28(12): 4305–4318. https://doi.org/10.1093/cercor/bhx283

Hoffman P, Lambon MAR, Rogers TT, 2013, Semantic Diversity: A Measure of Semantic Ambiguity Based on Variability in the Contextual Usage of Words. Behav Res, (45): 718–730. https://doi.org/10.3758/s13428-012-0278-x

Hoffman P, 2016, The Meaning of ‘Life’ and Other Abstract Words: Insights from Neuropsychology. Neuropsychology, 10(02): 317–343. https://doi.org/10.1111/jnp.12065

Crutch SJ, Warrington EK, 2010, The Differential Dependence of Abstract and Concrete Words Upon Associative and Similarity-Based Information: Complementary Semantic Interference and Facilitation Effects. Cognitive Neuropsychology, 27(01): 46–71. https://doi.org/10.1080/02643294.2010.491359

Zheng L, Chen C, Liu W, et al., 2018, Enhancement of Teaching Outcome Through Neural Prediction of the Students’ Knowledge State. Hum Brain Mapp, 39(7): 3046–3057. https://doi.org/10.1002/hbm.24059

Hu Y, Hu YY, Li XC, et al., 2017, Brain-to-Brain Synchronization Across Two Persons Predicts Mutual Prosociality. Social Cognitive and Affective Neuroscience, 12(12): 1835–1844, https://doi.org/10.1093/scan/nsx118

Pan Y, Cheng X, Hu Y, 2023, Three Heads Are Better Than One: Cooperative Learning Brains Wire Together When a Consensus is Reached. Cerebral Cortex, 33(04): 1155–1169. https://doi.org/10.1093/cercor/bhac127

Piaget J, 1964, Part I: Cognitive Development in Children: Piaget. Development and Learning. JRST, 02(03): 176–186. https://doi.org/10.1002/tea.3660020306

Lender A, Perdikis D, Gruber W, et al., 2023, Dynamics in Interbrain Synchronization While Playing a Piano Duet. New York Academy of Science, 1530(1): 124–137. https://doi.org/10.1111/nyas.15072

Müller V, Lindenberger U, 2022, Probing Associations Between Interbrain Synchronization and Interpersonal Action Coordination During Guitar Playing. New York Academy of Science, 1507(01): 146–161. https://doi.org/10.1111/nyas.14689

Sun B, Xiao W, Feng X, et al., 2020, Behavioral and Brain Synchronization Differences Between Expert and Novice Teachers When Collaborating with Students. Brain and Cognition, (139): 10553. https://doi.org/10.1016/j.bandc.2019.105513

Hu Y, Zhu M, Liu Y, et al., 2022, Musical Meter Induces Interbrain Synchronization During Interpersonal Coordination. ENEUO, 9(5). https://doi.org/10.1523/ENEURO.0504-21.2022

Zhou S, Xu X, He X, et al., 2023, Biasing the Neurocognitive Processing of Videos with the Presence of a Real Cultural Other. Cerebral Cortex, 33(04): 1090–1103. https://doi.org/10.1093/cercor/bhac122

Cheng X, Liu M, Pan Y, et al., 2021, The Teaching and Learning Brains: Interpersonal Neuroscience in Educational Research. Advances in Psychological Science, 29(11): 1993–2001. https://doi.org/10.3724/SP.J.1042.2021.01993

Novembre G, Knoblich G, Dunne L, et al., 2017, Interpersonal Synchrony Enhanced Through 20 Hz Phase-Coupled Dual Brain Stimulation. Social Cognitive and Affective Neuroscience, 12(04): 662–670. https://doi.org/10.1093/scan/nsw172

Long Y, Zhong M, Aili R, et al., 2022, Transcranial Direct Current Stimulation of the Right Anterior Temporal Lobe Changes Interpersonal Neural Synchronization and Shared Mental Processes. Brain Stimulation, 16(01): 28–39. https://doi.org/10.1016/j.brs.2022.12.009

Zhang Y, Hu Y, Ma F, et al., 2024, Interpersonal Educational Neuroscience: A Scoping Review of the Literature. Educational Research Review, (42): 100593. https://doi.org/10.1016/j.edurev.2024.100593

Klein F, Kohl SH, Lührs M, et al., 2023, From Lab to Life: Challenges and Perspectives of fNIRS for Hemodynamic Neurofeedback in Real-World Environments. PsyArXiv. https://doi.org/10.31234/osf.io/qdjfh

Zsuga J, Biro K, Papp C, et al., 2016, The “Proactive” Model of Learning: Integrative Framework for Model-Free and Model-Based Reinforcement Learning Utilizing the Associative Learning-Based Proactive Brain Concept. Behavioral Neuroscience, 130(1): 6–18. https://doi.org/10.1037/bne0000116

Jin L, 2019, Near Infrared Brain Imaging Representation for Daily Concept Evaluation, dissertation, Southeast University. https://doi.org/10.27014/d.cnki.gdnau.2019.003909

Clarke A, Tyler LK, 2014, Object-Specific Semantic Coding in Human Perirhinal Cortex. Journal of Neuroscience, 34(14): 4766–4775. https://doi.org/10.1523/JNEUROSCI.2828-13.2014

Kivisaari SL, Tyler LK, Monsch AU, et al., 2012, Medial Perirhinal Cortex Disambiguates Confusable Objects. BRAIN, 135(12): 3757–3769. https://doi.org/10.1093/brain/aws277

Wang L, Li M, Yang T, et al., 2022, Mathematics Meets Science in the Brain. Cerebral Cortex, 32(01): 123–136. https://doi.org/10.1093/cercor/bhab198

Amalric M, Dehaene S, 2016, Origins of the Brain Networks for Advanced Mathematics in Expert Mathematicians. PNAS, 113(18): 4909–4917. https://doi.org/10.1073/pnas.1603205113

Huang L, 2023, Chen Li’s Lecture Series on Frontiers of Psychological Science: Why I Gave Up Traditional Experimental Psychology After 20 Years, viewed April 10, 2024, http://www.psych.zju.edu.cn/2023/0410/c27619a2739655/page.htm

Zhou Z, 2016, Machine Learning, Tsinghua University Press, Beijing.

Mitchell TM, 1997, Machine Learning, McGraw-Hill Education, New York.

Schmidhuber J, 2014, Deep Learning in Neural Networks: An Overview. arXiv. https://doi.org/10.48550/arXiv.1404.7828

Rajkomar A, Dean J, Kohane I, et al., 2019, Machine Learning in Medicine. N Engl J Med, 380(14): 1347–1358. https://doi.org/10.1056/NEJMra1814259

Mack ML, Love BC, Preston AR, et al., 2016, Dynamic Updating of Hippocampal Object Representations Reflects New Conceptual Knowledge. PNAS, 113(46): 13203–13208. https://doi.org/10.1073/pnas.1614048113

Zhao H, Zhang T, Cheng T, et al., Neurocomputational Mechanisms of Young Children’s Observational Learning of Delayed Gratification. Cerebral Cortex, 33(10): 6063–6076. https://doi.org/10.1093/cercor/bhac484

Orrù G, Monaro M, Conversano C, et al., 2020, Machine Learning in Psychometrics and Psychological Research. Front. Psychol, (10): 2970. https://doi.org/10.3389/fpsyg.2019.02970

Fang SC, 2021, Towards Scientific Inquiry in Secondary Earth Science Classrooms: Opportunities and Realities. Int J of Sci and Math Educ, (19): 771–792. https://doi.org/10.1007/s10763-020-10086-6

OCED, 2017, PISA 2015 Assessment and Analytical Framework Science, Reading, Mathematic, Financial Literacy and Collaborative Problem Solving, PISA, OECD Publishing, Paris. https://doi.org/10.1787/9789264281820-en

Lei WX, 2016, Research on the Implementation Status and Improvement Strategies of Inquiry Teaching in Junior High School Chemistry, thesis, Chongqing Normal University. https://d.wanfangdata.com.cn/thesis/D00992676

Cao C, Zhang T, Xin T, 2024, The Effect of Reading Engagement on Scientific Literacy – An Analysis Based on the XGBoost Method. Front. Psychol, (15). https://doi.org/10.3389/fpsyg.2024.1329724

Çetin A, Özdemir OF, 2018, Mode-Method Interaction: The Role of Teaching Methods on The Effect of Instructional Modes on Achievements, Science Process Skills, and Attitudes Towards Physics. EURASIA J Math Sci Tech Ed, 14(05): 1815–1826. https://doi.org/10.29333/ejmste/85217

Oliver M, McConney A, Woods-McConney A, 2021, The Efficacy of Inquiry-Based Instruction in Science: a Comparative Analysis of Six Countries Using PISA 2015. Res Sci Educ, 51(Suppl 2): 595–616. https://doi.org/10.1007/s11165-019-09901-0

Jiang F, McComas WF, The Effects of Inquiry Teaching on Student Science Achievement and Attitudes: Evidence from Propensity Score Analysis of PISA Data. International Journal of Science Education, 37(03): 554–576. https://doi.org/10.1080/09500693.2014.1000426

Talvio M, Ferreira M, Meda L, 2024, Editorial: Innovations in Teaching and Learning: International Approaches in Developing Teacher Education and Curriculum for the Future. Front. Psychol, (15). https://doi.org/10.3389/fpsyg.2024.1403661

Cao Y, An S, Lavicza Z, et al., 2024, Editorial: Psychological Studies in the Teaching, Learning and Assessment of Mathematics. Front. Psychol, (15). https://doi.org/10.3389/fpsyg.2024.1389267