1
|
Schmitt A, Wollschläger R, Blanchette Sarrasin J, Masson S, Fischbach A, Schiltz C. Neuromyths and knowledge about intellectual giftedness in a highly educated multilingual country. Front Psychol 2023; 14:1252239. [PMID: 37928578 PMCID: PMC10623439 DOI: 10.3389/fpsyg.2023.1252239] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Accepted: 09/28/2023] [Indexed: 11/07/2023] Open
Abstract
Introduction Understanding brain functioning and intellectual giftedness can be challenging and give rise to various misconceptions. Nonetheless, there seems to be a widespread fascination and appetite for these subjects among the lay public and diverse professionals. The present study is the first to investigate general knowledge about the brain, neuromyths and knowledge about giftedness in a highly multilingual and educated country. Methods Starting from and extending two seminal studies on neuromyths, several novel statements on intellectual giftedness have been included in order to explore knowledge and misconceptions concerning giftedness. Our sample (N = 200) was composed of Luxembourgish education professionals, including students in educational science and cognitive psychology, thus allowing to analyze responses in general and according to training and professional profiles. Specifically, Group 1 consisted of teachers and futures teachers (n = 152). Group 2 consisted of other education professionals and psychology students (n = 48). Results Despite the size and the unbalanced distribution of the sample, our findings indicate a good level of general knowledge about the brain and learning (71.3% of correct responses in average) which does, however, not preclude the presence of the typically observed original neuromyths. Thus, we replicate the classical finding that misconceptions on Learning Styles (70% of error rate) and the Multiple Intelligence Theory (71.5% of error rate) are the most represented, both in (future and in-service) teachers and other education professionals. Moreover, the present sample also revealed a high presence of misconceptions on intellectual giftedness. Discussion Limitations and future directions are discussed.
Collapse
Affiliation(s)
- Anna Schmitt
- Department of Behavioral and Cognitive Sciences, Cognitive Science and Assessment Institute, Faculty of Humanities, Education and Social Sciences, University of Luxembourg, Esch-sur-Alzette, Luxembourg
- CRP-CPO, UR UPJV 7223, Université de Picardie Jules Verne, Amiens, France
| | - Rachel Wollschläger
- Luxembourg Centre for Educational Testing (LUCET), Faculty of Humanities, Education and Social Sciences, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Jérémie Blanchette Sarrasin
- Département de Didactique, Laboratory for Research in Neuroeducation, Université du Québec à Montréal, Montréal, QC, Canada
| | - Steve Masson
- Département de Didactique, Laboratory for Research in Neuroeducation, Université du Québec à Montréal, Montréal, QC, Canada
| | - Antoine Fischbach
- Luxembourg Centre for Educational Testing (LUCET), Faculty of Humanities, Education and Social Sciences, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Christine Schiltz
- Department of Behavioral and Cognitive Sciences, Cognitive Science and Assessment Institute, Faculty of Humanities, Education and Social Sciences, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| |
Collapse
|
2
|
Song S, Su M. The Intelligence Quotient-math achievement link: evidence from behavioral and biological research. Curr Opin Behav Sci 2022. [DOI: 10.1016/j.cobeha.2022.101160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
|
3
|
Chen X, Li B, Jia H, Feng F, Duan F, Sun Z, Caiafa CF, Solé-Casals J. Graph Empirical Mode Decomposition-Based Data Augmentation Applied to Gifted Children MRI Analysis. Front Neurosci 2022; 16:866735. [PMID: 35864986 PMCID: PMC9295389 DOI: 10.3389/fnins.2022.866735] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 05/27/2022] [Indexed: 12/05/2022] Open
Abstract
Gifted children and normal controls can be distinguished by analyzing the structural connectivity (SC) extracted from MRI data. Previous studies have improved classification accuracy by extracting several features of the brain regions. However, the limited size of the database may lead to degradation when training deep neural networks as classification models. To this end, we propose to use a data augmentation method by adding artificial samples generated using graph empirical mode decomposition (GEMD). We decompose the training samples by GEMD to obtain the intrinsic mode functions (IMFs). Then, the IMFs are randomly recombined to generate the new artificial samples. After that, we use the original training samples and the new artificial samples to enlarge the training set. To evaluate the proposed method, we use a deep neural network architecture called BrainNetCNN to classify the SCs of MRI data with and without data augmentation. The results show that the data augmentation with GEMD can improve the average classification performance from 55.7 to 78%, while we get a state-of-the-art classification accuracy of 93.3% by using GEMD in some cases. Our results demonstrate that the proposed GEMD augmentation method can effectively increase the limited number of samples in the gifted children dataset, improving the classification accuracy. We also found that the classification accuracy is improved when specific features extracted from brain regions are used, achieving 93.1% for some feature selection methods.
Collapse
Affiliation(s)
- Xuning Chen
- Department of Artificial Intelligence, Nankai University, Tianjin, China
| | - Binghua Li
- Department of Artificial Intelligence, Nankai University, Tianjin, China
| | - Hao Jia
- Department of Artificial Intelligence, Nankai University, Tianjin, China
| | - Fan Feng
- Department of Artificial Intelligence, Nankai University, Tianjin, China
| | - Feng Duan
- Department of Artificial Intelligence, Nankai University, Tianjin, China
- *Correspondence: Feng Duan
| | - Zhe Sun
- Computational Engineering Applications Unit, Head Office for Information Systems and Cybersecurity, RIKEN, Saitama, Japan
- Zhe Sun
| | - Cesar F. Caiafa
- Department of Artificial Intelligence, Nankai University, Tianjin, China
- Instituto Argentino de Radioastronomía, Consejo Nacional de Investigaciones Científicas y Técnicas – Centro Científico Tecnológico La Plata/Comisión de Investigaciones Científicas – Provincia de Buenos Aires/Universidad Nacional de La Plata, Villa Elisa, Argentina
| | - Jordi Solé-Casals
- Department of Artificial Intelligence, Nankai University, Tianjin, China
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
- Data and Signal Processing Research Group, University of Vic-Central University of Catalonia, Vic, Spain
- Jordi Solé-Casals
| |
Collapse
|
4
|
Kuhn T, Blades R, Gottlieb L, Knudsen K, Ashdown C, Martin-Harris L, Ghahremani D, Dang BH, Bilder RM, Bookheimer SY. Neuroanatomical differences in the memory systems of intellectual giftedness and typical development. Brain Behav 2021; 11:e2348. [PMID: 34651457 PMCID: PMC8613411 DOI: 10.1002/brb3.2348] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Revised: 07/14/2021] [Accepted: 08/14/2021] [Indexed: 11/17/2022] Open
Abstract
INTRODUCTION Studying neuro-structural markers of intellectual giftedness (IG) will inform scientific understanding of the processes helping children excel academically. METHODS Structural and diffusion-weighted MRI was used to compare regional brain shape and connectivity of 12 children with average to high average IQ and 18 IG children, defined as having IQ greater than 145. RESULTS IG had larger subcortical structures and more robust white matter microstructural organization between those structures in regions associated with explicit memory. TD had more connected, larger subcortical structures in regions associated with implicit memory. CONCLUSIONS It was found that the memory systems within brains of children with exceptional intellectual abilities are differently sized and connected compared to the brains of typically developing children. These different neurodevelopmental trajectories suggest different learning strategies. A spectrum of intelligence types is envisioned, facilitated by different ratios of implicit and explicit system, which was validated using a large external dataset.
Collapse
Affiliation(s)
- Taylor Kuhn
- Department ofPsychiatry and Biobehavioral Sciences, UCLA, 635 Charles E Young Dr, South, Los Angeles, CA, 90025, USA
| | - Robin Blades
- Department ofPsychiatry and Biobehavioral Sciences, UCLA, 635 Charles E Young Dr, South, Los Angeles, CA, 90025, USA
| | - Lev Gottlieb
- Department ofPsychiatry and Biobehavioral Sciences, UCLA, 635 Charles E Young Dr, South, Los Angeles, CA, 90025, USA
| | - Kendra Knudsen
- Department ofPsychiatry and Biobehavioral Sciences, UCLA, 635 Charles E Young Dr, South, Los Angeles, CA, 90025, USA
| | - Christopher Ashdown
- Department ofPsychiatry and Biobehavioral Sciences, UCLA, 635 Charles E Young Dr, South, Los Angeles, CA, 90025, USA
| | - Laurel Martin-Harris
- Department ofPsychiatry and Biobehavioral Sciences, UCLA, 635 Charles E Young Dr, South, Los Angeles, CA, 90025, USA
| | - Dara Ghahremani
- Department ofPsychiatry and Biobehavioral Sciences, UCLA, 635 Charles E Young Dr, South, Los Angeles, CA, 90025, USA
| | - Bianca H Dang
- Department ofPsychiatry and Biobehavioral Sciences, UCLA, 635 Charles E Young Dr, South, Los Angeles, CA, 90025, USA
| | - Robert M Bilder
- Department ofPsychiatry and Biobehavioral Sciences, UCLA, 635 Charles E Young Dr, South, Los Angeles, CA, 90025, USA
| | - Susan Y Bookheimer
- Department ofPsychiatry and Biobehavioral Sciences, UCLA, 635 Charles E Young Dr, South, Los Angeles, CA, 90025, USA
| |
Collapse
|