Differences in working memory between gifted or talented students and community samplesA meta-analysis
- Elena Rodríguez-Naveiras 1
- Emilio Verche 2
- Pablo Hernández-Lastiri 3
- Rubens Montero 3
- África Borges 3
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1
Universidad Europea de Canarias
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2
Universidad Europea de Madrid
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3
Universidad de La Laguna
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ISSN: 0214-9915, 1886-144X
Año de publicación: 2019
Volumen: 31
Número: 3
Páginas: 255-262
Tipo: Artículo
Otras publicaciones en: Psicothema
Resumen
Antecedentes: los estudiantes superdotados y con talento tienen un funcionamiento diferencial en algunas componentes de las funciones ejecutivas como la memoria de trabajo. Este meta-análisis estudia las diferencias entre estudiantes con alta capacidad intelectual y con inteligencia promedio en memoria de trabajo. Método: un total de 17 artículos con 33 estudios diferenciados fueron analizados. Se empleó un modelo de efectos aleatorios, calculando el tamaño del efecto con g de Hedges. Las variables moderadoras se analizaron empleando una meta-regresión para las continuas y ANOVA para las categóricas. Resultados: los resultados muestran un tamaño del efecto de g+=0.80 (95% CI: 0.621, 0.976) y una alta heterogeneidad (Q(32)=196.966; p<.001; I2=83.754%). En los estudios que miden memoria de trabajo verbal, el tamaño del efecto fue de g+=0.969 (95% CI: 0.697, 1.241) y la heterogeneidad I2=83.416%. En los que evalúan memoria de trabajo visual, g+=0.674 (95% CI: 0.443, 0.906) y la heterogeneidad I2 =83.416%. El análisis de variables moderadoras identificó la forma de medir la memoria de trabajo como la única variable significativa. Conclusiones: existe un efecto significativo en favor de los estudiantes superdotados y con talento, tanto en memoria de trabajo verbal como visual, con influencia del procedimiento utilizado para medir memoria de trabajo.
Referencias bibliográficas
- Ackerman, P. L., Beier, M. E., & Boyle, M. O. (2005). Working memory and intelligence: The same or different constructs? Psychological Bulletin, 131(1), 30-60. https://doi.org/10.1037/0033-2909.131.1.30
- Alloway, T. P., & Elsworth, M. (2012). An investigation of cognitive skills and behavior in high ability students. Learning and Individual Differences, 22, 891-895.
- Alloway, T. P., & Alloway, R.G. (2010). Investigating the predictive roles of working memory and IQ in academic attainment. Journal of Experimental Child Psychology, 106, 20-29. https://doi.org/10.1016/j. jecp.2009.11.003
- Baddeley, A. (2003). Working memory: Looking back and looking forward. Nature Reviews Neuroscience, 4(10), 829-839. https://doi.org/10.1038/nrn1201
- Baddeley, A. D., & Hitch, G. (1974). Working memory. In H. Bower (Ed.), The psychology of learning and motivation: Vol. 8. Advances in research and theory (pp. 47-89). New York: Academic Press.
- Barbey, A. K., Colom, R., Paul, E. J., & Grafman, J. (2014). Architecture of fl uid intelligence and working memory revealed by lesion mapping. Brain Structure and Function, 219(2), 485-494. https://doi.org/10.1007/s00429-013-0512-z
- Borenstein, M., Hedges, L., Higgins, J., & Rothstein, H. (2005). Comprehensive Meta Analysis (Version 3) [Computer software]. Englewood, NJ: BioStat.
- Borges, A., Hernández Jorge, C., & Rodríguez-Naveiras, E. (2011). Evidence against the myth of adjustment problems of people with high intellectual abilities. Psicothema, 23, 362-367.
- Botella-Ausina, J., & Sánchez-Meca, J. (2015). Meta-análisis en Ciencias Sociales y de la Salud [Meta-analysis in social and health sciences]. Madrid: Síntesis.
- Calero, M. D., García-Martín, M. B., Jiménez, M. I., Kazén, M., & Araque, A. (2007). Self-regulation advantage for high-IQ children: Findings from a research study. Learning and Individual Differences, 17, 328-343.
- Chekaf, M., Gauvrit, N., Guida, A., & Mathy, F. (2018) Compression in working memory and its relationship with fluid intelligence. Cognitive Science, 42, 904-922. https://hal.univ-rennes2.fr/hal-01873321
- Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20, 37-46. doi http://doi.org/10.1023/A
- Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). New York: Academic Press.
- Colom, R., Abad, F. J., Rebollo, I., & Shih, P. C. (2005). Memory span and general intelligence: A latent-variable approach. Intelligence, 33, 623 -642. https:// doi.org/10.1016/j.intell.2005.05.006
- Dai, Y., & Chen, F. (2014). Paradigms of gifted education. A guide to theory-based. practice-focused research. Texas, Usa: Prufrock Press.
- Desco, M., Navas-Sánchez, F. J., Sánchez-González, J., Reig, S., Robles, O., Franco, C., … & Arango, C. (2011). Mathematically gifted adolescents use more extensive and more bilateral areas of the frontoparietal network tan controls during executive functioning and fluid reasoning tasks. NeuroImage, 57, 281-292.
- D’Esposito, M., & Postle, B. R. (2015). The Cognitive neuroscience of working memory. Annual Review of Psychology, 66, 115-142. https://doi.org/10.1146/annurev-psych-010814-015031
- Duval, S. (2005). The trim and fill method. In H. Rothstein, A. J. Sutton & M. Borenstein (Eds.), Publication bias in meta analysis: Prevention, assessment and adjustments (pp. 127-144). Chichester, England: Wiley.
- Engle, R. W. (2002). Working memory capacity as executive attention. Current Directions in Psychological Science, 11, 19-23.
- Fleiss, J. L. (1981). Statistical methods for rates and proportions (2nd ed.) New York: Wiley.
- Gagné, F. (2004). Transforming gifts into talents: The DMGT as a developmental theory. High Ability Studies, 15, 119-147.
- Geake J. G. (2009). Neuropsychological characteristics of academic and creative giftedness. In L.V. Shavinina (Ed), International handbook of giftedness (pp. 261-273). Dordrecht: Springer.
- Harder, B., Vialle, W., & Ziegler, A. (2014). Conceptions of giftedness and expertise put to the empirical test. High Ability Studies, 25(2), 83-120.
- Haring, I. L. (2016). The predictive value of working memory and creativity in average performing and gifted children. Universiteit Utrecht, Utrecht. Retrieved from https://dspace.library.uu.nl/handle/1874/335028
- Heller, K. A. (2004). Identification of gifted and talented students. Psychology Science, 46(3), 302-323.
- Hernández-Torrano, D., & Gutiérrez-Sánchez, M. (2014). The study of high intellectual ability in Spain: Analysis of the current situation. Revista de Educación, 364, 251-272. https://10.4438/1988-592X-RE2014-364-261
- Hoard, M. K. (2005). Mathematical cognition in gifted children: Relationships between working memory, strategy use, and fluid intelligence. Dissertation abstracts international. B. The sciences and engineering, 67(9-B), 5443.
- Howard, S. J., Johnson, J., & Pascual-Leone, J. (2013). Measurement of mental attention: Assessing a cognitive component underlying performance on standardized intelligence tests. Psychological Test and Assessment Modeling, 55(3), 250-273.
- Jastrzębskia, J., Ciechanowskab, I., & Chuderskib, A. (2018). The strong link between fluid intelligence and working memory cannot be explained away by strategy use. Intelligence, 66, 44-53. https://doi. org/10.1016/j.intell.2017.11.002
- Johnson, J., Im-Bolter, N., & Pascual-Leone, J. (2003). Development of mental attention in gifted and mainstream children: The role of mental capacity, inhibition, and speed of processing. Child Development, 74(6), 1594-1614. https://doi.org/10.1046/j.1467-8624.2003.00626.x
- Khosravi-Fard, E., Keelor, J. L., Akbarzadeh-Bagheban, A. R., & Keith, R.W. (2016). Comparison of the Rey Auditory Verbal Learning Test (RAVLT) and digit test among typically achieving and gifted students. Iranian Journal of Child Neurology, 10(2), 26-37.
- Kormmann, J., Zettler, I., Kammerer, Y., Gerjets, P., & Trautwein, U. (2015). What characterizes children nominated as gifted by teachers? A closer consideration of working memory and intelligence. High Ability Studies, 26(1), 75-92. http://dx.doi.org/10.1080/13598139.201 5.1033513
- Leikin, M., Paz-Baruch, N., & Leikin, R. (2013). Memory abilities in generally gifted and excelling-in-mathematics adolescents. Intelligence, 41, 566-578.
- Leikin, R., Paz-Baruch, N., & Leikin, M. (2014). Cognitive characteristics of students with superior performance in mathematics. Journal of Individual Differences, 35(3), 119-129. https://10.1027/1614-0001/a000140
- Matthews, D. J., & Dai, D. Y. (2014). Gifted education: changing conceptions, emphases and practice. International Studies in Sociology of Education, 24(4), 335-353. https://doi.org/10.1080/0962 0214.2014.979578
- Navarro, J. I., Ramiro, P., López, J. M., & Aguilar, M. (2006). Mental attention in gifted and nongifted children. European Journal of Psychology of Education, XXI(4), 401-411.
- Paz-Baruch, N., Leikin, R., & Leikin, M. (2016). Visual processing in generally gifted and mathematically excelling adolescents. Journal for the Education of the Gifted, 39(3), 237-258. https://doi. org/10.1177/0162353216657184
- Pérez, J., Borges, A., & Rodríguez-Naveiras, E. (2017) Knowledge and myths about high abilities. Talincrea, 4(1), 40-51.
- Pfeiffer, S. I. (2012). Current perspectives on the identification and assessment of gifted students. Journal of Psychoeducational Assessment, 30, 3-9.
- Raven, J., Raven, J. C., & Court, J. H. (2000). Standard Progressive Matrices. Oxford: Oxford Psychologists Press.
- Sacuzzo, D. P., Johnson, N. E., & Guertin, T. L. (1994). Information processing in gifted versus nongifted african american, latino, filipino, and white children: Speeded versus nonspeeded paradigms. Intelligence, 19, 219-243.
- Sastre-Riba, S., & Viana-Sanz, L. (2016). Executive functions and high intellectual capacity. Revista de Neurología, 62 (Supl 1), S65-S71.
- Segalowitz, S. J., Unsal, A., & Dywan, J. (1992). Cleverness and wisdom in 12-year-olds: Electriphysiological evidence for late maturation of the frontal lobe. Developmental Neuropsychology, 8(2 & 3), 279-298.
- Sternberg, R. J. (1985). Beyond I. Q.: A triarchic theory of intelligence. Nueva York: Cambridge University Press.
- Stroup, D. F., Berlin, J. A., Morton, S. C., Olkin, I., Williamson, G. D., Rennie, D., & Thacker, S. B. (2000). Meta-analysis of observational studies in epidemiology. Journal of the American Medical Association, 283(15), 2008-2012. http://doi.org/10.1001/jama.283.15.2008
- Swanson, H. L. (2005). Cognitive processes that underlie mathematical precociousness in young children. Journal of Experimental Child Psychology, 93, 239-264.
- Redick, T. S., Shipstead, Z., Meier, M. E., Montroy, J. J., Hicks, K. L., Unsworth, N.,… Randall W. (2016). Cognitive predictors of a common multitasking ability: Contributions from working memory, attention control, and fluid intelligence. Journal of Experimental Psychology: General, 145(11), 1473-1492. http://dx.doi.org.accedys2.bbtk.ull. es/10.1037/xge0000219
- Rey-Mermet, A., Gade, M., Souza, A., von Bastian, C.C., & Oberauer, (2019). Is executive control related to working memory capacity and fluid intelligence? Journal of Experimental Psychology: General. Advance online publication. https://doi.org/10.1037/xge0000593
- Terman, L. (1925). Mental and physical traits of a thousand grafted children. Stanford, CA: Stanford University Press.
- Tirapu-Ustárroz, J., & Muñoz-Céspedes, J. M. (2005). Memory and the executive functions. Revista de Neurología, 41(8), 475-484. https://doi. org/10.33588/rn.4108.2005240
- Touron, J., & Touron, M. (2011). The center for Talented Youth Identification model: A review of the literature. Talent Development & Excellence, 3(2), 187-202.
- Van Tassel-Baska, J. (2013). Curriculum for the gifted. A commitment to excellence. Gifted Child Today, 36, 213-240.
- Wechsler, D. (1991). Wechsler Intelligence Scale for Children-Third Edition manual. San Antonio, TX: The Psychological Corporation.
- Wongupparaja, P., Sumicha, A., Wickensd, M., Kumaria, V., & Morris, R. G. (2018). Individual differences in working memory and general intelligence indexed by P200 and P300: A latent variable model. Biological Psychology, 13, 96-105. https://doi.org/10.1016/j.biopsycho.2018.10.009
- Yuan, K., Steedle, J., Shavelson, R., Alonzo, A., & Oppezzo, M. (2006). Working memory, fluid intelligence, and science learning. Educational Research Review, 1(2), 83-98. https://doi.org/10.1016/j.edurev.2006.08.005