No evidence for a core deficit in developmental dyscalculia or mathematical learning disabilities.
Journal
Journal of child psychology and psychiatry, and allied disciplines
ISSN: 1469-7610
Titre abrégé: J Child Psychol Psychiatry
Pays: England
ID NLM: 0375361
Informations de publication
Date de publication:
06 2021
06 2021
Historique:
accepted:
07
01
2021
pubmed:
9
3
2021
medline:
16
10
2021
entrez:
8
3
2021
Statut:
ppublish
Résumé
Two hypotheses were tested regarding the characteristics of children with mathematical learning disabilities (MLD): (a) that children with MLD would have a 'core deficit' in basic number processing skills; and (b) that children with MLD would be at the end of a developmental continuum and have impairments in many cognitive skills. From a large sample (N = 1,303) of typically developing children, we selected a group definable as having MLD. The children were given measures of basic number processing and domain-general constructs. Differences between the observed sample and a simulated population were estimated using Cohen's d and Bayes factors. Receiver operating characteristic curves were plotted, and the area under the curve was computed to ascertain the diagnostic power of measures. Results suggest that the differences between the MLD and control group can be defined along with general characteristics of the population rather than assuming single or multiple 'core deficits'. None of the measures of interest exceeded the diagnostic power that could be derived via simulation from the dimensional characteristics of the general population. There is no evidence for core deficit(s) in MLD. We suggest that future research should focus on representative samples of typical populations and on carefully tested clinical samples confirming to the criteria of international diagnostic manuals. Clinical diagnoses require that MLD is persistent and resistant to intervention, so studies would deliver results less exposed to measurement fluctuations. Uniform diagnostic criteria would also allow for the easy cross-study comparison of samples overcoming a serious limitation of the current literature.
Sections du résumé
BACKGROUND
Two hypotheses were tested regarding the characteristics of children with mathematical learning disabilities (MLD): (a) that children with MLD would have a 'core deficit' in basic number processing skills; and (b) that children with MLD would be at the end of a developmental continuum and have impairments in many cognitive skills.
METHODS
From a large sample (N = 1,303) of typically developing children, we selected a group definable as having MLD. The children were given measures of basic number processing and domain-general constructs. Differences between the observed sample and a simulated population were estimated using Cohen's d and Bayes factors. Receiver operating characteristic curves were plotted, and the area under the curve was computed to ascertain the diagnostic power of measures.
RESULTS
Results suggest that the differences between the MLD and control group can be defined along with general characteristics of the population rather than assuming single or multiple 'core deficits'. None of the measures of interest exceeded the diagnostic power that could be derived via simulation from the dimensional characteristics of the general population.
CONCLUSIONS
There is no evidence for core deficit(s) in MLD. We suggest that future research should focus on representative samples of typical populations and on carefully tested clinical samples confirming to the criteria of international diagnostic manuals. Clinical diagnoses require that MLD is persistent and resistant to intervention, so studies would deliver results less exposed to measurement fluctuations. Uniform diagnostic criteria would also allow for the easy cross-study comparison of samples overcoming a serious limitation of the current literature.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
704-714Commentaires et corrections
Type : CommentIn
Informations de copyright
© 2021 Association for Child and Adolescent Mental Health.
Références
American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th edn). Washington, DC: Author.
Astle, D.E., & Fletcher-Watson, S. (2020). Beyond the core-deficit hypothesis in developmental disorders. Current Directions in Psychological Science, 29, 431-437.
Brainard, D.H. (1997). The psychophysics toolbox. Spatial Vision, 10, 433-436.
Bürkner, P.C. (2018). Advanced Bayesian multilevel modeling with the R package brms. R Journal, 10, 395-411.
Butterworth, B. (1999). The mathematical brain. London: Macmillan.
Butterworth, B. (2011). Foundational numerical capacities and the origins of dyscalculia. In Space, time and number in the brain (pp. 249-265). Amsterdam: Elsevier. https://doi.org/10.1016/B978-0-12-385948-8.00016-5
Cantlon, J.F., & Brannon, E.M. (2005). Semantic congruity affects numerical judgments similarly in monkeys and humans. Proceedings of the National Academy of Sciences of the United States of America, 102, 16507-16511.
Cattell, R.B., & Cattell, A.K.S. (1981). Measuring intelligence with the culture fair tests. Institute for Personality and Ability Testing [Italian edition: Misurare l’intelligenza con i test “Culture Fair”]. Florence, Italy: Giunti OS.
Caviola, S., Colling, L., Mammarella, I., & Szűcs, D. (2020). Predictors of mathematics in primary school: Magnitude comparison, verbal and spatial working memory measures. Developmental Science, 23, e12957. https://doi.org/10.1111/desc.12957
Cornoldi, C., & Cazzola, C. (2004). AC-MT 11-14: Test for assessing calculation and problem solving skills [“Test AC-MT 11-14 - Test di Valutazione delle Abilità di Calcolo e Problem Solving”]. Trento, Italy: Erickson.
Cornoldi, C., Lucangeli, D., & Bellina, M. (2012). AC-MT 6-11: Test for assessing calculation and problem solving skills [“Test AC-MT 6-11 - Test di Valutazione delle Abilità di Calcolo e Problem Solving”]. Trento, Italy: Erickson.
De Smedt, B., Noël, M.P., Gilmore, C., & Ansari, D. (2013). How do symbolic and non-symbolic numerical magnitude processing skills relate to individual differences in children’s mathematical skills? A review of evidence from brain and behavior. Trends in Neuroscience and Education, 2, 48-55.
Dehaene, S. (2011). The number sense: How the mind creates mathematics, revised and updated edition.
Devine, A., Soltesz, F., Nobes, A., Goswami, U., & Szucs, D. (2013). Gender differences in developmental dyscalculia depend on diagnostic criteria. Learning and Instruction, 27, 31-39.
Engle, R.W. (2002). Working memory capacity as executive attention. Current Directions in Psychological Science, 11, 19-23. https://doi.org/10.1111/1467-8721.00160
Geary, D.C. (2004). Mathematics and learning disabilities. Journal of Learning Disabilities, 37, 4-15.
Geary, D.C., Hamson, C.O., & Hoard, M.K. (2000). Numerical and arithmetical cognition: A longitudinal study of process and concept deficits in children with learning disability. Journal of Experimental Child Psychology, 77, 236-263.
Gelman, A., & Carlin, J. (2014). Beyond power calculations: Assessing type S (Sign) and type M (Magnitude) errors. Perspectives on Psychological Science, 9, 641-651.
Giofrè, D., Mammarella, I.C., & Cornoldi, C. (2013). The structure of working memory and how it relates to intelligence in children. Intelligence, 41, 396-406.
Hauser, M.D., Tsao, F., Garcia, P., & Spelke, E.S. (2003). Evolutionary foundations of number: Spontaneous representation of numerical magnitudes by cotton-top tamarins. Proceedings of the Royal Society B: Biological Sciences, 270, 1441-1446.
Hornung, C., Brunner, M., Reuter, R.A.P., & Martin, R. (2011). Children's working memory: Its structure and relationship to fluid intelligence. Intelligence, 39, 210-221. https://doi.org/10.1016/j.intell.2011.03.002
Izard, V., Dehaene-Lambertz, G., & Dehaene, S. (2008). Distinct cerebral pathways for object identity and number in human infants. PLoS Biology, 6, e11.
Lee and Wagenmakers, 2013Lee, M.D., & Wagenmakers, E.J. (2013). Bayesian cognitive modeling: A practical course. In Bayesian Cognitive Modeling: A Practical Course. https://doi.org/10.1017/CBO9781139087759
Mammarella, I.C., & Cornoldi, C. (2005). Difficulties in the control of irrelevant visuospatial information in children with visuospatial learning disabilities. Acta Psycholica, 118, 211-228. https://doi.org/10.1016/j.actpsy.2004.08.004
Mazzocco, M.M.M., Feigenson, L., & Halberda, J. (2011). Impaired acuity of the approximate number system underlies mathematical learning disability (dyscalculia). Child Development, 82, 1224-1237.
Mazzocco, M.M.M., & Myers, G.F. (2003). Complexities in identifying and defining mathematics learning disability in the primary school-age years. Annals of Dyslexia, 53, 218-253.
Morey, R.D., & Rouder, J.N. (2015). BayesFactor: Computation of Bayes factors for common designs. R package version 0.9.12-2. https://doi.org/10.1007/978-3-642-21037-2
Murphy, M.M., Mazzocco, M.M.M., Hanich, L.B., & Early, M.C. (2007). Cognitive characteristics of children with Mathematics Learning Disability (MLD) vary as a function of the cutoff criterion used to define MLD. Journal of Learning Disabilities, 40, 458-478.
Mussolin, C., Mejias, S., & Noël, M.P. (2010). Symbolic and nonsymbolic number comparison in children with and without dyscalculia. Cognition, 115, 10-25.
Passolunghi, M.C., & Siegel, L.S. (2004). Working memory and access to numerical information in children with disability in mathematics. Journal of Experimental Child Psychology, 88, 348-367.
Peng, P., Wang, C., & Namkung, J. (2018). Understanding the cognition related to mathematics difficulties: A meta-analysis on the cognitive deficit profiles and the bottleneck theory. Review of Educational Research, 88, 434-476.
Peters, L., & Ansari, D. (2019). Are specific learning disorders truly specific, and are they disorders? Trends in Neuroscience and Education, 17, 100115.
Piazza, M., Facoetti, A., Trussardi, A.N., Berteletti, I., Conte, S., Lucangeli, D., … & Zorzi, M. (2010). Developmental trajectory of number acuity reveals a severe impairment in developmental dyscalculia. Cognition, 116, 33-41.
Piazza, M., Izard, V., Pinel, P., Le Bihan, D., & Dehaene, S. (2004). Tuning curves for approximate numerosity in the human intraparietal sulcus. Neuron, 44, 547-555.
Price, G., & Ansari, D. (2013). Dyscalculia: characteristics, causes, and treatments. Numeracy, 6, Article 2.
Robin, X., Turck, N., Hainard, A., Tiberti, N., Lisacek, F., Sanchez, J.C., & Müller, M. (2011). pROC: An open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics, 12, Article 77.
Rubinsten, O., & Henik, A. (2009). Developmental dyscalculia: Heterogeneity might not mean different mechanisms. Trends in Cognitive Sciences, 13, 92-99.
Sartori, G., Job, R., & Tressoldi, P. (1995). Battery for the assessment of dyslexia and developmental dysorthography [“Batteria per la valutazione della dislessia edella disortografia evolutiva”]. Florence, Italy: Organizzazioni Speciali.
Schwenk, C., Sasanguie, D., Kuhn, J.T., Kempe, S., Doebler, P., & Holling, H. (2017). (Non-)symbolic magnitude processing in children with mathematical difficulties: A meta-analysis. Research in Developmental Disabilities, 64, 152-167.
Shalev, R.S. (1997). Neuropsychological aspects of developmental dyscalculia. Mathematical Cognition, 3, 105-120.
Szűcs, D. (2016). Subtypes and comorbidity in mathematical learning disabilities: Multidimensional study of verbal and visual memory processes is key to understanding. Progress in Brain Research, 227, 277-304.
Szucs, D., Devine, A., Soltesz, F., Nobes, A., & Gabriel, F. (2013). Developmental dyscalculia is related to visuo-spatial memory and inhibition impairment. Cortex, 49, 2674-2688.
Szucs, D., Devine, A., Soltesz, F., Nobes, A., & Gabriel, F. (2014). Cognitive components of a mathematical processing network in 9-year-old children. Developmental Science, 17, 506-524.
Szucs, D., & Ioannidis, J.P.A. (2017). Empirical assessment of published effect sizes and power in the recent cognitive neuroscience and psychology literature. PLoS Biology, 15, e2000797.
Tein, J.-Y., Coxe, S., & Cham, H. (2013). Statistical power to detect the correct number of classes in latent profile analysis. Structural Equation Modeling: A Multidisciplinary Journal, 20, 640-657.
Temple, C.M., & Sherwood, S. (2002). Representation and retrieval of arithmetical facts: Developmental difficulties. Quarterly Journal of Experimental Psychology Section A: Human Experimental Psychology, 55, 733-752.
Zhu, W., Zeng, N., & Wang, N. (2010). Sensitivity, specificity, accuracy, associated confidence interval and ROC analysis with practical SAS® implementations. Northeast SAS Users Group 2010: Health Care and Life Sciences.