Hostname: page-component-cd9895bd7-dk4vv Total loading time: 0 Render date: 2024-12-26T19:55:59.891Z Has data issue: false hasContentIssue false
  • English
  • Français

Literacy effects on artificial grammar learning (AGL) with letters and colors: evidence from preschool and primary school children

Published online by Cambridge University Press:  15 July 2021

ANA PAULA SOARES Open the ORCID record for ANA PAULA SOARES [Opens in a new window] ,
ROSA SILVA ,
FREDERICA FARIA ,
MARIA SILVA SANTOS ,
HELENA MENDES OLIVEIRA  and
LUIS JIMÉNEZ
ANA PAULA SOARES*
Affiliation:
Research Group in Psycholinguistics, CIPsi, School of Psychology, University of Minho, Portugal
ROSA SILVA
Affiliation:
Research Group in Psycholinguistics, CIPsi, School of Psychology, University of Minho, Portugal
FREDERICA FARIA
Affiliation:
Research Group in Psycholinguistics, CIPsi, School of Psychology, University of Minho, Portugal
MARIA SILVA SANTOS
Affiliation:
Research Group in Psycholinguistics, CIPsi, School of Psychology, University of Minho, Portugal
HELENA MENDES OLIVEIRA
Affiliation:
Research Group in Psycholinguistics, CIPsi, School of Psychology, University of Minho, Portugal
LUIS JIMÉNEZ
Affiliation:
Faculty of Psychology, University of Santiago de Compostela, Spain
*
Address for correspondence: Ana Paula Soares, Human Cognition Lab, School of Psychology, University of Minho, Campus de Gualtar, 4710-057, Braga, Portugal. e-mail: asoares@psi.uminho.pt
Get access Rights & Permissions [Opens in a new window]

Abstract

Literacy affects many aspects of language and cognition, including the shift from a more holistic mode of processing to a more analytical part-based mode of processing. Here we examined whether this shift impacts the ability of preschool and primary school children to learn the rules underlying a finite-state grammar using an artificial grammar learning (AGL) paradigm implemented with either linguistic (letters) or non-linguistic (colors) materials to further examine if children’s AGL performance was modulated by type of stimuli. Both tasks involved a training phase in which half of the preschool children and half of the primary school children were exposed to a set of either letter or color strings without any information about the rules underlying the construction of those strings. Later, in the test phase, they were asked to decide whether a new set of letter or color strings conformed to those rules to test grammar learning. Results showed that only primary school children showed evidence of learning, and, importantly, only with colors. These findings seem to support the view that learning to read promotes reliance on smaller linguistic units that might hinder the ability of first-graders to learn the rules underlying finite-state grammars implemented with linguistic materials.

Keywords

literacyreadingartificial grammar learningstarting big hypothesispreliteratesliteratesdeveloping readersanalytical mode of processingletter processingcolors processing
Type
Article
Information
Language and Cognition , Volume 13 , Issue 4 , December 2021 , pp. 534 - 561
Check for updates
Copyright
© The Author(s), 2021. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

This study was conducted at the Psychology Research Centre (PSI/01662), University of Minho, and supported by Grant POCI-01-0145-FEDER-028212 from the Portuguese Foundation for Science and Technology and the Portuguese Ministry of Science, Technology and Higher Education through national funds, and co-financed by FEDER through COMPETE2020 under the PT2020 Partnership Agreement. The research was also funded by the Spanish Ministerio de Economía y Competitividad with a research grant to Luis Jiménez (PSI2015-70990-P).

References

references

Abrahamse, E. L., Jiménez, L., Verwey, W. B. & Clegg, B. A. (2010). Representing serial action and perception. Psychonomic Bulletin and Review 17(5), 603–623.CrossRefGoogle ScholarPubMed
Abrams, M. & Reber, A. S. (1988). Implicit learning: robustness in the face of psychiatric disorders. Journal of Psycholinguistic Research 17(5), 425–439.CrossRefGoogle ScholarPubMed
Alves Martins, M. (2007). Literacy practices in kindergartens and conceptualizations about written language among Portuguese preschool children. L1 – Educational Studies in Language and Literature 7(3), 147–171.CrossRefGoogle Scholar
Arnon, I. (2010). Starting big: the role of multi-word phrases in language learning and use. Stanford, CA: Stanford University.Google Scholar
Arnon, I. & Christiansen, M. H. (2017). The role of multiword building blocks in explaining L1–L2 differences. Topics in Cognitive Science 9(3), 621–636.CrossRefGoogle ScholarPubMed
Arnon, I. & Ramscar, M. (2012). Granularity and the acquisition of grammatical gender: how order-of-acquisition affects what gets learned. Cognition 122(3), 292–305.CrossRefGoogle ScholarPubMed
Berry, D. C. & Dienes, Z. (1993). Implicit learning: theoretical andempirical issues. Hillsdale, NJ: Erlbaum.Google Scholar
Bogaerts, L., Siegelman, N. & Frost, R. (2016). Splitting the variance of statistical learning performance: a parametric investigation of exposure duration and transitional probabilities. Psychological Bulletin Review 23(4), 1250–1256.CrossRefGoogle ScholarPubMed
Bowers, J., Vankov, I. & Ludwig, C. J. H. (2016). The visual system supports on-line translation invariance for object identification. Psychonomic Bulletin & Review 23(2), 432–438.CrossRefGoogle Scholar
Castro-Caldas, A. & Reis, A. (2003). The knowledge of orthography is a revolution in the brain. Reading and Writing 16(1/2), 81–97.CrossRefGoogle Scholar
Chang, G. Y. & Knowlton, B. J. (2004). Visual feature learning in artificial grammar classification. Journal of Experimental Psychology: Learning Memory and Cognition 30(3), 714–722.Google ScholarPubMed
Christiansen, M. H. (2019). Implicit-statistical learning: a tale of two literatures. Topics in Cognitive Science 11(3), 468–481.CrossRefGoogle ScholarPubMed
Christiansen, M. H. & Arnon, I. (2017). More than words: the role of multiword sequences in language learning and use. Topics in Cognitive Science 9, 542–551.CrossRefGoogle ScholarPubMed
Cohen, L., Dehaene, S., Naccache, L., Lehéricy, S., Dehaene-Lambertz, G., Hénaff, M. A. & Michel, F. (2000). The visual word form area: spatial and temporal characterization of an initial stage of reading in normal subjects and posterior split-brain patients. Brain 123(2), 291–307.CrossRefGoogle ScholarPubMed
Danziger, E. & Pederson, E. (1998). Through the looking glass: literacy, writing systems and mirror-image discrimination. Written Language & Literacy 1(2), 153–169.CrossRefGoogle Scholar
Dehaene, S. (2005). From monkey brain to human brain: a Fyssen Foundation symposium. Cambridge, MA: MIT Press.CrossRefGoogle Scholar
Dehaene, S. & Cohen, L. (2007). Cultural recycling of cortical maps. Neuron 56(2), 384–398.CrossRefGoogle ScholarPubMed
Dehaene, S. & Cohen, L. (2011). The unique role of the visual word form area in reading. Trends in Cognitive Sciences 15(6), 254–262.CrossRefGoogle ScholarPubMed
Dehaene, S., Cohen, L., Morais, J. & Kolinsky, R. (2015). Illiterate to literate: behavioural and cerebral changes induced by reading acquisition. Nature Reviews Neuroscience 16(4), 234–244.CrossRefGoogle ScholarPubMed
Dehaene, S., Cohen, L., Sigman, M. & Vinckier, F. (2005). The neural code for written words: a proposal. Trends in Cognitive Sciences 9(7), 335–341.CrossRefGoogle ScholarPubMed
Dehaene, S., Pegado, F., Braga, L. W., Ventura, P., Nunes, G., Jobert, A., Dehaene-Lambertz, D., Kolinsky, R., Morais, J. & Cohen, L. (2010). How learning to read changes the cortical networks for vision and language. Science 330(6009), 1359–1364.CrossRefGoogle ScholarPubMed
Dienes, Z. & Seth, A. (2010). Gambling on the unconscious: a comparison of wagering and confidence ratings as measures of awareness in an artificial grammar task. Consciousness and Cognition 19(2), 674–681.CrossRefGoogle Scholar
Don, A. J., Schellenberg, E. G., Reber, A. S., DiGirolamo, K. M. & Wang, P. P. (2003). Implicit learning in children and adults with Williams syndrome. Developmental Neuropsychology 23(1/2), 201–225.CrossRefGoogle ScholarPubMed
Duñabeitia, J. A., Dimitropoulou, M., Estévez, A. & Carreiras, M. (2013). The influence of reading expertise in mirror-letter perception: evidence from beginning and expert readers. Mind, Brain, and Education 7(2), 124–135.CrossRefGoogle ScholarPubMed
Duñabeitia, J. A., Molinaro, N., & Carreiras, M. (2011). Through the looking-glass: mirror reading. NeuroImage 54(4), 3004–3009.CrossRefGoogle ScholarPubMed
Duñabeitia, J. A., Orihuela, K. & Carreiras, M. (2014). Orthographic coding in illiterates and literates. Psychological Science 25(6), 1275–1280.CrossRefGoogle ScholarPubMed
Fernandes, T., Leite, I. & Kolinsky, R. (2016). Into the looking glass: literacy acquisition and mirror invariance in preschool and first-grade children. Child Development 87(6), 2008–2025.CrossRefGoogle ScholarPubMed
Grainger, J., Rey, A. & Dufau, S. (2008). Letter perception: from pixels to pandemonium! Trends in Cognitive Sciences 12(1), 381–387.CrossRefGoogle ScholarPubMed
Havron, N., Raviv, L. & Arnon, I. (2018). Literate and preliterate children show different learning patterns in an artificial language learning task. Journal of Cultural Cognitive Science 2(1/2), 21–33.CrossRefGoogle Scholar
Horan, W. P., Green, M. F., Knowlton, B. J., Wynn, J. K., Mintz, J. & Nuechterlein, K. H. (2008). Impaired implicit learning in schizophrenia. Neuropsychology 22(5), 606–617.CrossRefGoogle ScholarPubMed
IBM (2020). IBM SPSS Statistics for Windows, Version 27.0. Armonk, NY: IBM Corp.Google Scholar
Inquisit (2011). Inquisit 3 [Computer software]. Online <https://www.millisecond.com>..>Google Scholar
Ise, E., Arnoldi, C. J., Bartling, J. & Schulte-Körne, G. (2012). Implicit learning in children with spelling disability: evidence from artificial grammar learning. Journal of Neural Transmission 119(9), 999–1010.CrossRefGoogle ScholarPubMed
Janacsek, K., Fiser, J. & Nemeth, D. (2012). The best time to acquire new skills: age-related differences in implicit sequence learning across the human lifespan. Developmental Science 15(4), 496–505.CrossRefGoogle ScholarPubMed
Jiménez, L., Oliveira, H. M. & Soares, A. P. (2020). Surface features can deeply affect artificial grammar learning. Consciousness and Cognition 80, e102919.CrossRefGoogle ScholarPubMed
Kahta, S. & Schiff, R. (2016). Implicit learning deficits among adults with developmental dyslexia. Annals of Dyslexia 66(2), 235–250.CrossRefGoogle ScholarPubMed
Katan, P., Kahta, S., Sasson, A. & Schiff, R. (2017). Performance of children with developmental dyslexia on high and low topological entropy artificial grammar learning task. Annals of Dyslexia 67(2), 163–179.CrossRefGoogle ScholarPubMed
Kaufman, S. B., DeYoung, C. G., Gray, J. R., Jiménez, L., Brown, J. & Mackintosh, N. (2010). Implicit learning as an ability. Cognition 116(3), 321–340.CrossRefGoogle ScholarPubMed
Knowlton, B. J. & Squire, L. R. (1996). Artificial grammar learning depends on implicit acquisition of both abstract and exemplar-specific information. Journal of Experimental Psychology: Learning Memory and Cognition 22(1), 169–181.Google ScholarPubMed
Kolinsky, R. (2015). How learning to read influences language and cognition. In Pollatsek, A. & Treiman, R. (eds), The Oxford handbook of reading (pp. 377–393). New York: Oxford University Press.Google Scholar
Kolinsky, R., Verhaeghe, A., Fernandes, T., Mengarda, E. J., Grimm-Cabral, L. & Morais, J. (2011). Enantiomorphy through the looking-glass: literacy effects on mirror-image discrimination. Journal of Experimental Psychology: General 140(2), 210–238.CrossRefGoogle ScholarPubMed
Laasonen, M., Väre, J., Oksanen-Hennah, H., Leppämäki, S., Tani, P., Harno, H., Hokkanen, L., Pothos, E. & Cleeremans, A. (2014). Project DyAdd: implicit learning in adult dyslexia and ADHD. Annals of Dyslexia 64(1), 1–33.CrossRefGoogle ScholarPubMed
Lachmann, T., Khera, G., Srinivasan, N. & van Leeuwen, C. (2012). Learning to read aligns visual analytical skills with grapheme–phoneme mapping: evidence from illiterates. Frontiers in Evolutionary Neuroscience 4, e00008.CrossRefGoogle ScholarPubMed
Lachmann, T. & van Leeuwen, C. (2007). Paradoxical enhancement of letter recognition in developmental dyslexia. Developmental Neuropsychology 31(1), 61–77.CrossRefGoogle ScholarPubMed
Lukács, Á. & Kemény, F. (2015). Development of different forms of skill learning throughout the lifespan. Cognitive Science 39(2), 383–404.CrossRefGoogle ScholarPubMed
Malik-Moraleda, S., Orihuela, K., Carreiras, M. & Duñabeita, J. A. (2018). The consequences of literacy and schooling for parsing strings. Language, Cognition and Neuroscience 33(3), 293–299.CrossRefGoogle Scholar
Ministério da Educação (1997). Orientações curriculares para a educação pré-escolar [Curricular Guidelines for Preschool Education]. Lisboa: Editorial do Ministério da Educação.Google Scholar
Nakamura, K., Kuo, W. J., Pegado, F., Cohen, L., Tzeng, O. J. & Dehaene, S. (2012). Universal brain systems for recognizing word shapes and handwriting gestures during reading. Proceedings of the National Academy of Sciences of the United States of America 109(5), 20762–20767.CrossRefGoogle ScholarPubMed
Nigro, L., Jiménez-Fernández, G., Simpson, I. C. & Defior, S. (2016). Implicit learning of non-linguistic and linguistic regularities in children with dyslexia. Annals of Dyslexia 66(2), 1–17.CrossRefGoogle ScholarPubMed
Pacton, S., Perruchet, P., Fayol, M. & Cleeremans, A. (2001). Implicit learning out of the lab: the case of orthographic regularities. Journal of Experimental Psychology: General 130(3), 401–426.CrossRefGoogle ScholarPubMed
Paul, J. Z. and Grüter, T. (2016). Blocking effects in the learning of Chinese classifiers. Language Learning 66(4), 972–999.CrossRefGoogle Scholar
Pavlidou, E. V., Kelly, M. L. & Williams, J. M. (2010). Do children with developmental dyslexia have impairments in implicit learning? Dyslexia 16(2), 143–161.CrossRefGoogle ScholarPubMed
Pavlidou, E. V. & Williams, J. M. (2014). Implicit learning and reading: insights from typical children and children with developmental dyslexia using the artificial grammar learning (AGL) paradigm. Research in Developmental Disabilities 35(7), 1457–1472.CrossRefGoogle ScholarPubMed
Pavlidou, E. V., Williams, J. M. & Kelly, L. M. (2009). Artificial grammar learning in primary school children with and without developmental dyslexia. Annals of Dyslexia 59(1), 55–77.CrossRefGoogle ScholarPubMed
Payne, B. K. & Gawronski, B. (2010). A history of implicit social cognition: Where is it coming from? Where is it now? Where is it going? In Gawronski, B. & Payne, B. K. (eds), Handbook of implicit social cognition: measurement, theory, and application (pp. 1–15). New York: Guilford Press.Google Scholar
Pederson, E. (2003). Mirror-image discrimination among nonliterate, monoliterate, and biliterate Tamil subjects. Written Language and Literacy 6(1), 71–91.CrossRefGoogle Scholar
Pegado, F., Nakamura, K., Braga, L., Ventura, P., Nunes, Pallier, C. G., Jobert, A., Morais, J., Cohen, L., Kolinsky, R. & Dehaene, S. (2014). Literacy breaks mirror invariance for visual stimuli: a behavioral study with adult illiterates. Journal of Experimental Psychology: General 143(2), 887–894.CrossRefGoogle ScholarPubMed
Pegado, F., Nakamura, K., Cohen, L. & Dehaene, S. (2011). Breaking the symmetry: mirror discrimination for single letters but not for pictures in the Visual Word Form Area. NeuroImage 55(2), 742–749.CrossRefGoogle Scholar
Perea, M., Moret-Tatay, C. & Panadero, V. (2011). Suppression of mirror generalization for reversible letters: evidence from masked priming. Journal of Memory and Language 65(3), 237–246.CrossRefGoogle Scholar
Perruchet, P. & Pacteau, C. (1990). Synthetic grammar learning: Implicit rule abstraction or explicit fragmentary knowledge? Journal of Experimental Psychology: General 119(3), 264–275.CrossRefGoogle Scholar
Pothos, E. M. & Bailey, T. M. (2000). The role of similarity in artificial grammar learning. Journal of Experimental Psychology: Learning Memory and Cognition 26(4), 847–862.Google ScholarPubMed
Pothos, E. M. & Kirk, J. (2004). Investigating learning deficits associated with dyslexia. Dyslexia 10(1), 61–76.CrossRefGoogle ScholarPubMed
Pothos, E. M. & Wood, R. L. (2009). Separate influences in learning: evidence from artificial grammar learning with traumatic brain injury patients. Brain Research 1275, 67–72.CrossRefGoogle ScholarPubMed
Reber, A. S. (1967). Implicit learning of artificial grammars. Journal of Verbal Learning and Verbal Behavior 6(6), 855–863.CrossRefGoogle Scholar
Reber, A. S. (1989). Implicit learning and tacit knowledge. Journal of Experimental Psychology: General 118(3), 219–235.CrossRefGoogle Scholar
Reber, A. S. (1993). Implicit learning and tacit knowledge: an essay on the cognitive unconscious. New York: Oxford University Press.Google Scholar
Richler, J. J. & Gauthier, I. (2014). A meta-analysis and review of holistic face processing. Psychological Bulletin 140(5), 1281–1302.CrossRefGoogle ScholarPubMed
Saffran, J. R., Aslin, R. N. & Newport, E. L. (1996). Statistical learning by 8-month-old infants. Science 274(5294), 1926–1928.CrossRefGoogle ScholarPubMed
Saffran, J. R., Johnson, E. K., Aslin, R. N. & Newport, E. L. (1999). Statistical learning of tone sequences by human infants and adults. Cognition 70(1), 27–52.CrossRefGoogle ScholarPubMed
Schiff, R. & Katan, P. (2014). Does complexity matter? Meta-analysis of learner performance in artificial grammar tasks. Frontiers in Psychology 5, e01084.CrossRefGoogle ScholarPubMed
Schiff, R., Sasson, A., Star, G. & Kahta, S. (2017). The role of feedback in implicit and explicit artificial grammar learning: a comparison between dyslexic and non-dyslexic adults. Annals of Dyslexia 67(3), 333–355.CrossRefGoogle ScholarPubMed
Shanks, D. R., Johnstone, T. & Staggs, L. (1997). Abstraction processes in artificial grammar learning. Quarterly Journal of Experimental Psychology Section A: Human Experimental Psychology 50(1), 216–252.CrossRefGoogle ScholarPubMed
Siegelman, N. & Arnon, I. (2015). The advantage of starting big: learning from unsegmented input facilitates mastery of grammatical gender in an artificial language. Journal of Memory and Language 85, 60–75.CrossRefGoogle Scholar
Siegelman, N., Bogaerts, L. & Frost, R. (2017). Measuring individual differences in statistical learning: current pitfalls and possible solutions. Behavior Research Methods 49(2), 418–432.CrossRefGoogle ScholarPubMed
Soares, A. P., Gutiérrez-Domínguez, F., Vasconcelos, M. G., Oliveira, H. M., Tomé, D. & Jiménez, L. (2020). Not all words are equally acquired: transitional probabilities and instructions affect the electrophysiological correlates of statistical learning. Frontiers in Human Neuroscience 14, e577991.CrossRefGoogle ScholarPubMed
Soares, A. P., Lages, A., Oliveira, H. M. & Hernández-Cabrera, J. (2019). The mirror reflects more for ‘d’ than for ‘b’: right-asymmetry bias on the visual recognition of words containing reversal letters. Journal of Experimental Child Psychology 182, 18–37.CrossRefGoogle ScholarPubMed
Soares, A. P., Lages, A., Velho, M., Oliveira, H. M. & Hernández, J. (2021). The mirror reflects more for genial than for casual: right-asymmetry bias on the visual word recognition of words containing non-reversal letters. Reading and Writing 34, 1467–1489.CrossRefGoogle Scholar
Soares, A. P., Lousada, M. & Ramalho, M. (2021). Perturbação do desenvolvimento da linguagem: Terminologia, caracterização e implicações para os processos de alfabetização [Language development disorder: terminology, characterization and implications for literacy]. In Alves, R. A. & Leite, I. (eds), Alfabetização Baseada na Ciência (pp. 441–471). CAPES: Ministério da Educação.Google Scholar
Soares, A. P., Nunes, A., Martins, P. & Lousada, M. (2018). Do children with Specific Language Impairment (SLI) present implicit learning (IL) deficits? Evidence from an Artificial Grammar Learning (AGL) paradigm. Proceedings of the 4th IPLeiria’s International Health Congress, Leiria, Portugal. Online <https://doi.org/10.1186/s12913-018-3444-8>.Google Scholar
Soares, A. P., Oliveira, H. M., Comesaña, M. & Costa, A. S. (2018). Lexico-syntactic interactions in the resolution of relative clause ambiguities in a second language (L2): the role cognate status and L2 proficiency. Psicológica 39, 164–197.CrossRefGoogle Scholar
Soares, A. P., Oliveira, H. M., Ferreira, M., Comesaña, M., Macedo, A. F., Ferré, P., Acuña-Fariña, C., Hernández-Cabrera, J. & Fraga, I. (2019). Lexico-syntatic interactions during the processing of temporally ambiguous L2 relative clauses: an eye-tracking study with intermediate and advanced Portuguese–English bilinguals. PLoS ONE 14(5), e0216779.CrossRefGoogle ScholarPubMed
Soares, A. P., Velho, M. & Oliveira, H. M. (2020). The role of letter features on the consonant bias effect: evidence from masked priming. Acta Psychologica 210, e103171.CrossRefGoogle ScholarPubMed
Thomas, K., Hunt., R. H., Vizueta, N., Sommer, T., Durston, S., Yang, Y. & Worden, M. S. (2004). Evidence of developmental differences in implicit sequence learning: an fMRI study of children and adults. Journal of Cognitive Neuroscience 16(8), 1339–1357.CrossRefGoogle ScholarPubMed
Ventura, P., Fernandes, T., Cohen, L., Morais, J., Kolinsky, R. & Dehaene, S. (2013). Literacy acquisition reduces the influence of automatic holistic processing of faces and houses. Neuroscience Letters 554, 105–109.CrossRefGoogle ScholarPubMed
Winters, J. & Morin, O. (2019). From context to code: information transfer constrains the emergence of graphic codes. Cognitive Science 43(3), e12722.CrossRefGoogle ScholarPubMed
Witt, A. & Vinter, A. (2012). Artificial grammar learning in children: Abstraction of rules or sensitivity to perceptual features? Psychological Research 76(1), 97–110.CrossRefGoogle ScholarPubMed
Witt, A. & Vinter, A. (2017). Perceptual and positional saliencies influence children’s sequence learning differently with age and instructions at test. Quarterly Journal of Experimental Psychology 70(11), 2219–2233.CrossRefGoogle ScholarPubMed
Zwart, F. S., Vissers, C. T. H., Kessels, R. P. & Maes, J. H. (2017). Procedural learning across the lifespan: a systematic review with implications for atypical development. Journal of Neuropsychology 13(2), 149–182.CrossRefGoogle ScholarPubMed