Predictors of individual differences in category learning in 6–10-year-old children
DOI:
https://doi.org/10.54359/nens3357Abstract
This study examines the relationship between individual differences in categorical learning and two cognitive factors – mental attention capacity and verbal flexibility – in primary school students (grades 1–3, N = 60). Age-related differences were observed across all measures: older children demonstrated higher categorical learning performance, greater attention capacity, and increased verbal flexibility. However, when controlling for age, only the positive association between categorical learning performance and mental attention capacity remained statistically significant. To further investigate the mechanisms underlying categorical learning, an eye-tracking analysis was conducted to compare attention allocation strategies between children and adults (N = 30). The findings indicated that children tended to employ a distributed attention strategy, whereas adults predominantly used a selective attention strategy during the learning task. The findings have implications for the development of predictive models of learning based on stable individual characteristics of cognitive functioning.
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Arsalidou M., Im-Bolter N. Why parametric measures are critical for understanding typical and atypi-cal cognitive development. Brain Imaging and Behavior, 2017, 11(4), 1214–1224. DOI:10.1007/s11682-016-9592-8
Arsalidou M., Pascual-Leone J., Johnson J. Misleading cues improve developmental assessment of working memory capacity: The color matching tasks. Cognitive Development, 2010, 25(3), 262–277. DOI:10.1016/j.cogdev.2010.07.001
Ashby F.G., Maddox W.T. Human category learning. Annual Review of Psychology, 2005, 56(1), 149–178. DOI:10.1146/annurev.psych.56.091103.070217
Baddeley A. The episodic buffer: a new component of working memory?. Trends in cognitive scienc-es, 2000, 4(11), 417–423. DOI: 10.1016/S1364-6613(00)01538-2
Barker J.E., Semenov A.D., Michaelson L., Provan L.S., Snyder H.R., Munakata Y. Less-structured time in children's daily lives predicts self-directed executive functioning. Frontiers in psychology, 2014, No. 5, 85789. DOI:10.3389/fpsyg.2014.00593
Blanco N.J., Turner B.M., Sloutsky V.M. The benefits of immature cognitive control: How distribut-ed attention guards against learning traps. Journal of Experimental Child Psychology, 2023, No. 226, 105548. DOI:10.1016/j.jecp.2022.105548
Brashears B.N., Minda J.P. The effects of feature verbalizablity on category learning. Proceedings of the Annual Meeting of the Cognitive Science Society, 2020, No. 42, 655–660.
Cowan N., Elliott E.M., Saults J.S., Morey C.C., Mattox S., Hismjatullina A., Conway A.R.A. On the capacity of attention: Its estimation and its role in working memory and cognitive aptitudes. Cogni-tive psychology, 2005, 51(1), 42–100. DOI:10.1016/j.cogpsych.2004.12.001
Deng W.S., Sloutsky V.M. Selective attention, diffused attention, and the development of categoriza-tion. Cognitive psychology, 2016, No. 91, 24–62. DOI:10.1016/j.cogpsych.2016.09.002
Ericsson K.A., Kintsch W. Long-term working memory. Psychological review, 1995, 102(2), 211–245. DOI:10.1037/0033-295X.102.2.211
Hoffman A.B., Rehder B. The costs of supervised classification: The effect of learning task on con-ceptual flexibility. Journal of Experimental Psychology: General, 2010, 139(2), 319–340. DOI:10.1037/a0019042
Koren R., Kofman O., Berger A. Analysis of word clustering in verbal fluency of school-aged chil-dren. Archives of Clinical Neuropsychology, 2005, 20(8), 1087–1104. DOI:10.1016/j.acn.2005.06.012
Kotov A.A., Kotova T.N. Ehffekt interferiruyushchei zadachi na otdel'nykh ehtapakh kategori-al'nogo naucheniya. Psikhologicheskii zhurnal, 2018, 39(1), 57–69. (in Russian) DOI:10.7868/S0205959218010063
Lloyd K., Sanborn A., Leslie D., Lewandowsky S. Why higher working memory capacity may help you learn: Sampling, search, and degrees of approximation. Cognitive Science, 2019, 43(12), e12805. DOI:10.1111/cogs.12805
Minda J.P., Miles S.J. The influence of verbal and nonverbal processing on category learning. Psy-chology of learning and motivation, 2010, No. 52, 117–162. DOI:10.1016/S0079-7421(10)52003-6
Nosofsky R.M., Meagher B.J., Kumar P. Contrasting exemplar and prototype models in a natural-science category domain. Journal of Experimental Psychology: Learning, Memory, and Cognition, 2022, 48(12), 1970–1994. DOI:10.1037/xlm0001069
Pascual-Leone J. A mathematical model for the transition rule in Piaget's developmental stages. Acta psychologica, 1970, No. 32, 301–345. DOI:10.1016/0001-6918(70)90108-3
Sewell D.K., Lewandowsky S. Attention and working memory capacity: insights from blocking, highlighting, and knowledge restructuring. Journal of Experimental Psychology: General, 2012, 141(3), 444–469. DOI:10.1037/a0026560
Sloutsky V.M. From perceptual categories to concepts: What develops?. Cognitive science, 2010, 34(7), 1244–1286. DOI:10.1111/j.1551-6709.2010.01129.x
Snyder H.R., Munakata Y. Becoming self-directed: Abstract representations support endogenous flexibility in children. Cognition, 2010, 116(2), 155–167. DOI:10.1016/j.cognition.2010.04.007
Zeithamova D., Maddox W.T. Dual-task interference in perceptual category learning. Memory & cog-nition, 2006, 34(2), 387–398. DOI:10.3758/BF03193416
Zettersten M., Bredemann C., Kaul M., Ellis K., Vlach H.A., Kirkorian H., Lupyan G. Nameability supports rule‐based category learning in children and adults. Child Development, 2024, 95(2), 497–514. DOI:10.1111/cdev.14008
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