Spearman, C. ‘General intelligence’ objectively determined and measured. Am. J. Psychol. 15, 201–293 (1904).
Carroll, J. B. et al. Human Cognitive Abilities: A Survey of Factor-Analytic Studies (Cambridge Univ. Press, 1993).
Conway, A. R., Cowan, N., Bunting, M. F., Therriault, D. J. & Minkoff, S. R. A latent variable analysis of working memory capacity, short-term memory capacity, processing speed, and general fluid intelligence. Intelligence 30, 163–183 (2002).
Kovacs, K. & Conway, A. R. Process overlap theory: a unified account of the general factor of intelligence. Psychol. Inq. 27, 151–177 (2016).
Jensen, A. R. Clocking the Mind: Mental Chronometry and Individual Differences (Elsevier, 2006).
Schubert, A.-L., Hagemann, D. & Frischkorn, G. T. Is general intelligence little more than the speed of higher-order processing?. J. Exp. Psychol. 146, 1498–1512 (2017).
Kaufman, S. B., DeYoung, C. G., Gray, J. R., Brown, J. & Mackintosh, N. Associative learning predicts intelligence above and beyond working memory and processing speed. Intelligence 37, 374–382 (2009).
Van Der Maas, H. L. et al. A dynamical model of general intelligence: the positive manifold of intelligence by mutualism. Psychol. Rev. 113, 842–861 (2006).
Barbey, A. K. Network neuroscience theory of human intelligence. Trends Cogn. Sci. 22, 8–20 (2018).
Steyvers, M., Hawkins, G. E., Karayanidis, F. & Brown, S. D. A large-scale analysis of task switching practice effects across the lifespan. Proc. Natl Acad. Sci. USA 116, 17735–17740 (2019).
Steyvers, M. & Benjamin, A. S. The joint contribution of participation and performance to learning functions: exploring the effects of age in large-scale data sets. Behav. Res. Methods 51, 1531–1543 (2019).
Donner, Y. & Hardy, J. L. Piecewise power laws in individual learning curves. Psychon. Bull. Rev. 22, 1308–1319 (2015).
Ilin, A. & Raiko, T. Practical approaches to principal component analysis in the presence of missing values. J. Machine Learning Res. 11, 1957–2000 (2010).
Tipping, M. E. & Bishop, C. M. Probabilistic principal component analysis. J. R. Stat. Soc. B 61, 611–622 (1999).
Lim, Y.J. & Teh, Y.W. Variational Bayesian approach to movie rating prediction. In Proc. International Conference on Knowledge Discovery and Data Mining 15–21 (ACM, 2007).
Bell, R. M. & Koren, Y. Scalable collaborative filtering with jointly derived neighborhood interpolation weights. In Proc. Seventh IEEE International Conference on Data Mining 43–52 (IEEE Computer Society, 2007).
Driver, C. C. & Voelkle, M. C. Hierarchical Bayesian continuous time dynamic modeling. Psychol. Methods 23, 774–799 (2018).
Kievit, R. A. et al. Developmental cognitive neuroscience using latent change score models: a tutorial and applications. Dev. Cogn. Neurosci. 33, 99–117 (2018).
Isiordia, M. & Ferrer, E. Curve of factors model: a latent growth modeling approach for educational research. Educ. Psychol. Meas. 78, 203–231 (2018).
Ram, N. & Grimm, K. J. in Handbook of Child Psychology and Developmental Science (ed. Lerner, R. M.) 1–31 (Wiley, 2015).
McArdle, J. J., Ferrer-Caja, E., Hamagami, F. & Woodcock, R. W. Comparative longitudinal structural analyses of the growth and decline of multiple intellectual abilities over the life span. Dev. Psychol. 38, 115–142 (2002).
Preacher, K. J., Wichman, A. L., MacCallum, R. C. & Briggs, N. E. Latent Growth Curve Modeling (Sage, 2008).
McNeish, D., Dumas, D. G. & Grimm, K. J. Estimating new quantities from longitudinal test scores to improve forecasts of future performance. Multivariate Behav. Res. https://doi.org/10.1080/00273171.2019.1691484 (2019).
Rosenberg, M. D., Casey, B. & Holmes, A. J. Prediction complements explanation in understanding the developing brain. Nat. Commun. 9, 589 (2018).
Settles B., Brust, C., Gustafson, E., Hagiwara, M. & Madnani, N. Second language acquisition modeling. In Proc. Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications (eds Tetreault, J., Burstein, J., Kochmar, E., Leacock, C. & Yannakoudakis, H.) 56–65 (ACL, 2018).
Luttinen, J. & Ilin, A. Transformations in variational Bayesian factor analysis to speed up learning. Neurocomputing 73, 1093–1102 (2010).
Fabrigar, L. R., Wegener, D. T., MacCallum, R. C. & Strahan, E. J. Evaluating the use of exploratory factor analysis in psychological research. Psychol. Methods 4, 272–299 (1999).
Abdi, H. in Encyclopedia for Research Methods for the Social Sciences (ed. Lewis-Beck, M. S. et al.) 792–795 (Sage, 2004).
Widaman, K. F., Ferrer, E. & Conger, R. D. Factorial invariance within longitudinal structural equation models: measuring the same construct across time. Child Dev. Perspect. 4, 10–18 (2010).
Salthouse, T. A. The processing-speed theory of adult age differences in cognition. Psychol. Rev. 103, 403–428 (1996).
Jensen, A. R. Regularities in Spearman’s law of diminishing returns. Intelligence 31, 95–105 (2003).
Griffiths, T. L. Manifesto for a new cognitive revolution. Cognition 135, 21–23 (2015).
Goldstone, R. L. & Lupyan, G. Discovering psychological principles by mining naturally occurring data sets. Topics Cogn. Sci. 8, 548–568 (2016).
Molenaar, D., Dolan, C. V., Wicherts, J. M. & van der Maas, H. L. Modeling differentiation of cognitive abilities within the higher-order factor model using moderated factor analysis. Intelligence 38, 611–624 (2010).
Tucker-Drob, E. M. Differentiation of cognitive abilities across the life span. Dev. Psychol. 45, 1097–1118 (2009).
Kievit, R. A. et al. Mutualistic coupling between vocabulary and reasoning supports cognitive development during late adolescence and early adulthood. Psychol. Sci. 28, 1419–1431 (2017).
Kievit, R. A., Hofman, A. D. & Nation, K. Mutualistic coupling between vocabulary and reasoning in young children: a replication and extension of the study by Kievit et al.(2017). Psychol. Sci. 30, 1245–1252 (2019).
Evans, N. J., Brown, S. D., Mewhort, D. J. & Heathcote, A. Refining the law of practice. Psychol. Rev. 125, 592–605 (2018).
Frischkorn, G. & Schubert, A.-L. Cognitive models in intelligence research: advantages and recommendations for their application. J. Intell. 6, 34 (2018).
Melby-LervÅg, M., Redick, T. S. & Hulme, C. Working memory training does not improve performance on measures of intelligence or other measures of ‘far transfer’ evidence from a meta-analytic review. Perspect. Psychol. Sci. 11, 512–534 (2016).
Simons, D. J. et al. Do ‘brain-training’ programs work? Psychol. Sci. Public Interest 17, 103–186 (2016).