VanLehn, K. Cognitive skill acquisition. Ann. Rev. Psychol. 47, 513539 (1996). <\/p>\n
Article Google Scholar <\/p>\n
DeKeyser, R. in Skill Acquisition Theory 83104 (Routledge, 2020). <\/p>\n
Tabibian, B. et al. Enhancing human learning via spaced repetition optimization. Proc. Natl Acad. Sci. USA 116, 39883993 (2019). <\/p>\n
Article MathSciNet Google Scholar <\/p>\n
Evans, N. J., Brown, S. D., Mewhort, D. J. & Heathcote, A. Refining the law of practice. Psychol. Rev. 125, 592 (2018). <\/p>\n
Article Google Scholar <\/p>\n
Heathcote, A., Brown, S. & Mewhort, D. J. The power law repealed: the case for an exponential law of practice. Psychon. Bull. Rev. 7, 185207 (2000). <\/p>\n
Article Google Scholar <\/p>\n
Shrager, J., Hogg, T. & Huberman, B. A. A graph-dynamic model of the power law of practice and the problem-solving fan-effect. Science 242, 414416 (1988). <\/p>\n
Article Google Scholar <\/p>\n
Wixted, J. T. The enigma of forgetting. Proc. Natl Acad. Sci. USA 119, e2201332119 (2022). <\/p>\n
Article Google Scholar <\/p>\n
Averell, L. & Heathcote, A. The form of the forgetting curve and the fate of memories. J. Math. Psychol. 55, 2535 (2011). <\/p>\n
Article MathSciNet Google Scholar <\/p>\n
Roediger III, H. L. Relativity of remembering: why the laws of memory vanished. Annu. Rev. Psychol. 59, 225254 (2008). <\/p>\n
Article Google Scholar <\/p>\n
Chiaburu, D. S. & Marinova, S. V. What predicts skill transfer? An exploratory study of goal orientation, training self-efficacy and organizational supports. Int. J. Train. Dev. 9, 110123 (2005). <\/p>\n
Article Google Scholar <\/p>\n
Sturm, L. P. et al. A systematic review of skills transfer after surgical simulation training. Ann. Surgery 248, 166179 (2008). <\/p>\n
Article Google Scholar <\/p>\n
Logan, G. D. Toward an instance theory of automatization. Psycho. Rev. 95, 492 (1988). <\/p>\n
Article Google Scholar <\/p>\n
Logan, G. D. Shapes of reaction-time distributions and shapes of learning curves: a test of the instance theory of automaticity. J. Exp. Psychol. 18, 883 (1992). <\/p>\n
Google Scholar <\/p>\n
Anderson, J. R. Acquisition of cognitive skill. Psychol. Rev. 89, 369 (1982). <\/p>\n
Article Google Scholar <\/p>\n
Tenison, C. & Anderson, J. R. Modeling the distinct phases of skill acquisition. J. Exp. Psychol. 42, 749 (2016). <\/p>\n
Google Scholar <\/p>\n
Tenison, C., Fincham, J. M. & Anderson, J. R. Phases of learning: how skill acquisition impacts cognitive processing. Cognitive Psychol. 87, 128 (2016). <\/p>\n
Article Google Scholar <\/p>\n
Jordan, M. I. Serial order: a parallel distributed processing approach. Adv. Pyschol. 121, 471495 (1997). <\/p>\n
McClelland, J. L. et al. Parallel Distributed Processing Vol. 2 (MIT Press, 1986). <\/p>\n
Young, R. M. & Lewis, R. L. in Models of Working Memory: Mechanisms of Active Maintenance and Executive Control 224256 (Cambridge Univ. Press, 1999). <\/p>\n
Anderson, J. R., Matessa, M. & Lebiere, C. in HumanComputer Interaction Vol. 12, 439462 (Lawrence Erlbaum Associates, 1997). <\/p>\n
Ritter, F. E., Tehranchi, F. & Oury, J. D. ACT-R: a cognitive architecture for modeling cognition. WIREs Cogn. Sci. 10, e1488 (2019). <\/p>\n
Article Google Scholar <\/p>\n
Goldstone, R. L. & Lupyan, G. Discovering psychological principles by mining naturally occurring data sets. Topics Cogn. Sci. 8, 548568 (2016). <\/p>\n
Article Google Scholar <\/p>\n
Jenkins, J. J., Cermak, L. & Craik, F. in Levels of Processing in Human Memory 429446 (Pyschology, 1979). <\/p>\n
Udrescu, S.-M. & Tegmark, M. AI Feynman: a physics-inspired method for symbolic regression. Sci. Adv. 6, eaay2631 (2020). <\/p>\n
Article Google Scholar <\/p>\n
Wang, H. et al. Scientific discovery in the age of artificial intelligence. Nature 620, 4760 (2023). <\/p>\n
Article Google Scholar <\/p>\n
Schmidt, M. & Lipson, H. Distilling free-form natural laws from experimental data. Science 324, 8185 (2009). <\/p>\n
Article Google Scholar <\/p>\n
Cranmer, M. et al. Discovering symbolic models from deep learning with inductive biases. In 34th Conference on Neural Information Processing Systems 1742917442 (NeurIPS, 2020). <\/p>\n
Rudy, S. H., Brunton, S. L., Proctor, J. L. & Kutz, J. N. Data-driven discovery of partial differential equations. Sci. Adv. 3, e1602614 (2017). <\/p>\n
Article Google Scholar <\/p>\n
Chen, Z., Liu, Y. & Sun, H. Physics-informed learning of governing equations from scarce data. Nat. Commun. 12, 6136 (2021). <\/p>\n
Article Google Scholar <\/p>\n
Margraf, J. T., Jung, H., Scheurer, C. & Reuter, K. Exploring catalytic reaction networks with machine learning. Nat. Catal. 6, 112121 (2023). <\/p>\n
Article Google Scholar <\/p>\n
Han, Z.-K. et al. Single-atom alloy catalysts designed by first-principles calculations and artificial intelligence. Nat. Commun. 12, 1833 (2021). <\/p>\n
Article Google Scholar <\/p>\n
Wang, Y., Wagner, N. & Rondinelli, J. M. Symbolic regression in materials science. MRS Commun. 9, 793805 (2019). <\/p>\n
Article Google Scholar <\/p>\n
He, M. & Zhang, L. Machine learning and symbolic regression investigation on stability of mxene materials. Comput. Mater. Sci. 196, 110578 (2021). <\/p>\n
Article Google Scholar <\/p>\n
LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436444 (2015). <\/p>\n
Article Google Scholar <\/p>\n
Vaswani, A. et al. Attention is all you need. In Advances in Neural Information Processing Systems 30 (NIPS, 2017). <\/p>\n
Baker, F. B. The Basics of Item Response Theory (ERIC, 2001). <\/p>\n
Hambleton, R. K., Swaminathan, H. & Rogers, H. J. Fundamentals of Item Response Theory Vol. 2 (Sage, 1991). <\/p>\n
Swaminathan, H. & Gifford, J. A. Bayesian estimation in the three-parameter logistic model. Psychometrika 51, 589601 (1986). <\/p>\n
Article MathSciNet Google Scholar <\/p>\n
Maris, G. & Bechger, T. On interpreting the model parameters for the three parameter logistic model. Measurement 7, 7588 (2009). <\/p>\n
Google Scholar <\/p>\n
Hornik, K. Approximation capabilities of multilayer feedforward networks. Neural Netw. 4, 251257 (1991). <\/p>\n
Article Google Scholar <\/p>\n
Raghu, M., Poole, B., Kleinberg, J., Ganguli, S. & Sohl-Dickstein, J. On the expressive power of deep neural networks. In Proc. 34th International Conference on Machine Learning 28472854 (PMLR, 2017). <\/p>\n
Cybenko, G. Approximation by superpositions of a sigmoidal function. Math. Control Signals Syst. 2, 303314 (1989). <\/p>\n
Article MathSciNet Google Scholar <\/p>\n
Spearman, C. General intelligence, objectively determined and measured. Am. J. Psychol. 15, 201293 (1904). <\/p>\n
Article Google Scholar <\/p>\n
Eid, M., Geiser, C., Koch, T. & Heene, M. Anomalous results in g-factor models: explanations and alternatives. Psychol. Methods 22, 541 (2017). <\/p>\n
Article Google Scholar <\/p>\n
Steyvers, M. & Schafer, R. J. Inferring latent learning factors in large-scale cognitive training data. Nat. Hum. Behav. 4, 11451155 (2020). <\/p>\n
Article Google Scholar <\/p>\n
Simons, D. J. et al. Do brain-training programs work?. Psychol. Sci. Public Interest 17, 103186 (2016). <\/p>\n
Article Google Scholar <\/p>\n
Kievit, R. A. et al. Mutualistic coupling between vocabulary and reasoning supports cognitive development during late adolescence and early adulthood. Psychol. Sci. 28, 14191431 (2017). <\/p>\n
Article Google Scholar <\/p>\n
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, 12451252 (2019). <\/p>\n
Article Google Scholar <\/p>\n
Fisher, R. A. Design of experiments. Brit. Med. J. 1, 554 (1936). <\/p>\n
Article Google Scholar <\/p>\n
Kumar, A., Benjamin, A. S., Heathcote, A. & Steyvers, M. Comparing models of learning and relearning in large-scale cognitive training data sets. NPJ Sci. Learn. 7, 24 (2022). <\/p>\n
Article Google Scholar <\/p>\n
Liu, R. & Koedinger, K. R. Towards reliable and valid measurement of individualized student parameters. In Proc.10th International Conference on Educational Data Mining 135142 (International Educational Data Mining Society, 2017). <\/p>\n
Koedinger, K. R., Carvalho, P. F., Liu, R. & McLaughlin, E. A. An astonishing regularity in student learning rate. Proc. Natl Acad. Sci. USA 120, e2221311120 (2023). <\/p>\n
Article Google Scholar <\/p>\n
Neath, A. A. & Cavanaugh, J. E. The bayesian information criterion: background, derivation, and applications. WIREs Comput. Stats 4, 199203 (2012). <\/p>\n
Article Google Scholar <\/p>\n
Vrieze, S. I. Model selection and psychological theory: a discussion of the differences between the Akaike information criterion (AIC) and the Bayesian information criterion (BIC). Psychol. Methods 17, 228 (2012). <\/p>\n
Article Google Scholar <\/p>\n
Paszke, A. et al. PyTorch: an imperative style, high-performance deep learning library. In Proc. 33rd International Conference on Neural Information Processing Systems 80268037 (Curran Associates Inc., 2019). <\/p>\n
Kingma, D. P. & Ba, J. Adam: a method for stochastic optimization. Preprint at https:\/\/arxiv.org\/abs\/1412.6980<\/a> (2015). <\/p>\n 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, 15311543 (2019). <\/p>\n Article Google Scholar <\/p>\n <\/p>\n The rest is here: VanLehn, K. Cognitive skill acquisition Continue reading
\nAutomated discovery of symbolic laws governing skill acquisition from naturally occurring data - Nature.com<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"