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. 2020 Jan 21;3:34. doi: 10.1038/s42003-020-0759-x

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© The Author(s) 2020

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 1 — a Left: task setting and complexity algorithm for reward delivery (see text) Right: tree structure of the task and reward distribution. b Typical trajectories in training (T) and complexity (C) conditions. c Increase of the success rate over sessions in the complexity setting. Mice improved their performance in the first sessions (c01 versus c05, T = 223.5, p = 0.015, Wilcoxon test) then reached a plateau (c05 versus c10, t(25) = −0.43, p = 0.670, paired t-test) close to the theoretical 75% success rate of random selection (c10, t(25) = −1.87, p = 0.073, single sample t-test). The shaded area represents a 95% confidence interval. Inset, linear regressions of individual mice performance increase for individual mice (gray line) and average progress (blue line). d Increase of the behavior complexity over sessions: the NLZcomp measure of complexity increased in the beginning (training versus c01, T = 52, p = 0.0009, Wilcoxon test, c01 versus c05, t(26) = −2.67, p = 0.012, paired t-test) before reaching a plateau (c05 versus c10, T = 171, p = 0.909, Wilcoxon test). The average complexity reached by animal is lower than 1 (c10, t(25) = −9.34, p = 10–9, single sample t-test), which corresponds to the complexity of random sequences. The RQA ENT entropy-based measure of complexity decreased over sessions (training versus c01, t(26) = 2.81, p = 0.009, paired t-test, c01 versus c05, T = 92, p = 0.019, Wilcoxon test, c05 versus c10, T = 116, p = 0.13, Wilcoxon test). The rate of U-turns increased over sessions (training versus c01, t(26) = −2.21, p = 0.036, c01 versus c05, t(26) = −3.07, p = 0.004, paired t-test, c05 versus c10, T = 75, p = 0.010, Wilcoxon test). Error bars represent 95% confidence intervals. e Correlation between individual success rate and complexity of mice sequences. Also noteworthy is the decrease in data dispersion in session c10 compared to c1. N = 27 in all sessions except c10 where N = 26.