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. 2018 Oct 29;9:4503. doi: 10.1038/s41467-018-06781-2

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© The Author(s) 2018

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. 4 — Computational properties and behavioral correlates of value normalization. a Likelihood difference (from model fitting) between the RELATIVE and the ABSOLUTE models over the 80 trials of the task sessions for both experiments (N = 60). A negative likelihood difference means that the ABSOLUTE model is the best-fitting model for the trial and a positive likelihood difference means that the RELATIVE model is the best-fitting model for the trial. Green dots: likelihood difference significantly different from 0 (P < 0.05, t-test). b Likelihood difference between the RELATIVE and the ABSOLUTE models over the first part of the task (40 first trials) and the last part (40 last trials) for both experiments. c Likelihood difference between the RELATIVE and the ABSOLUTE models for the two experiments. A negative likelihood difference means that the ABSOLUTE model is the best-fitting model for the experiment and a positive likelihood difference means that the RELATIVE model is the best-fitting model for the experiment. d Subject-specific free parameter weight (ω) comparison for the two experiments. e Subject-specific free parameter weight (ω) as a function of correct debriefing for the two questions (“fixed pairs” and “number of pairs”). f Debriefing as a function of the weight parameter. Small light gray dots above and below the bars in a–f represent individual subjects (N = 60). g and h Correct choice rate as a function of subjects’ weight parameter in the learning sessions and the transfer test for both Experiments 1 and 2. One dot corresponds to one participant (N = 60); green lines represent the linear regression calculations. Error bars represent s.e.m. ***P < 0.001, **P < 0.01, *P < 0.05, t-test