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. 2019 Dec 27;9:19937. doi: 10.1038/s41598-019-56315-z

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© The Author(s) 2019

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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Temporal tuning analysis in Experiments 1 and 2. (A) We divided observers’ response frequency into a 3 × 3 matrix based on their responses 1/2/3 and the actual object category (A/B/C) on a current trial. (B) We then divided observers’ mistakes into three matrixes depending on whether the previous objects category was (A/B/C). First letter (in red) indicates morph category on the previous trial, second letter indicates morph category on the current trial (in green), and the number indicates classification response on a current trial (in green). Only mistakes were considered and hits were not taken into account (black squares on the diagonal line). (C) For a given response (1, for example), we summed mistakes when response and previous category were the same (AB1,AC1), and divided them by the sum of all the mistakes (AB1,AC1,BB1,BC1,CB1,CC1). This ratio yielded an index of serial dependence for a given responses (1 in this example). We then averaged the indexes across the three responses 1/2/3 and subtracted the chance baseline (33%).