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. 2022 Dec 12;12:21459. doi: 10.1038/s41598-022-11698-4

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© The Author(s) 2022

Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/.

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Methodological approach. Panel (A) illustrates the two steps, edge inclusion and filtering, involved in inferring networks from semantic fluency sequences. For details see Materials and Methods. The resulting network is based on 142 sequences of the older adults’ group of study 1. To simplify the visualization more conservative inferences settings were employed than used in the analyses reported below. Panel (B) illustrates the creation of networks from similarity ratings by normalizing individuals’ responses to the range of 0 and 1. The weighted network is based on the average ratings of the older adults’ group of study 3.