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. 2020 Jul 2;5:9. doi: 10.1038/s41539-020-0067-8

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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. 6 — Within each blue rectangle are the ten tests that were included in the object-based spatial battery, while shaded in red are the six tests that are part of the spatial orientation battery set in a naturalistic virtual environment. Obj CS cross-section, obj 2D 2D drawing, obj PA pattern assembly, obj SR shapes rotation, obj MecR mechanical reasoning, obj PF paper folding, obj 3D 3D drawing, obj PT perspective taking, obj EM Elithorn Maze, obj Maz Mazes, SC scanning, PT perspective taking, NL navigation according to landmarks, ND navigation according to directions, RM route memory, MR map reading. Both the phenotypic (χ² = 351.870 (101), p < 0.001, CFI = 0.953, TLI = 0.944, RMSEA = 0.041, SMRS = 0.041) and genetic (χ² = 1681.128 (1040), p = < 0.001; CFI = 0.941; TLI = 0.944; RMSEA = 0.026; SRMR = 0.056) model provided good fit for the data.