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. 2021 Dec 3;84(2):459–473. doi: 10.3758/s13414-021-02387-x

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© The Author(s) 2021

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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Fig. 1 — Simplified possible outcomes of Experiment 1. The ordering of the labels on the x-axis is based on distance; the two high-probability locations were always maximally distant. (A) Learning is context-independent, so that each high-probability location is suppressed equally. (B) Learning is somewhat context-dependent, so that the high-probability location matching the current context is suppressed most. (C) Learning is fully context-dependent, so that suppression occurs only for the matching high-probability location