Cognitive Science and Education Research
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The samplr package, which includes a variety of sampling algorithms and cognitive models built upon them, is introduced, which shows how models in this package can match qualitative patterns found in human data, and walks readers through examples of how to compare these models quantitatively to human data.
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Many models of cognition have sampling as a central component. Researchers have used them to explain human performance across a wide range of tasks involving perception, probability reasoning, and choice. One family of these models takes inspiration from sampling approaches developed in statistics, either by adjusting samples with prior beliefs or by implementing a sampling algorithm that overcomes the difficulty of independent samples at the expense of autocorrelation. While these models have been successful in modeling human behavior, their adoption has been slowed by the technical difficulties in applying them. Here, we introduce the samplr package, which includes a variety of these algorithms and cognitive models built upon them. We show how models in this package can match qualitative patterns found in human data, and walk readers through examples of how to compare these models quantitatively to human data. This tool aims to make these methods accessible to many more researchers.
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@article{Castillo2026samplr,
title = {The samplr package: A tool for modeling human cognition with sampling algorithms},
author = {Lucas Castillo and Yun-Xiao Li and Adam N. Sanborn},
journal = {Behavior Research Methods},
year = {2026},
doi = {10.3758/s13428-026-03188-6},
url = {https://doi.org/10.3758/s13428-026-03188-6}
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