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  PyGellermann: a Python tool to generate pseudorandom series for human and non-human animal behavioural experiments

Jadoul, Y., Düngen, D., & Ravignani, A. (2023). PyGellermann: a Python tool to generate pseudorandom series for human and non-human animal behavioural experiments. BMC Research Notes, 16: 135. doi:10.1186/s13104-023-06396-x.

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2023
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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 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/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

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 Creators:
Jadoul, Yannick1, Author           
Düngen, Diandra1, Author           
Ravignani, Andrea1, 2, 3, Author           
Affiliations:
1Comparative Bioacoustics, MPI for Psycholinguistics, Max Planck Society, ou_3217299              
2Center for Music in the Brain, Department of Clinical Medicine, Aarhus University & The Royal Academy of Music Aarhus, Aarhus, Denmark, ou_persistent22              
3Department of Human Neurosciences, Sapienza University of Rome, Rome, Italy, ou_persistent22              

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 Abstract: Objective

Researchers in animal cognition, psychophysics, and experimental psychology need to randomise the presentation order of trials in experimental sessions. In many paradigms, for each trial, one of two responses can be correct, and the trials need to be ordered such that the participant’s responses are a fair assessment of their performance. Specifically, in some cases, especially for low numbers of trials, randomised trial orders need to be excluded if they contain simple patterns which a participant could accidentally match and so succeed at the task without learning.
Results

We present and distribute a simple Python software package and tool to produce pseudorandom sequences following the Gellermann series. This series has been proposed to pre-empt simple heuristics and avoid inflated performance rates via false positive responses. Our tool allows users to choose the sequence length and outputs a .csv file with newly and randomly generated sequences. This allows behavioural researchers to produce, in a few seconds, a pseudorandom sequence for their specific experiment. PyGellermann is available at https://github.com/YannickJadoul/PyGellermann.

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Language(s): eng - English
 Dates: 2023-07-052023-07-05
 Publication Status: Issued
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 Rev. Type: Peer
 Identifiers: DOI: 10.1186/s13104-023-06396-x
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Title: BMC Research Notes
Source Genre: Journal
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Pages: - Volume / Issue: 16 Sequence Number: 135 Start / End Page: - Identifier: ISSN: 1756-0500