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

BMC Research Notes

Research Note

Diandra Duengen¹  Yannick Jadoul¹  Andrea Ravignani² 

[1] Comparative Bioacoustics Group, Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands;Comparative Bioacoustics Group, Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands;Center for Music in the Brain, Department of Clinical Medicine, Aarhus University & The Royal Academy of Music Aarhus/Aalborg, Aarhus, Denmark;Department of Human Neurosciences, Sapienza University of Rome, Rome, Italy;

关键词: Animal cognition;    Experimental psychology;    Randomization;    Simple heuristics;    Python;    Psychometrics;    Two-alternative forced-choice;    Go/no-go;   

DOI  :  10.1186/s13104-023-06396-x

 received in 2023-02-18, accepted in 2023-06-18,  发布年份 2023

来源: Springer

【 摘 要 】

ObjectiveResearchers 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.ResultsWe 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.

【 授权许可】

CC BY   
© The Author(s) 2023

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