PyGellermann: a Python tool to generate pseudorandom series for human and non-human animal behavioural experiments
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 particip...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-07-01
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| Series: | BMC Research Notes |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13104-023-06396-x |
| _version_ | 1797784754691506176 |
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| author | Yannick Jadoul Diandra Duengen Andrea Ravignani |
| author_facet | Yannick Jadoul Diandra Duengen Andrea Ravignani |
| author_sort | Yannick Jadoul |
| collection | DOAJ |
| description | 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 . |
| first_indexed | 2024-03-13T00:44:24Z |
| format | Article |
| id | doaj.art-4c8386e685024343aa7f9893dcce2520 |
| institution | Directory Open Access Journal |
| issn | 1756-0500 |
| language | English |
| last_indexed | 2024-03-13T00:44:24Z |
| publishDate | 2023-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Research Notes |
| spelling | doaj.art-4c8386e685024343aa7f9893dcce25202023-07-09T11:05:15ZengBMCBMC Research Notes1756-05002023-07-011611510.1186/s13104-023-06396-xPyGellermann: a Python tool to generate pseudorandom series for human and non-human animal behavioural experimentsYannick Jadoul0Diandra Duengen1Andrea Ravignani2Comparative Bioacoustics Group, Max Planck Institute for PsycholinguisticsComparative Bioacoustics Group, Max Planck Institute for PsycholinguisticsComparative Bioacoustics Group, Max Planck Institute for PsycholinguisticsAbstract 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 .https://doi.org/10.1186/s13104-023-06396-xAnimal cognitionExperimental psychologyRandomizationSimple heuristicsPythonPsychometrics |
| spellingShingle | Yannick Jadoul Diandra Duengen Andrea Ravignani PyGellermann: a Python tool to generate pseudorandom series for human and non-human animal behavioural experiments BMC Research Notes Animal cognition Experimental psychology Randomization Simple heuristics Python Psychometrics |
| title | PyGellermann: a Python tool to generate pseudorandom series for human and non-human animal behavioural experiments |
| title_full | PyGellermann: a Python tool to generate pseudorandom series for human and non-human animal behavioural experiments |
| title_fullStr | PyGellermann: a Python tool to generate pseudorandom series for human and non-human animal behavioural experiments |
| title_full_unstemmed | PyGellermann: a Python tool to generate pseudorandom series for human and non-human animal behavioural experiments |
| title_short | PyGellermann: a Python tool to generate pseudorandom series for human and non-human animal behavioural experiments |
| title_sort | pygellermann a python tool to generate pseudorandom series for human and non human animal behavioural experiments |
| topic | Animal cognition Experimental psychology Randomization Simple heuristics Python Psychometrics |
| url | https://doi.org/10.1186/s13104-023-06396-x |
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