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

Yannick Jadoul*, Diandra Duengen, Andrea Ravignani*

*Corresponding author for this work

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

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 .

Original languageEnglish
Article number135
JournalBMC Research Notes
Volume16
ISSN1756-0500
DOIs
Publication statusPublished - Jul 2023

Keywords

  • Animal cognition
  • Experimental psychology
  • Go/no-go
  • Psychometrics
  • Python
  • Randomization
  • Simple heuristics
  • Two-alternative forced-choice

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