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

Area of Science:

• Animal Cognition
• Psychophysics
• Experimental Psychology

Background:

• Randomized trial presentation is crucial for accurate performance assessment in behavioral experiments.
• Simple patterns in trial orders can lead to inflated performance due to accidental learning, especially with few trials.

Purpose of the Study:

• To provide a software tool for generating pseudorandom trial sequences that avoid simple patterns.
• To assist researchers in creating fair and unbiased experimental designs.

Main Methods:

• Development of a Python software package, PyGellermann.
• Implementation of the Gellermann series for pseudorandom sequence generation.
• User-friendly interface allowing selection of sequence length and output to CSV.

Main Results:

• PyGellermann efficiently generates pseudorandom sequences, preventing simple heuristics and false positive responses.
• The tool produces sequences that are suitable for various experimental paradigms, ensuring a fair assessment of participant performance.
• The software is readily available for download and use by the research community.

Conclusions:

• PyGellermann offers a practical solution for researchers needing to randomize trial orders.
• The tool helps mitigate bias in experimental results by avoiding predictable patterns.
• This facilitates more reliable data collection in animal cognition and psychological studies.