PyGellermann

PyGellermann generates pseudorandom trial sequences for behavioral experiments involving human and non-human animals with two possible correct responses, constraining exploitable patterns for controlled randomization.

Key Features:

• Gellermann Series Implementation: Implements the Gellermann series to produce pseudorandom binary sequences that limit exploitable patterns and simple response heuristics.
• Customizable Sequence Length: Generates sequences of user-defined length for experimental sessions.
• Structured Output: Exports generated sequences in .csv format for integration with downstream behavioral data analysis workflows.

Scientific Applications:

• Two-Choice Behavioral Paradigms: Controls trial order in experiments with binary correct responses to prevent pattern-based performance inflation and improve assessment of learning and cognitive processes.
• Low-Trial Experiments: Reduces bias in studies with limited trial numbers where unrestricted randomization may produce predictable patterns.

Methodology:

Implements the Gellermann series to constrain binary trial sequences by limiting consecutive repetitions and distribution imbalance, counteracting simple participant heuristics and reducing false positive performance rates.