PyBOLE – A General Python Based Framework for Behavioral Online Experiments: A Case Study Using the QUEST+ Adaptive Method

Designing online behavioral experiments that require flexible control over trial structure, stimulus presentation, and computational modeling, remains technically challenging. We introduce PyBOLE, a Python-based framework built on Django that enables researchers to develop and deploy fully programmable online experiments. The framework supports controlled presentation of visual and auditory stimuli, response collection, and screen management, while allowing customization without extensive JavaScript development. It also enables integration of external Python libraries, real-time updating of computational models during data collection, and automated recording of both behavioral responses and model states. To illustrate its capabilities, we present an online adaptive psychophysical experiment using the Bayesian QUEST+ procedure. The experiment was executed entirely within the framework, demonstrating stable model convergence and results consistent with a laboratory implementation of the same protocol. PyBOLE is freely available and designed to support experiments ranging from standard behavioral tasks to computationally demanding paradigms, emphasizing flexibility and full control over experimental logic, as a complementary tool for integrating advanced computational models within a Python-based environment.