A Multimodal Investigation of Controllability and Cognitive Load in Interactive Machine Learning

Interactive Machine Learning (IML) systems promise to democratize AI by enabling human influence over model behavior, yet the cognitive and behavioral implications of user control remain understudied. We present an investigation of how system controllability affects human factors in IML through the lens of an extensible research platform designed for both pedagogical and research applications. Our system features a toggleable hyperparameter control panel that transforms a streamlined annotation interface into an adjustable learning environment, allowing users to directly manipulate model training dynamics including learning rates, optimizers, and regularization parameters. Through a controlled laboratory study with 46 participants performing Named Entity Recognition (NER) tasks, we used multimodal measurements combining subjective assessments (NASA-TLX), physiological measures (pupil dilation, galvanic skin response), and behavioral metrics to understand the human cost of algorithmic control.