Physiological signals can potentially be applied as objective measures to understand the behavior and engagement of users interacting with information access systems. However, the signals are highly sensitive, and many controls are required in laboratory user studies. To investigate the extent to which controlled or uncontrolled (i.e., confounding) variables such as task sequence or duration influence the observed signals, we conducted a pilot study where each participant completed four types of information-processing activities (READ, LISTEN, SPEAK, and WRITE). Meanwhile, we collected data on blood volume pulse, electrodermal activity, and pupil responses. We then used machine learning approaches as a mechanism to examine the influence of controlled and uncontrolled variables that commonly arise in user studies. Task duration was found to have a substantial effect on the model performance, suggesting it represents individual differences rather than giving insight into the target variables. This work contributes to our understanding of such variables in using physiological signals in information retrieval user studies.

翻译：生理信号可作为客观指标来理解用户与信息检索系统交互时的行为及参与度。然而，此类信号高度敏感，在实验室用户研究中需施加诸多控制条件。为探究任务顺序或持续时间等受控或非受控（即混杂）变量对观测信号的影响程度，我们开展了一项预实验研究：每位受试者需完成四种信息处理活动（阅读、聆听、口语表达和书写），同时采集其血容量脉搏、皮电活动及瞳孔反应数据。随后采用机器学习方法作为分析手段，考察用户研究中常见的受控与非受控变量的影响。研究发现任务持续时间对模型性能产生显著影响，这表明该变量反映的是个体差异而非目标变量的内在特征。本研究有助于深化对信息检索用户研究中运用生理信号时相关变量的理解。