Characterizing Information Seeking Processes with Multiple Physiological Signals
Kaixin Ji, Danula Hettiachchi, Flora D. Salim, Falk Scholer, Damiano Spina
摘要
Information access systems are getting complex, and our understanding of user behavior during information seeking processes is mainly drawn from qualitative methods, such as observational studies or surveys. Leveraging the advances in sensing technologies, our study aims to characterize user behaviors with physiological signals, particularly in relation to cognitive load, affective arousal, and valence. We conduct a controlled lab study with 26 participants, and collect data including Electrodermal Activities, Photoplethysmogram, Electroencephalogram, and Pupillary Responses. This study examines informational search with four stages: the realization of Information Need (IN), Query Formulation (QF), Query Submission (QS), and Relevance Judgment (RJ). We also include different interaction modalities to represent modern systems, e.g., QS by text-typing or verbalizing, and RJ with text or audio information. We analyze the physiological signals across these stages and report outcomes of pairwise non-parametric repeated-measure statistical tests. The results show that participants experience significantly higher cognitive loads at IN with a subtle increase in alertness, while QF requires higher attention. QS involves demanding cognitive loads than QF. Affective responses are more pronounced at RJ than QS or IN, suggesting greater interest and engagement as knowledge gaps are resolved. To the best of our knowledge, this is the first study that explores user behaviors in a search process employing a more nuanced quantitative analysis of physiological signals. Our findings offer valuable insights into user behavior and emotional responses in information seeking processes. We believe our proposed methodology can inform the characterization of more complex processes, such as conversational information seeking.
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了每一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
引用它的顶会 Paper4
- SenseSeek Dataset: Multimodal Sensing to Study Information Seeking BehaviorsKaixin Ji, Danula Hettiachchi, Falk Scholer, Flora D. Salim 等UbiComp 2025 · 被引用 7 次
- Towards Brain Passage Retrieval: An Investigation of EEG Query RepresentationsNiall McGuire, Yashar MoshfeghiSIGIR 2025 · 被引用 5 次
- An Eye Tracking Study: Are AI Overviews Changing Search Behavior?Sara Allawati, Dana McKay, Mark Sanderson, Paul Thomas 等SIGIR 2026 · 被引用 2 次
- BESPOKE: Benchmark for Search-Augmented Large Language Model Personalization via Diagnostic FeedbackHyunseo Kim, Sangam Lee, Kwangwook Seo, Dongha LeeICML 2026
它引用的顶会 Paper5
- A Critique of Electrodermal Activity Practices at CHIEbrahim Babaei, Benjamin Tag, Tilman Dingler, Eduardo VellosoCHI 2021 · 被引用 64 次
- Bias-Aware Systems: Exploring Indicators for the Occurrences of Cognitive Biases when Facing Different OpinionsNattapat Boonprakong, Xiuge Chen, Catherine M. Davey, Benjamin Tag 等CHI 2023 · 被引用 27 次
- Towards a Better Understanding of Human Reading Comprehension with Brain SignalsZiyi Ye, Xiaohui Xie, Yiqun Liu, Zhihong Wang 等WWW 2022 · 被引用 25 次
- Information Need Awareness: An EEG StudyDominika Michalkova, Mario Parra-Rodriguez, Yashar MoshfeghiSIGIR 2022 · 被引用 14 次
- Do Affective Cues Validate Behavioural Metrics for Search?Daniel McDuff, Paul Thomas, Nick Craswell, Kael Rowan 等SIGIR 2021 · 被引用 12 次
相关 Paper
- The Cortical Activity of Graded RelevanceZuzana Pinkosova, William J. McGeown, Yashar MoshfeghiSIGIR 2020 · 被引用 31 次
- Cognitive Style Shapes Search Behaviours: An fNIRS Study of Exploratory SearchHuimin Tang, Boon Giin Lee, Dave Towey, Max L. Wilson 等SIGIR 2026
- A Passage-Level Reading Behavior Model for Mobile SearchZhijing Wu, Jiaxin Mao, Kedi Xu, Dandan Song 等WWW 2023 · 被引用 3 次
- Brain Topography Adaptive Network for Satisfaction Modeling in Interactive Information Access SystemZiyi Ye, Xiaohui Xie, Yiqun Liu, Zhihong Wang 等ACM MM 2022 · 被引用 6 次
- Measuring Human Trust in a Virtual Assistant using Physiological Sensing in Virtual RealityKunal Gupta, Ryo Hajika, Yun Suen Pai, Andreas Duenser 等IEEE VR 2020 · 被引用 16 次
