Understanding how working memory (WM) capacity influences cognitive load (CL) during interactive information retrieval (IIR) tasks is critical for designing effective user interfaces. This knowledge supports minimizing cognitive overload, optimizing performance, and tailoring systems to individual cognitive profiles. This study examines CL dynamics and emotional responses during two types of search tasks: fact-checking (FC) and decision-making (DM), among individuals with varying WM capacity, and explores the relationship between CL and emotional states. CL was estimated in near-real-time using pupil diameter signals and analyzed across task phases (begin, mid, end). Participants were divided into high and low WM groups based on N-back task performance. Results show that CL was higher during DM tasks, especially for low WM individuals, while no group differences emerged during FC tasks. CL was highest at the beginning of the tasks and decreased over time, suggesting cognitive adaptation. Analysis of emotions revealed that high WM individuals exhibited positive correlations between CL and valence, joy, and engagement, whereas low WM individuals showed stronger associations with confusion and surprise, particularly during DM tasks. These findings highlight the importance of WM capacity in shaping cognitive and emotional experiences, offering insights for personalized and adaptive system design.

Attention is a crucial construct in Interactive Information Retrieval (IIR) activities and has become an area of growing interest among CHIIR researchers. Despite this importance, explicit definitions are rare. Many studies refer to “attention” without specifying which process or aspect is under examination, and often operationalize it using practical indicators (e.g., eye fixations, dwell time, cursor traces, interaction logs) without a clear conceptual framework guiding measurement choices, raising concerns about the comparability of findings. This paper reviews how attention has been defined, operationalized, and measured in CHIIR publications from the conference’s inception in 2016 through 2025. We searched the ACM Digital Library for "attention" within CHIIR proceedings, finding 296 results. After filtering and using semantic similarity, we narrowed it to 45 relevant papers, from which 19 were selected for in-depth review based on their clear relevance to attention. Drawing on theoretical frameworks and empirical findings from psychology and cognitive science, we analyze how CHIIR researchers define and apply the concept of attention in various contexts. Our review uncovers a variety of interpretations, aspects, and measurement approaches to attention, which reflect broader challenges in bridging cognitive theory and information interaction research. We discuss key issues underlying these differing uses of the term “attention,” and outline possible directions for advancing conceptual clarity and methodological robustness of attention research within CHIIR. We hope this perspective paper raises researchers’ awareness of the need to clearly define attention, thereby promoting greater rigor and reproducibility in future IIR studies.

With the rise of online misinformation and AI-generated text, understanding human perception of news truthfulness is critical. In this study, we examine visual attention and cognitive processing using eye-tracking measures as individuals read fake and real news articles sharing nearly identical structure and imagery, differing only in subtle textual changes. Using the public FakeNewsPerception dataset, we analyze advanced gaze measures, including scanpaths, AOI transitions, and luminance-corrected pupil measures, beyond basic gaze features, in relation to news truthfulness and perceived believability. Results show that, given the high fidelity of the fake news, readers exhibited comparable visual scanning patterns, attention allocation across AOIs, and cognitive load regardless of truthfulness and perceived believability of news. Interestingly, when participants believed a news item, they demonstrated lower focal attention than when uncertain or disbelieving, suggesting that prior belief reduces visual inspection. These findings, supported by behavioral analysis, may help explain human susceptibility to high-fidelity misinformation.

Relevance assessment is central to interactive information retrieval, yet most studies focus on judgment outcomes rather than underlying cognitive processes. Eye-tracking reveals how individuals allocate visual attention, but spatial gaze distribution measures only capture limited aspects of gaze behavior. In this study, we use a previously collected eye-tracking dataset to provide a multidimensional analysis of visual attention and cognitive load during document evaluation, examining spatial gaze distribution, fixation density, visual search dynamics, and pupil-based measure of cognitive load. Our results show that irrelevant documents, that are objectively irrelevant and judged as such, were scanned quickly, with shorter fixations, higher saccade rates, simpler scanpaths, and lower cognitive load. In contrast, relevant or topical documents, and documents left unjudged, showed longer fixations, slower saccades, focused inspection, greater scanpath complexity, and higher cognitive load. Our findings highlight that relevance assessment is a dynamic process shaped by interactions between document properties and individual judgments.

Understanding how users perform map-based tasks is essential for improving map design and supporting efficient spatial decision-making. Eye-tracking and pupil-based measures provide objective insights into visual search organization and cognitive load. Using a previously collected eye-tracking dataset, we examined differences in scanpath complexity, average inter-saccadic direction change (AISDC), and pupil-based cognitive load between high- and low-efficiency task performance during location-finding and route-planning on cartographic and satellite maps. Each sub-task was divided into three temporal phases for analysis. All participants completed all tasks. Results showed that scanpath complexity reflected how visual search organization varied with both task type and map representation. However, task efficiency was not associated with AISDC; instead, AISDC varied significantly across task phases, reflecting temporal changes in visual scanning behavior. Furthermore, pupil-based cognitive load was not significantly related to task efficiency. These findings deepen our understanding of visual strategies and cognitive demands, highlighting their importance in navigation tool design.

Relevance assessment is fundamental to Interactive Information Retrieval (IIR), and understanding the underlying cognitive processes is essential for optimizing user experience.To investigate how relevance and confirmation bias relate to cognitive load, we conducted a within-subject, lab-based, eye-tracking experiment (N=32).We applied the Low/High Index of Pupillary Activity (LHIPA) to measure cognitive load.LHIPA was calculated based on changes in pupil diameter and was unaffected by lighting conditions.Our results suggest that during relevance assessment: (1) for topically irrelevant documents, cognitive load is higher when users perceive them as relevant; for topically relevant documents, cognitive load is higher when users perceive them as irrelevant; (2) users with higher levels of topic familiarity and interest expend more cognitive load; and(3) users with higher predisposition to confirmation bias invest less cognitive load.These findings demonstrate that during relevance assessment, users' perceived relevance, topical knowledge, interest, and confirmation bias tendency influence cognitive load levels.This research has implications for improving user interface design to facilitate more accurate relevance judgments and more effective, unbiased information retrieval.

During visual search, individuals’ attention shifts between ambient and focal states in response to task demands and stimuli. The ambient/focal coefficient K is a statistically validated measure of these states, computed offline from fixation duration and saccade amplitude data. While current methods compute K offline, real-time computation could enable applications such as monitoring user attention, creating attention-adaptive user interfaces, and optimizing graphics rendering. However, real-time computation of K requires stable estimates for the parameters of fixation duration and saccade amplitude distributions. Since these distributions are heavy-tailed, the real-time estimates exhibit high variance and slow convergence. To overcome this, we propose a robust parametrization and an alternative estimation method, along with two real-time measures analogous to K. Through a map viewing study involving localization and route planning tasks, we show that our proposed measures exhibit dynamics consistent with offline K.

Real-Time Advanced Eye Movements Analysis Pipeline (RAEMAP) is an advanced pipeline to analyze traditional positional gaze measurements as well as advanced eye gaze measurements. The proposed implementation of RAEMAP includes real-time analysis of fixations, saccades, gaze transition entropy, and low/high index of pupillary activity. RAEMAP will also provide visualizations of fixations, fixations on AOIs, heatmaps, and dynamic AOI generation in real-time. This paper outlines the proposed architecture of RAEMAP.

Reading plays a vital role in updating the researchers on recent developments in the field, including but not limited to solutions to various problems and collaborative studies between disciplines. Prior studies identify reading patterns to vary depending on the level of expertise of the researcher on the content of the document. We present a pilot study of eye-tracking measures during a reading task with participants across different areas of expertise with the intention of characterizing the reading patterns using both eye movement and pupillary information.

The Low/High Index of Pupillary Activity (LHIPA), an eye-tracked measure of pupil diameter oscillation, is redesigned and implemented to function in real-time. The novel Real-time IPA (RIPA) is shown to discriminate cognitive load in re-streamed data from earlier experiments. Rationale for the RIPA is tied to the functioning of the human autonomic nervous system yielding a hybrid measure based on the ratio of Low/High frequencies of pupil oscillation. The paper's contribution is drawn from provision of documentation of the calculation of the RIPA. As with the LHIPA, it is possible for researchers to apply this metric to their own experiments where a measure of cognitive load is of interest.

When conducting eye tracking studies, having a mechanism to collect data, build workflows, and validate results in a FAIR (i.e., findable, accessible, interoperable, and reusable) manner, facilitates automation. Given the vast landscape of vendor-specific eye tracking software, adopting FAIR metadata standards for the eye tracking domain is one step towards this. In this paper, we propose an approach to simplify the creation, execution, and validation of eye tracking studies through metadata. Using a metadata format that we developed, we first describe two eye trackers, and two datasets collected using them. Next, we use this metadata to simulate real-time data collection by replaying each dataset. From this replayed data, we analyze eye movements in real-time, and synthesize eye movement data from analytics in real-time. Based on our results, we discuss the utility of metadata in real-time eye tracking studies, and how this idea can be generalized into other applications.

The vastness of the web imposes a prohibitive cost on building large-scale search engines with limited resources. Crawl frontiers thus need to be optimized to improve the coverage and freshness of crawled content. In this paper, we propose an approach for modeling the dynamics of change in the web using archived copies of webpages. To evaluate its utility, we conduct a preliminary study on the scholarly web using 19,977 seed URLs of authors’ homepages obtained from their Google Scholar profiles. We first obtain archived copies of these webpages from the Internet Archive (IA), and estimate when their actual updates occurred. Next, we apply maximum likelihood to estimate their mean update frequency (λ) values. Our evaluation shows that λ values derived from a short history of archived data provide a good estimate for the true update frequency in the short-term, and that our method provides better estimations of updates at a fraction of resources compared to the baseline models. Based on this, we demonstrate the utility of archived data to optimize the crawling strategy of web crawlers, and uncover important challenges that inspire future research directions.