CORTEXA
← Browse
arxivcs.HC2026-07-15

Interaction Density as a Behavioural Signature of Exhibit Type: A Minimal-Log Study from a Two-Venue Science Experience Centre

R A Udaya Rakshith, Inavamsi Enaganti, Umang J Gala

Understanding how visitors engage with interactive exhibits usually calls for either labour-intensive manual observation or invasive multimodal sensing -- eye-tracking, cameras, wearables -- that few science centres can deploy at scale. We ask how much can be learned instead from the handful of fields that most touch-enabled exhibits already log by default: a session's start time, end time, and press count. Analysing 2,816 visitor sessions across eight exhibits at two venues of a science experience centre in Bengaluru, India, we derive interaction density -- presses per second -- as a simple behavioural signature, and use it to distinguish fast-paced games from slower, deliberate quizzes. Density does so cleanly (Mann-Whitney r=0.556) and predicts exhibit type on its own with a cross-validated AUC=0.778. But the data complicates the obvious story: games are not just more intense, visitors also dwell on them longer (r=0.172), reversing the intuitive trade-off between intensity and duration -- traced to exhibits whose escalating difficulty creates open-ended re-engagement loops rather than fixed endpoints. Density is not a universal replacement for existing metrics either: raw press count alone explains far more variance in dwell time (R^2=0.527) than density does (R^2=0.081), though combining both improves on either alone (R^2=0.667). Exhibit-level anomalies, a cross-venue replication check, and a session-length censoring artefact further stress-test rather than simply confirm these results. The broader case we make is methodological: minimal, privacy-preserving interaction logs -- not additional sensors -- can already support rigorous, falsifiable behavioural research at any science centre with touch-enabled exhibits.

View free PDFSource page

Related papers

arxivcs.MMcs.HC2026-07-20

Toward Site-Aware MR Art Exhibitions: A SLAM-Based Deployment Pipeline for Spatial Coherence and Exhibition Experience

Yawei Zhao, Yuming Zhu, Hao Li, Yuqi Liang, Ao Yu, Anca-Simona Horvath, et al.

Mixed Reality (MR) is increasingly being used in exhibition settings to bring digital artworks into relation with the physical environment. However, existing MR exhibition systems are often confined to prototypes or case-specific deployments, offering limited guidance for large-s…

View free PDFSource page
arxivcs.HC2026-07-15

From Product-Centred Retrieval to Experience-Led Commerce:Twelve Candidate Design Principles for Fashion E-Commerce User Experience

Nafiul I. Khan, Mansura Habiba, Rafflesia Khan

This paper proposes twelve candidate Experience-Led Commerce design principles for high-constraint, relational fashion e-commerce, surfaced through design-led induction while building VogueDrop, a multi-vendor prototype. The principles address multi-entry discovery, experience co…

View free PDFSource page
arxivcs.HC2026-07-02

OrchestrXR: A Multi-Agent System for Idea-to-Prototype XR Study Authoring

Shuqi Liao, Chenfei Zhu, Karthik Ramani, Voicu Popescu

Extended Reality (XR) has become an important interaction paradigm in Human-Computer Interaction (HCI). XR studies are used to investigate interaction, perception, and user behavior in immersive environments, and typically involve experimental tasks, 3D scenes, and interactive lo…

View free PDFSource page
arxivcs.HC2026-06-26

Typing Behavior in Human-LLM Interaction: Keystroke Dynamics Reveal Cognitive Effort During Prompting

Laura Schütz, Yousri Cherif, Clara Sayffaerth, Thomas Weber, Francesco Chiossi

As Large Language Models (LLMs) become increasingly integrated into daily routines, understanding how users interact with these systems is crucial for effective human-AI collaboration. This work investigates keystroke dynamics as a behavioral measure of user mental effort and per…

View free PDFSource page
arxivcs.HCcs.AIcs.CL2026-07-16

Memory-Driven Self-Disclosure and Relational Turning Points: A Longitudinal Multimodal Study of Human-AI Interaction

Ryuichi Sumida, Mao Saeki, Masaki Eguchi, Sadahiro Yoshikawa, Koji Inoue, Tatsuya Kawahara, et al.

As conversational AI systems are designed for repeated use, a central question is how a series of interactions becomes a relationship. We present a longitudinal multimodal study of a memory-augmented conversational agent (24 participants x 10 sessions), in which participants rate…

View free PDFSource page