// Use-Inspired Basic Research Lines
Understanding and designing effective interactions between humans and computing technology requires multidisciplinary collaboration across Human Factors, Computer Science, and Engineering. Our laboratory advances Human Factors science by establishing predictive human performance models and translating cognitive principles into engineering solutions.
1 > We apply cognitive principles to replace fragile security practices with human-centered cybersecurity designs and personalized risk-messaging systems.
2 > We replace subjective visual UX heuristics with computable, objective Attentional Priority models.
3 > We establish the boundary conditions of attentional guidance in complex visual environments.
01. Cognition-Inspired Design in Cybersecurity
Instead of viewing operators as security failure points, our research designs interfaces that exploit natural human cognitive strengths (e.g., leveraging recognition over recall; Still, in press). This work has yielded novel graphical authentication schemes, provisional and nonprovisional patents, and consolidated usability guidelines (Still, Cain, & Schuster, 2017; Still & Still, 2025). We examined the impact authentication schemes have on our limited working memory resources (Still & Cain, 2019; Mator & Still, 2025;Notepad LM Podcast). Expanding into AI-informed cyber hygiene, we develop personalized risk-messaging systems that make invisible security threats relatable and actionable to enhance end-user Cyber Situation Awareness (CSA; Cain, Edwards, & Still, 2018; Mator & Still, 2021; Katsarakes et al., under review). We also explore the psychological drivers behind cybersecurity behaviors (e.g., cyber hygiene; Cain, Edwards, & Still, 2018 Notebook Podcast | Error Classification; Grandhi & Still, 2025b; Notebook Podcast | Personality profiling; Grandhi & Still, 2025a). We have been examining user susceptibility to SMiShing (SMS Phishing) to develop countermeasures (e.g., Edwards et al., 2026; Edwards & Still, 2026).
02. Computational Salience & Attentional Priority (AP) Models
Traditional UX guidelines rely on subjective heuristic models (e.g., Faraday's framework) that lack empirical validity in predicting initial visual fixations (Still, 2018; Still & Still, in press). Because preattentive visual mechanisms operate outside conscious awareness, we implement computational salience modeling to quantify low-level visual contrast across color, intensity, and edge channels (c.f., Itti, Niebur, & Koch, 1998). To integrate top-down cognitive expectations, we developed the Attentional Priority (AP) model framework. The AP computational model couples bottom-up salience with empirically derived spatial convention maps (e.g., desktop "F-shaped" or mobile "railroad track" scanning pattern) (Still, 2025; Still, 2017; Still & Hicks, 2020).
03. Attentional Guidance in Complex Scences
In high-density environments like drone footage or astronaut scheduling interfaces (e.g., NASA Playbook), visual complexity degrades search efficiency and elevates cognitive workload. Our findings show that target salience accelerates selection times by up to 900 ms in specialized tasks (Still, Hicks, & Cain, 2020) and operates independently of background clutter density (Hicks & Still, 2019). Furthermore, in goal-directed search tasks, we aim to better understand "attentional spillover," which shifts attentional focus toward non-salient interface targets (Still & Still, 2019; Still & Still, under review). Our future goal is to both understand visual priority and help designers structure information hierarchically so that critical elements are noticed appropriately.