Eyetracking Study Applications

Recap: Gaze + Pen UI Study Recently Apple Vision Pro started support of gaze+pen UI with the Logitech Muse stylus. Earlier this year, we conducted a study to better understand its performance and usability trade-offs. With Meta Quest Pro's eye-tracking and the stylus-grip controllers, we evaluated 4 object movement techniques in a shape point translation task: ✏️Direct Pen: selects the object directly, then drags it directly ✏️Raypointing: selects via the pen’s forward ray, then drags indirectly ✏️👀Gaze + Pen: selects with gaze, drags with pen indirectly ✏️👀👀Gaze + Snap: selects with gaze, drags with gaze using target-snapping* Results: ⏱️Gaze + Snap fastest overall (≈2.5s), compared to other techniques (3.4-3.6s) ❌Higher error rate for Gaze+Snap (2.6%), others (0.5–1.2%) ⏱️Raypointing ~10% faster for initial selection but ~16% slower during dragging compared to Direct Pen 💪Gaze + Snap lowest perceived hand fatigue but highest eye fatigue 🧠TLX workload and overall user preference favored Gaze+Snap In sum, more integrated use of gaze can be beneficial to performance. Compared to our similar study last year using hand+gaze, a key new finding is that the snapping approach not only reduces hand fatigue but also improves time by ~30%, at the cost of ~2% additional errors. This makes it a useful alternative to current Gaze+Pinch / Gaze+Pen UIs in tasks where snapping is possible. The paper was led by Uta Wagner (Universität Konstanz) and Jeremy Wu (KTH Royal Institute of Technology), with Qiushi Zhou and myself (Department of Computer Science, Aarhus University / Pioneer Centre for AI (P1)), in collaboration with Jinwook Kim (KAIST), Mario Romero (Linköping University), Alessandro Iop (KTH Royal Institute of Technology), and Tiare Feuchtner (Universität Konstanz). Presented at #ISMAR 2025 in Seoul, Korea. Links: 📄 Paper: https://lnkd.in/d9QQVWjF 🎥 Video: https://lnkd.in/eAywyUNi - Last year's object movement study: https://lnkd.in/eWiRP_YZ * This technique requires target-knowledge, enabling the target-snapping. Based on Vildan Tanriverdi and Rob Jacob's early work https://lnkd.in/eMtiTTKZ

I was reading a magazine on a Sunday morning in 2014 when I stumbled over a word. Ouagadougou. The capital of Burkina Faso. My eyes paused. Dwelled longer. Went back to re-read it. The article explained: Eye movement reveals cognitive understanding in real-time. When you read fluently, your eyes flow smoothly. When you encounter something unfamiliar, they pause, return, hesitate. That pause is measurable. It reveals cognitive load. That’s when I had a thought I couldn’t shake: If eye movement shows cognitive friction during reading… what would it show during airline operations control decisions? I was a product manager for ops and crew systems. Controllers would tell me: “The system works fine.” But I’d see the hunting. The clicking back and forth. The frustration. They’d adapted so completely to dysfunction they couldn’t articulate what was wrong. Within a week, I found an eye-tracking partner in Brandenburg, Germany. We tracked controllers through 12-hour shifts. Controllers said: “System works fine.” Their eyes said: Cognitive chaos. → 47-second hunt for information that should be immediate → Repeated returns to same screen (context loss) → Extended dwell time revealing confusion, not comprehension One ops controller watched her video: “I didn’t realize how much I was searching. I thought I was working. I was just… hunting.” She’d been doing the job for 12 years. Eye-tracking made the invisible visible. Here’s what haunts me: That Brandenburg partner? Acquired by Apple. The same technology is now in every iPhone. On every controller’s desk. Right now. Millions use it daily. But airline operations systems haven’t adopted it. Not because it doesn’t work. Because it’s “not proven in aviation.” And here’s what we’re missing: Eye-tracking isn’t just UX research. It’s the ultimate AI performance metric. Everyone’s deploying “AI-powered” systems. But how do you know if AI actually helps? Current metrics: Accuracy, speed, error rates Missing metric: Does it reduce cognitive burden? Eye-tracking reveals this objectively: If AI works: Eyes move smoothly, less dwelling, directed patterns (like reading fluent text) If AI fails: Extended dwell time, anxious scanning, more returns (like stumbling over Ouagadougou) You can’t fake eye patterns. Controllers can say “AI is helpful” while eyes reveal anxiety. The technology exists. The capability sits on controllers’ desks. We’re just not measuring what matters. The question isn’t whether AI produces right answers. The question is: Does it make decision-making feel like reading fluent text, or stumbling over Ouagadougou? I wrote about how a Sunday morning magazine led to measuring cognitive loa, and why eye-tracking should be the standard for AI performance. Operations professionals: When you use “AI assistance,” does it feel like it’s reading your mind, or like you’re validating everything it does? Technology teams: Are you measuring cognitive load, or just accuracy?

This is what bad interfaces do to your eyes We usually talk about bad UX as an inconvenience, but what if it’s something more physical than that? Having my Master degree in biomedical engineering I was really curious about how our body actually respond to digital inaccessibility. When a user lands on a page with: • low contrast text • dense blocks of content • unclear structure • too many competing elements they don’t just feel “annoyed”, their brain has to work harder to process what’s in front of them. Also, when people concentrate harder, they blink significantly less and blinking is what keeps the eye hydrated and protected. Here’s what science tells us in depth about it: 1️⃣ People blink less when interfaces are harder to process Higher cognitive load → lower blink rate → more eye strain 🔗Multiple usability studies show that difficult interfaces reduce blinking and increase attention effort (https://lnkd.in/ec79UN77) 2️⃣ Pupils dilate when users struggle Your brain signals effort → your pupils physically expand 🔗 Pupil dilation is a well-established biological marker of mental effort in cognitive tasks (https://lnkd.in/e5Ztu9NG) 3️⃣ Eyes stay fixed longer on confusing content Users stare longer, re-read, and search more 🔗 Eye-tracking research shows increased fixation duration with higher task difficulty and poor usability (https://lnkd.in/eWzhNCJV) 4️⃣ Eye behavior reflects cognitive stress in real time Blink rate, pupil size, gaze patterns = measurable stress signals 🔗Eye tracking is widely used to objectively measure cognitive load and user effort (https://lnkd.in/e_GTNJtm) 5️⃣ These signals are directly tied to how hard the interface is Not the user. Not their skills. 🔗Studies confirm eye metrics (blink rate, pupil dilation, fixations) are reliable indicators of interface difficulty and mental workload (https://lnkd.in/euRPJpNF) What’s interesting is that most studies don’t even look at “accessibility”, instead they look at cognitive load, which is exactly what accessibility issues increase. ➡️ A wall of text is not just “bad design” - it forces the eyes to work harder to track lines ➡️ Low contrast is not just “non-compliant” - it increases effort for basic perception ➡️ Unclear buttons are not just “confusing” - they keep users in a constant state of micro-decision-making So accessibility is not only about inclusion, it’s about respecting the limits of the human body. Have you ever noticed certain websites making your eyes tired almost instantly? #CognitiveLoad #HumanCenteredDesign #DigitalHealth #DesignResearch #Usability #A11y #ProductDesign

Eye-gaze measurement is becoming an advanced metric to understand audience engagement. Here’s how it’s typically done: 1. Camera-based Eye-Tracking Technology • How it works: Cameras (usually discreet and privacy-compliant) are installed near the DOOH screen. They capture facial landmarks and estimate where the viewer’s eyes are directed. • AI processing: Algorithms analyze head position, gaze direction, and dwell time to determine if and how long a person looked at the screen. • Metrics collected: Impressions, gaze duration, number of engaged viewers, heatmaps of attention. 2. Computer Vision + Sensors • Uses a combination of video analytics and depth sensors to track movement and attention. • Can differentiate between those who simply pass by and those who actively look at the screen. 3. Eye-Tracking Panels or Wearables (Less Common) • Research panels may use glasses or mobile devices with eye-tracking sensors to record gaze data in controlled studies, providing insights into how much attention DOOH ads receive. 4. Privacy & Compliance • Data is anonymized and aggregated (no facial recognition for identity). • GDPR and local privacy laws require clear policies and sometimes opt-in mechanisms. Why Measure Eye-Gaze in DOOH? • Proof of engagement: Beyond reach, it measures attention quality. • Creative optimization: Which content holds attention longest. • Media value: Can help justify premium pricing for high-engagement sites.

That may very well be ONE element of a correct and compliant registration as SaMD: Expert gaze as a usability indicator of medical AI decision support systems: a preliminary study Abstract Given the current state of medical artificial intelligence (AI) and perceptions towards it, collaborative systems are becoming the preferred choice for clinical workflows. This work aims to address expert interaction with medical AI support systems to gain insight towards how these systems can be better designed with the user in mind. As eye tracking metrics have been shown to be robust indicators of usability, we employ them for evaluating the usability and user interaction with medical AI support systems. We use expert gaze to assess experts’ interaction with an AI software for caries detection in bitewing x-ray images. We compared standard viewing of bitewing images without AI support versus viewing where AI support could be freely toggled on and off. We found that experts turned the AI on for roughly 25% of the total inspection task, and generally turned it on halfway through the course of the inspection. Gaze behavior showed that when supported by AI, more attention was dedicated to user interface elements related to the AI support, with more frequent transitions from the image itself to these elements. When considering that expert visual strategy is already optimized for fast and effective image inspection, such interruptions in attention can lead to increased time needed for the overall assessment. Gaze analysis provided valuable insights into an AI’s usability for medical image inspection. Further analyses of these tools and how to delineate metrical measures of usability should be developed. Castner, N., Arsiwala-Scheppach, L., Mertens, S. et al. Expert gaze as a usability indicator of medical AI decision support systems: a preliminary study. npj Digit. Med. 7, 199 (2024). DOI:10.1038/s41746-024-01192-8 #ai #artificialintelligence #samd #aiamd #medicaldevices #regulatoryaffairs #regulation #regulatorycompliance #healthcare #digitalhealth #research #eo #euaiact #hhs #fda #scientificresearch 

What if the text could follow your eyes? We just published a study testing gaze-based word highlighting in 2nd graders. Here’s what we found: • Kids read faster when the word they look at changes color • They made fewer eye movements backwards • No negative effect on pronunciation or understanding Using EyeJustRead and an eye tracker, we recreated finger-point reading, but digitally. When a child looks at a word, it turns blue. Simple, but effective. • Great for early readers • Helpful for reading practice • A step toward smart, personalized reading tools Authors: Koen Rummens & Sofie Beier Part of the ScreenReads project, funded by Innovation Fund Denmark. https://lnkd.in/dvupSsFG

👁️ What Your Eyes Know Before Your Brain Does (Insights from my Joyffles eye-tracking study with Auros Design) In packaging design, being seen isn't enough; you need to be understood. When we tested Joyffles against other breakfast options: 🧠 95% of shoppers noticed Joyffles within 1.4 seconds of looking at the shelf, which was the fastest of all products on the shelf. That’s shelf science at work. What Eye Tracking Revealed Design tweaks that look minor to an untrained eye can completely reshape how a shopper experiences a pack: 🎯 Contrast creates hierarchy. A slight increase in contrast on “oat-powered waffles” and “gluten-free” boosted visibility instantly and altered the gaze path. 🎨 Color changes the story. The flavor with the lighter palette shifted gaze toward flavor cues; darker palettes made benefits pop first. 🚫 Movement ≠ meaning. An animated waffle image may disrupt the gaze path, reducing the chance for other elements to be noticed. The Broader Principle Your shopper’s eyes make decisions long before they realize it. And eye tracking might be the only tool precise enough to show you what’s truly working and what’s stealing attention from what matters most. This is how design can guide behavior. If your brand isn’t using eye tracking to validate design, you’re leaving shelf impact to chance. #EyeTracking #PackagingDesign #ConsumerPsychology #BrandStrategy #ShopperInsights #DesignThinking #VisualHierarchy This study was conducted with 100+ online shoppers using webcam eye tracking with Anna Ison💥

A small tease of something I’ve been wanting for years: replaying a writing session and actually seeing where the writer is looking. 👀⌨️ This screenshot is from TypeFlow EYE’s experimental replayer. Each coloured circle is a fixation, the lines between them are saccades, all projected on top of the text that was written at that moment. You can scrub through the timeline, change the playback speed, and watch how gaze shifts across the page as the text unfolds. The aim is simple: bring keystroke logging and webcam-based eye tracking together in a single workflow. One session, one workbook, and a replayer that lets you see pauses, revisions, and gaze behaviour on the same screen. For L2 writing research (and writing research more broadly), this opens up a lot of questions we can finally start to test: -Where do writers look just before a major revision? -How does attention move across sentences and paragraphs during planning? -Do different task conditions or proficiency levels “leave a trace” in gaze patterns? TypeFlow EYE is still under development, and the replayer will keep changing as we test it with real datasets. The plan is to make it available to researchers and teachers over the next few months, as part of the wider TypeFlow ecosystem. More soon. For now, here is the sneak peek. #TypeFlow #TypeFlowEYE #keystrokelogging #eyetracking #L2writing #writingresearch #edtech

👁️ It’s Not What You Show. It’s Where You Make Them Look. We often focus on what the customer sees: color palettes, layouts, images. But neuroscience reminds us of a deeper truth: The way we guide the eyes can change how people feel, think—and decide. A new peer-reviewed study shows how visual structure—not content—can drive physiological and emotional responses. Not hypotheticals. Measurable shifts in heart rate, eye movement patterns, and perceived stress—all triggered by the design of what’s seen. When the eyes move smoothly and symmetrically, the brain relaxes. When the gaze is blocked or fragmented, cognitive load increases—and so does emotional friction. In my latest article, I break down: - What the research really found - How it connects to EMDR therapy (yes, really) - What this means for retail, packaging, UX, and marketing design 🧠 It’s not about showing more. It’s about showing better—based on how the brain actually works. Read the full piece here Let me know what you think—and how your brand is applying neuroscience to drive attention and action. #neuroscience #marketingstrategy #retaildesign #consumerbehavior #neuroUX #packaging #visualattention #branding #eyetracking

I'm excited to share insights from a comprehensive analysis of 19 studies on the use of eye-tracking technology in medical education. Our research lab is equipped with state-of-the-art eye-tracking devices, which have been instrumental in exploring its diverse applications—from decoding clinical vignettes to enhancing radiological expertise. This technology provides deep insights into cognitive processes by measuring visual attention and cognitive load and offers data-driven enhancements to medical training. Moreover, its adaptation to remote learning through innovative webcam-based solutions is revolutionizing online education. Are you curious to learn more or interested in discussing how these technologies can be integrated into healthcare training programs? Let’s connect and explore eye-tracking technology's possibilities for advancing medical education! #MedicalEducation #EyeTracking #HealthcareTechnology #MedTech