Designing for Cognitive Efficiency

You can’t see cognitive overload. That’s why it’s ignored. Most teams treat accessibility as contrast ratios and alt text. But cognitive accessibility is wider than that, and less forgiving when you get it wrong. Here are 5 common cognitive disabilities And what designers can actually do. 1. ADHD Challenges: • Distractibility • Difficulty prioritizing • Overwhelm from dense layouts Design for: • Clear visual hierarchy • One primary action per section • Step-based flows Avoid: • Competing primary CTAs • Auto-rotating carousels • Notification overload 2. Dyslexia Challenges: • Slower decoding • Reading fatigue • Difficulty with dense text blocks Design for: • Plain language • Left-aligned text • Generous line height (1.5+ recommended) • Clear headings and chunking Avoid: • Justified text • Long paragraphs • Low-contrast body text 3. Autism Spectrum Challenges: • Sensory sensitivity • Cognitive overload • Distress from unexpected change Design for: • Predictable layouts • Explicit labels • Warnings before context shifts • User-controlled animation and motion Avoid: • Sudden modals • Autoplay video • Reduced motion off by default • Ambiguous copy like “Try it” or “Explore.” 4. Memory Impairment Challenges: • Forgetting steps • Losing context in multi-step flows Design for: • Persistent instructions • Progress indicators • Auto-save • Clear error recovery Avoid: • Clearing form data on error • Hiding previous answers • Long forms without sectioning 5. Anxiety Disorders Challenges: • Fear of mistakes • Stress from uncertainty • Decision paralysis Design for: • Reassuring microcopy • Undo functionality • Transparent consequences • Calm error messaging Avoid: • Countdown timers • Aggressive urgency language • Vague destructive actions Ask yourself: "Does this screen reduce thinking or increase it?" 👇🏽 Are we over-indexing on visual accessibility while ignoring cognitive overload? Drop your thoughts in the comments. ♻️ Share and save this for your team. --- ✉️ Subscribe to my newsletter for accessibility and design insights here: https://lnkd.in/gZpAzWSu --- Accessibility note: Content in the post is the same as the image attached (except for a few bullets omitted for easy scanability)

Clinicians don’t want more data. They want fewer decisions. HealthTech keeps confusing complexity with sophistication. We assume that because clinicians are smart, they want more dashboards. More alerts. More choices. In truth, they want something no algorithm can measure: Cognitive relief. Imagine you’re a pilot. Mid-flight, you’re shown 17 new dials. Flashing red. Each says something important. Now make a life-or-death decision. Fast. Would you say thank you? That’s what most clinical decision support looks like in HealthTech today. And it’s killing trust faster than bad data ever could. Why? Because information isn’t value. Clarity is. The problem, IMO, isn’t the number of alerts. It’s the hidden cost of each micro-decision. Every time we ask a clinician to interpret another data stream, we’re not helping them, we’re taxing them. It’s not death by data. It’s death by 1,000 cognitive cuts. We’ve forgotten the difference between data and decision. Between information and insight. Between noise and relevance. And worst of all? We often design for what looks impressive - not what actually works on a ward round. The best HealthTech doesn’t make clinicians feel smarter. It makes them feel safer. Not “empowered.” Not “augmented.” Just calm. Just clear. That’s the gold standard now isn’t it? Tools that remove thinking, not add to it. If you’re building in HealthTech, Don’t ask: “What more can we show?” Ask: “What decisions can we take away?” That’s where trust is built. That’s where burnout is reduced. Build for fewer decisions. What would you add? P.S. Tools that reduce decisions are finally being valued. VCs are rewarding clarity, not complexity. If your AI product calms the chaos - you're building in the right direction - https://lnkd.in/euA2-8a2

When someone lands on your site, every extra word, button, or menu is a cognitive tax. Take this landing page comparison: Attio - keeps the load light • One navigation bar • 12 words in total for the header + sub-header • 9 clickable exits above the fold • Lots of whitespace • Sneak peak at product imagery The result = focus 🧘♀️ HubSpot - seems to have many cooks in the kitchen • Two navigation bars at the top • 50% more words (24 words in the header + subheader) • 13 clickable exits above the fold • Bigger chat widgets • Lifestyle imagery instead of whitespace The result = distraction 🐿️ With busier pages comes higher cognitive load, the paradox of choice, and decision paralysis 🧠 In real terms: if someone pauses even a split second more and doesn’t act, they’re more likely to bounce. And this isn’t just true for landing pages - it applies to pricing pages, homepages, dashboards… anywhere with competing priorities 👩🍳 👩🍳 👩🍳 It’s easy to add, hard to cut. ✂️ Good design isn’t what you add, it’s what you remove (or don't add in the first place). So ask yourself: What's the 30% you can remove from your page? 🗑️

Neuroinclusive by Design: why it’s neuroscience — not “niceness” — and 10 tweaks leaders can make. Most organisations say they “support neurodiversity”. Far fewer have actually designed work so neurodivergent people can thrive without constantly asking for exceptions. Neuroscience explains why this gap matters. When work is unclear, unpredictable, noisy or judgement-heavy, the brain’s threat system (centred around the amygdala and stress-response networks) switches on. In that state, energy is pulled away from the prefrontal cortex — the part of the brain responsible for planning, prioritising, working memory, emotional regulation and flexible thinking. The result? Capable people appear distracted, slower, reactive or “underperforming” — not because they lack skill, but because their brains are operating in survival mode. This effect is amplified for neurodivergent adults whose brains already expend more energy on sensory processing and executive function. Poor design quietly taxes health and performance at the same time. Psychological safety does the opposite. Clear expectations, predictable communication and normalised adjustments calm the nervous system, bring the prefrontal cortex back online, and unlock creativity, problem-solving and collaboration. Here are 10 tweaks leaders can make. 1. Build “How do you work best?” into inductions and regular 1:1s Don’t wait for crisis or disclosure — proactive asking reduces vigilance and builds trust. 2. Give every meeting a clear purpose, agenda and outcome — shared in advance This lowers cognitive load and allows time to process, rather than forcing real-time scrambling. 3. Follow verbally heavy meetings with concise written notes Decisions, owners and deadlines support working memory and reduce anxiety. 4. Use quarterly job-crafting conversations Ask: Which tasks energise you? Which drain you? What small swaps could we make? This aligns work with motivation and dopamine systems, not constant effortful compensation. 5. Make flexibility part of the design, not a special favour Agreed WFH, focus days and quiet reduce masking and social threat. 6. Audit your environment for sensory overload — and fix one thing -all feed directly into nervous system regulation. 7. Protect focus blocks where instant replies aren’t expected. 8. Increase predictability Share what’s coming next week and next month to support planning, energy and executive function. 9. Ask servant-leadership questions in 1:1s “What’s getting in the way of your best work — and what can I remove or change?” 10. Treat disclosure and adjustment requests as gold-dust feedback Thank people, explore options together, and follow up. Feeling believed actively calms the threat system. None of this is about being “nice”. It’s about designing work that allows brains to stay regulated, so people can actually do the work you hired them to do. #NeuroinclusiveLeadership #NeurodiversityAtWork #ADHDAtWork #AutismAtWork

UX research is a series of decisions under uncertainty. Cognitive modeling helps those decisions by turning our assumptions about perception, learning, memory, and choice into testable predictions. Instead of asking only what happened, we ask how it happened and what will happen next if we change the design. That shift lets us pick better metrics, design safer flows, and avoid classic traps like order effects or overfitting. Connectionist models treat cognition as activity in networks of simple units. Knowledge lives in connection weights that update with experience. They explain generalization and robustness to noise, which is useful when users face new patterns, changing layouts, or imperfect inputs. Bayesian models treat cognition as probabilistic inference. People combine prior expectations with new evidence and update beliefs. This lens is valuable for risk displays, recommendations, and any interface where uncertainty must be shown and trusted. Symbolic and hybrid models represent explicit rules and structured knowledge, and combine them with learned components when needed. They match real workflows that mix rule following with habit, so they help when you are designing guided steps that also need to adapt. Logic based modeling captures reasoning with formal logic so assumptions and conclusions are explicit. It supports transparency and verification in regulated or safety critical products where users must trust how a system reached a decision. Dynamical systems view cognition as continuous change in time. Behavior settles into stable patterns called attractors and stays controlled through feedback. This helps tune real time interaction such as pointing, gestures, and VR or AR control so motion feels smooth and recoveries are quick. Quantum models use quantum probability to explain context and order effects in judgment. They matter for survey and testing work because question order and framing can shift responses in systematic ways that you can predict and control. Cognitive architectures are large frameworks that integrate perception, memory, attention, goals, and action in one running system. They let you simulate multi step tasks and multitasking to estimate time, error risk, and cognitive load before you build. Deep learning treats cognition as learned layers of distributed representations. Deep networks capture aspects of perception, categorization, and sequence learning without hand coded rules. Reinforcement learning models behavior shaped by rewards and feedback over time. It guides decisions about onboarding, notification timing, and longer term engagement so short term clicks do not undermine long term outcomes.

Web accessibility & mental health: why we need to talk about it In my years working as a web accessibility expert, I’ve often noticed: we tend to focus on physical and sensory disabilities, but mental-health issues and cognitive differences often sit in the shadows of our accessibility discussions. Here’s what I’ve come to understand: · A recent study found that when accessibility features designed for cognitive support were absent, even users without disabilities showed declining cognitive engagement over time (eye-tracking & heart-rate monitoring used) (link to the study: https://lnkd.in/e5ZQe2i7) · The World Wide Web Consortium has a dedicated page on Cognitive Accessibility, acknowledging that many user needs are still not addressed in current standards (link to the webpage: https://lnkd.in/enTWiJdJ) · The European Commission published a 2022 study on inclusive web-accessibility for persons with cognitive disabilities, noting that improved cognitive accessibility benefits everyone (link to the study: https://lnkd.in/e7Z-XAxW) 🚨 Why mental health & cognitive accessibility matters, but gets overlooked · Many mental-health conditions affect attention, memory, processing speed, anxiety, distraction. Yet accessibility standards like WCAG only indirectly address these via criteria like “Readable” or “Predictable”. · This means a website can be technically WCAG compliant, but still highly stressful or inaccessible for a person experiencing anxiety, depression, PTSD, or cognitive fatigue. · Because mental-health issues are less visible and more variable, teams often don’t plan for them, yet by doing so we exclude a very large group of users. ✏️ Practical tips for designing with mental-health & cognitive needs in mind 1. Simplify tasks & reduce cognitive load Use clear, concise language; break down complex processes into simple steps. Provide “skip this step” or “help” options when tasks require concentration. 2. Manage pace, timing & interruptions Don’t assume users can process content the same as usual - allow more time, allow pauses. Provide options to reduce motion, remove auto-refreshing content. 3. Offer predictable, consistent navigation and UI Avoid surprises, unexpected changes, hidden actions. People with anxiety or executive-function challenges benefit greatly from consistency. 4. Enable personalization & adaptation Allow users to choose simpler mode, reduce visual clutter, choose focus mode, change colours or fonts. 5. Test with real users Too often we test only “visual/motor” disabilities, but persons with cognitive or mental-health-related challenges have unique real-world pain points and involve them early. If you’re working on a project, I invite you to pause and ask: “How would this feel if I were anxious, processing slowly, distracted, or tired?” Because accessibility is empathy translated into design. #Accessibility #MentalHealth #CognitiveAccessibility #InclusiveDesign #WebAccessibility #A11y #UX

66% of websites utterly fail at something most of us would consider simple: Telling users where they are. That figure—from Baymard Institute—might sound bad enough on its own, but it also drives 48% of cart abandonments and puts conversion in a stranglehold. Most teams treat navigation as structure. But it’s actually cognitive infrastructure—an external memory system that supports (or sabotages) how your users think. Here’s what makes navigation work (or fail): 🧠 Cognitive Load Theory → Your labels, menus, and paths either lighten or add to users’ mental burden. → Reducing extraneous load lets them focus on goal completion. 🧭 Wayfinding Psychology → Every user subconsciously asks: ① Where am I? ② Where can I go? ③ How do I get there? ④ How do I know I’ve arrived? 👃 Information Scent → Ambiguous links (“Learn more”) kill conversion. → Predictive cues (“View pricing & plans”) build trust and clarity. Swipe for the full breakdown of → cognitive principles → practical frameworks → testing methods that separate functional navigation from forgettable UX. When navigation aligns with cognition, it stops being structure and becomes a mental model users can trust. Food for thought: If navigation is external memory, what are you helping users remember—and what are you making them forget? #uxdesign #userpsychology #designsystems #informationarchitecture ⸻ 👋🏼 Hi, I’m Dane—your source for UX and product strategy insights. ❤️ Found this helpful? A 👍🏼 would be thuper kewl. 🔄 Share to help others (or for easy access later). ➕ Follow for more UX clarity in your feed every day.

📘 Free resource: Designing for Thinking: Managing Cognitive Load with AI We talk a lot about rigor — but rarely about the cognitive architecture that makes rigor possible. Cognitive Load Theory reminds us: working memory is small, schema-building is slow, and overload kills learning before it starts. The new Alder Branch guide — Designing for Thinking: Managing Cognitive Load with AI — helps educators use research and AI tools like ChatGPT to plan smarter, declutter lessons, and measure mental effort in real time. Inside, you’ll find: 🔹 Research summaries grounded in Sweller (1988), Mayer (2005), Paas & van Merriënboer (1994), and Greenberg (2022) — real, sourceable citations, not just buzzwords. 🔹 AI prompt exemplars you can copy directly into ChatGPT, like: • “Review this lesson plan for extraneous cognitive load and suggest how to simplify without losing rigor.” • “Create a 3-question exit ticket that helps students self-rate mental effort and identify confusion points.” • “Design a reflection activity linking today’s concept to prior schema to strengthen germane load.” 🔹 Practical models for scaffolding intrinsic load, reducing extraneous load, and increasing germane load through structured prompts. 🔹 Tools for cognitive-load monitoring — quick reflections, formative check-ins, and visual audit templates. 🔹 A final section connecting load management to attention and care, showing how design choices protect energy, empathy, and equity. 💡 This guide reframes AI from a “content generator” into a cognitive design partner — helping educators design for minds that think, not minds that just survive. 📥 Download more free resources now: https://lnkd.in/g-uvsvMw 👩🏫 Try one idea this week: Ask ChatGPT — “Analyze my slides for cognitive friction and recommend one design change to free up working memory.” Then watch what happens to clarity, curiosity, and connection. Rooted in Care. 🌿 Growing Through Our Connections. #FreeResource #CognitiveLoadTheory #AIinEducation #AlderBranch #TeacherPD #ChatGPTforTeachers #InstructionalDesign #VisibleLearning #SchemaBasedLearning #EdTech #CognitiveScience #EducationalLeadership #ChatGPT #ChatGPTforEducation #AIAcademy Check out below: Musa Avsar Aman Kumar Austin Bates Kristi Burgh Harvard University Dr. Timothy M. Wagner Timothy Mahoney Kevin Riambon Loed Lacayo Dr. Monique Darrisaw-Akil Monica Burns, Ed.D Dr. Judy Fields Julie Carroll-Hantson Cullen Skyles David Roney Hannah Nino-Harris, M.Ed. Hippocratic AI Munjal Shah Christina Aldrich (Pou) Johanna Poncio Jordan Reilly Harmeyer Jill Hlavacek Jim West