Cognitive Load In UX

Buyers rarely choose the objectively best option. They choose the one they recognize. In many B2B decisions, familiarity plays a greater role than features or pricing. Teams may evaluate multiple vendors, but preference often leans toward the one they have consistently seen, heard, and understood over time. The reason is simple. Recognition signals safety. When a brand shows up repeatedly with clear, consistent messaging, it reduces perceived risk. Buyers feel more confident choosing what already feels familiar, even if alternatives may appear stronger on paper. This is where many marketing strategies lose effectiveness. In the pursuit of novelty, teams constantly change angles, campaigns, and positioning. But without consistency, recognition never compounds. Messaging resets instead of reinforcing, and trust takes longer to build. Repetition, when done well, is not redundancy. It is reinforcement. Each consistent touchpoint strengthens recall. Each repeated idea builds confidence. Over time, familiarity becomes preference, especially in longer B2B buying cycles. This week’s newsletter explores the psychology behind recognition, why repetition drives trust, and how to build consistency without losing relevance. For teams focused on sustainable growth, this is a shift worth understanding.

Meta's Breakthrough in Recommendation Systems: Solving the Recall vs Relevance Dilemma Researchers at Meta Platforms have just published groundbreaking work that tackles one of the most fundamental challenges in recommendation systems: the trade-off between finding highly engaging content and semantically relevant content. The Problem: Traditional item-to-item (I2I) retrieval models excel at finding content users will engage with based on co-engagement patterns, but they often miss semantically relevant items that could drive long-term user satisfaction and content diversity. The Innovation: MTMH (Multi-Task Multi-Head) Architecture Here's how their system works under the hood: Multi-Task Learning Framework: - Combines co-engagement loss (using InfoNCE) with semantic relevance loss (using KL divergence) - The semantic relevance loss leverages knowledge distillation from a pre-trained content encoder - Uses VIT-H14-632M for visual content and XLM-R-Large-550M for text processing Dual-Head Architecture: - Engagement Head: Trained solely on co-engagement data to capture highly engaging items - Relevance Head: Trained with multi-task loss to balance engagement and semantic similarity - Both heads operate in parallel during inference for maximum efficiency Smart Serving Pipeline: - Multi-ANN Module: Performs parallel approximate nearest neighbor search using K-means clustering - Preranker Module: Ranks candidates from each head separately to avoid bias - Quota-Based Merging: Combines results using an adjustable alpha parameter, allowing real-time tuning between recall and relevance without retraining Impressive Results: - 14.4% improvement in recall@500 - 56.6% boost in semantic relevance - Successfully deployed on a platform serving billions of users - Significant improvements in user experience metrics including content diversity, novel interest discovery, and fresh content delivery Why This Matters: This approach addresses critical long-term challenges like content diversity, user retention, and the cold-start problem for new content. The flexible alpha parameter allows platforms to dynamically adjust the recall-relevance trade-off based on specific use cases or user segments. The work demonstrates how thoughtful architecture design can overcome fundamental trade-offs that have plagued recommendation systems, paving the way for more balanced and user-centric content discovery.

Think about the last time someone brought up a detail you'd mentioned weeks ago, like your weekend hiking trip or your daughter's recital. That spark of recognition that makes you feel valued. New research from the University of Aberdeen reveals something we intuitively know but rarely leverage in our professional relationships: demonstrating that you remember specific details someone shared with you is just as effective at making them feel important as explicitly saying "Your thoughts matter." We have this powerful relationship tool right at our fingertips which we use with ease in our personal connections, yet we often forget to use it professionally. When we reference past conversations, we're saying "what you shared mattered enough for me to remember"—and that message resonates deeply. Here are seven moments when flexing your memory muscle transforms professional relationships: 1. When giving constructive feedback: "Remember when you mentioned wanting to develop presentation skills after that March client meeting? Here's how this project could help..." 2. During performance reviews: "You shared six months ago that you wanted more cross-functional collaboration. I've noticed how you've actively sought those opportunities..." 3. When offering support: "I know your team was struggling with that software implementation. How did the training session go?" 4. During follow-ups: "Last time we talked, you were considering that leadership course. Did you enroll?" 5. When delegating: "This reminds me of that process improvement idea you pitched in January. Want to take the lead?" 6. During check-ins: "How's your son's soccer season? Last time they were heading into playoffs." 7. When introducing colleagues: "Sarah, meet Tom. He shares your passion for making sourdough!" But what if your memory feels like Swiss cheese? (Asking for myself at 53!) The good news is you don't need perfect recall. Try jotting down a quick note after meaningful conversations in your phone, calendar, or CRM. Even a simple "mentioned daughter's graduation" can transform your next interaction. The effort to remember matters as much as the remembering itself. Here are the key takeaways: *Memory displays equal explicit value statements in making people feel appreciated *We underuse this strategy professionally while using it naturally in personal contexts *Small remembered details create big emotional connections *Perfect memory isn't required (phew!); notes and systems work just as well Your memory (with a little help) might just be your most underused relationship-building superpower. #relationships #coaching #leadership

The average B2B buyer is drowning in information. Research shows: Only 17% of the buying journey is spent meeting with vendors. The rest? Sorting through conflicting information. Trying to make sense of mixed messages. Drowning in content from multiple sources. I watched a deal implode last week. The prospect said: "We went with someone else because their solution was simpler to understand." Not better. Not cheaper. Simpler to understand. This made me curious. So I reviewed our process: - 17 separate emails with attachments - 9 automated follow-ups - 3 technical documents - implementation guides That's 29 separate communications. All living in different inboxes. All requiring different logins. All telling slightly different stories. No wonder they were confused. We were creating cognitive overload. The human brain can only handle 5-9 pieces of information at once. Yet we bombard prospects with dozens. Yesterday, I tried something different: For a new enterprise opportunity, instead of our usual process, I created a single digital space: - One URL they could always return to - Information organized by stakeholder role - Content that appeared in logical sequence - No unnecessary details until requested The feedback was immediate: "This is the clearest sales process I've experienced. I actually understand what you do now." They signed in half our usual sales cycle. Most sales teams obsess over: • What information to share • When to share it Almost none think about: • How to organize it • How to reduce cognitive load Your prospects aren't rejecting your product. They're rejecting confusion. Create clarity, win more deals. The simplest story usually wins. Agree?

A good survey works like a therapy session. You don’t begin by asking for deep truths, you guide the person gently through context, emotion, and interpretation. When done in the right sequence, your questions help people articulate thoughts they didn’t even realize they had. Most UX surveys fall short not because users hold back, but because the design doesn’t help them get there. They capture behavior and preferences but often miss the emotional drivers, unmet expectations, and mental models behind them. In cognitive psychology, we understand that thoughts and feelings exist at different levels. Some answers come automatically, while others require reflection and reconstruction. If a survey jumps straight to asking why someone was frustrated, without first helping them recall the situation or how it felt, it skips essential cognitive steps. This often leads to vague or inconsistent data. When I design surveys, I use a layered approach grounded in models like Levels of Processing, schema activation, and emotional salience. It starts with simple, context-setting questions like “Which feature did you use most recently?” or “How often do you use this tool in a typical week?” These may seem basic, but they activate memory networks and help situate the participant in the experience. Visual prompts or brief scenarios can support this further. Once context is active, I move into emotional or evaluative questions (still gently) asking things like “How confident did you feel?” or “Was anything more difficult than expected?” These help surface emotional traces tied to memory. Using sliders or response ranges allows participants to express subtle variations in emotional intensity, which matters because emotion often turns small usability issues into lasting negative impressions. After emotional recall, we move into the interpretive layer, where users start making sense of what happened and why. I ask questions like “What did you expect to happen next?” or “Did the interface behave the way you assumed it would?” to uncover the mental models guiding their decisions. At this stage, responses become more thoughtful and reflective. While we sometimes use AI-powered sentiment analysis to identify patterns in open-ended responses, the real value comes from the survey’s structure, not the tool. Only after guiding users through context, emotion, and interpretation do we include satisfaction ratings, prioritization tasks, or broader reflections. When asked too early, these tend to produce vague answers. But after a structured cognitive journey, feedback becomes far more specific, grounded, and actionable. Adaptive paths or click-to-highlight elements often help deepen this final stage. So, if your survey results feel vague, the issue may lie in the pacing and flow of your questions. A great survey doesn’t just ask, it leads. And when done right, it can uncover insights as rich as any interview. *I’ve shared an example structure in the comment section.

You might have heard of 𝗺𝗲𝗻𝘁𝗮𝗹 𝗮𝘃𝗮𝗶𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆 and its importance to brand purchase. But these two issues are a major sticking point in practice:  𝟭. 𝗜𝘁'𝘀 𝗶𝗻𝗰𝗼𝗺𝗽𝗹𝗲𝘁𝗲, 𝗮𝗻𝗱   𝟮. 𝗬𝗼𝘂'𝗹𝗹 𝘀𝘁𝗿𝘂𝗴𝗴𝗹𝗲 𝘁𝗼 𝗺𝗲𝗮𝘀𝘂𝗿𝗲 𝗶𝘁 The EBI defines Mental Availability as “the propensity for a brand to be thought of in buying situations.” 𝗧𝗵𝗮𝘁 𝗶𝗱𝗲𝗮 𝗰𝗵𝗮𝗻𝗴𝗲𝗱 𝗺𝗮𝗿𝗸𝗲𝘁𝗶𝗻𝗴 𝗳𝗼𝗿 𝘁𝗵𝗲 𝗴𝗼𝗼𝗱. But here’s where the science (particularly neuroscience) adds something new. All current measures of Mental Availability (“Which brands come to mind…”) rely on conscious, verbal, recall. That assumes: 1️⃣ Brand retrieval happens consciously 2️⃣ Self-report reflects the same process as real-time brand choice Both assumptions don’t fully align with what happens when we look at the brain. 𝟭. 𝗧𝗶𝗺𝗶𝗻𝗴 In low-involvement FMCG, research has suggested that a large proportion of category decisions occur within 1-2 seconds. Neuroscientific evidence shows that recognition and preference signals emerge within 𝟭𝟱𝟬–𝟲𝟬𝟬𝗺𝘀, which is 𝗵𝘂𝗻𝗱𝗿𝗲𝗱𝘀 𝗼𝗳 𝗺𝗶𝗹𝗹𝗶𝘀𝗲𝗰𝗼𝗻𝗱𝘀 before conscious awareness. This means that: • By the time a shopper realises they’ve seen a brand, the neural decision is already underway. • By the time a shopper realises they’ve seen a brand, the decision has likely already been made. This is why, at GreyMattr, we talk so much about the importance of fluency. 𝟮. 𝗥𝗲𝗰𝗮𝗹𝗹 ≠ 𝗥𝗲𝗰𝗼𝗴𝗻𝗶𝘁𝗶𝗼𝗻 Psychology distinguishes between conscious 𝘳𝘦𝘤𝘢𝘭𝘭 (slow, verbal, reflective) and 𝘯𝘰𝘯-𝘤𝘰𝘯𝘴𝘤𝘪𝘰𝘶𝘴 recognition (fast, sensory, automatic). Where we need to move forward is that the EBI is measuring the conscious part, even though behaviour is primarily driven by the non-conscious. 𝟯. 𝗕𝗿𝗮𝗻𝗱𝘀 𝗹𝗶𝘃𝗲 𝗶𝗻 𝗻𝗲𝘁𝘄𝗼𝗿𝗸𝘀, 𝗻𝗼𝘁 𝗹𝗶𝘀𝘁𝘀 In the brain, brands aren’t “thought of” in isolation. We see the brand and it activates networks of sensory, emotional, and conceptual links. Colours, shapes, and feelings trigger memory faster than words. So top-of-mind awareness (or any awareness) ≠ neural accessibility. 𝟰. 𝗧𝗵𝗲 𝗰𝗶𝗿𝗰𝘂𝗹𝗮𝗿𝗶𝘁𝘆 𝗶𝘀𝘀𝘂𝗲 Using recall to validate a recall-based construct (“brands that come to mind are bought more often”) makes Mental Availability descriptively useful but not mechanistically complete. 𝟱. 𝗪𝗵𝗮𝘁 𝗻𝗲𝘂𝗿𝗼𝘀𝗰𝗶𝗲𝗻𝗰𝗲 𝗮𝗱𝗱𝘀 If decisions are largely subconscious, we need tools that capture subconscious processes. • E𝗘𝗚 𝗰𝗼𝗻𝗻𝗲𝗰𝘁𝗶𝘃𝗶𝘁𝘆 (𝗚𝗠𝗜) informs the efficiency of brand network integration, • 𝗜𝗺𝗽𝗹𝗶𝗰𝗶𝘁 𝗮𝘀𝘀𝗼𝗰𝗶𝗮𝘁𝗶𝗼𝗻 & 𝗽𝗿𝗶𝗺𝗶𝗻𝗴 demonstrates latency-based activation, and • 𝗘𝘆𝗲-𝘁𝗿𝗮𝗰𝗸𝗶𝗻𝗴 + 𝗘𝗘𝗚 𝗰𝗼𝘂𝗽𝗹𝗶𝗻𝗴 provides insight into the speed of recognition and attentional capture These don’t replace EBI’s work; they extend it. They measure what Mental Availability is trying to describe, the subconscious accessibility of brand memory.

The smallest design tweaks can have the biggest impact. Take the “View All” link for product swatches on Nordstrom's website. When it’s grouped directly within the grid of options, it feels natural. It’s part of the same flow. Users don’t have to hunt for extra colors or sizes. They discover it effortlessly because it’s right where they’re already looking. Research backs this up. Nielsen Norman Group’s “10 Usability Heuristics” highlights the principle of “Recognition rather than Recall.” When “View all” is grouped with the color swatches, users recognize it as the way to access additional shades. If the link is separate, they have to remember to look for extra options somewhere else—often missing it. Baymard Institute’s 2022 Product Page Usability Study likewise found that up to 31% of desktop users missed extended color or size variations if the “More Colors/See More” link was placed out of the immediate scanning area. Keeping “View all” in the same chunk as the other swatches raised discoverability and lowered user frustration. What’s the result of better placement? → Higher discoverability. → Lower frustration. → A seamless shopping experience. If you’re designing for usability, remember this: Every detail counts. Putting options where users expect them isn't just a best practice—it’s a boost to your bottom line.

When we actively recall/retrieve information our brains put a little hashtag on it: #useful. And those tags compound with more retrievals. In addition, memories are best strengthened if they are retrieved just before we forget them. This means that the time between retrievals should increase with each one. Furthermore, the fewer cues we are given for recall increases the likelihood of making more associations between new information and prior knowledge. As such, learners can think analogously & apply concepts across contexts. Strategy 1: Use low stakes formative assessments as retrieval practice to enhance memory retention. Strategy 2: Incrementally increase the space between retrieval practice to maximize the effect. Strategy 3: Gradually increase the complexity of retrieval practice using the three types of recall to enhance depth of understanding. 3-4 of these retrieval events will suffice at about 15 minutes per. 🧠 Go for recall over recognition:  Don’t use multiple choice questions as a summative assessment because in the real world they won’t be given a set of options where one is the correct answer. Learners being forced to generate the information is more effective. Free recall is more effective than cued recall and recognition, though it’s prudent for learners to work their way up from recognition to recall. 🔠 Make sure the context and mode of retrieval is varied:  Mix it up. One day they post a video. Next, have them write something. The Later, have them create a diagram or map, etc. Generating information in multiple modes is even more powerful than being presented information in multiple representations. What’s more, this also goes for practicing related information in varying combinations. See Interleaving. 🌉 Make sure retrieval practice is properly scaffolded and elaborative:  Go from concrete to abstract, simple to complex, easy to difficult; from questions to answer to problems to solve. Each retrieval event along the curve should be increasingly more involved to create a Desirable Difficulty. See also Bruner's Spiraling Curriculum & Reigeluth’s Elaboration Theory. 💡 Push creation of concrete examples, metaphors, and analogies:  Concrete examples and analogous thinking have a high positive impact on memory. Especially if it is learner-generated. This provides students with the opportunity to put new, abstract concepts in terms of what they already know. It updates their existing schemas. 🔁 Give feedback, and time it right:  If you’re not giving feedback that is corrective and often, your learners might suffer from confusion or even start to develop bad habits. But don’t wait too long to do it. Check out PREP feedback and Quality Matters helpful recommendations. Be sure to fade feedback as student develop mastery. #instructionaldesign #teachingandlearning #retrievalpractice

Practical design tips for cognitive & mental-health accessibility What can we actually do, in practice, to make digital experiences calmer, safer, and easier to use? Here are some techniques that go beyond WCAG checklists, drawn from real accessibility work and user feedback: 1. Reduce cognitive load → simplify tasks Use plain language, short sentences, chunked steps. Example: GOV.UK consistently ranks as one of the best models for cognitive clarity: https://lnkd.in/eNc-Cbwx 2. Support emotional safety → avoid stress-inducing patterns Remove manipulative patterns, rushed steps, or overwhelming UI. Study: Digital design influencing emotional distress - “Digital Health Risks & Social Isolation”: https://lnkd.in/eXQSbUCz 3. Give users control over pace, motion & interruptions Provide pause/stop controls, reduce auto-refresh, allow more time. Study: Digital mental-health accessibility and processing time needs: https://lnkd.in/e_R33qZF 4. Create highly predictable navigation Users with anxiety or executive dysfunction rely on consistency. Study: “Improving Cognitive Web Accessibility” - predictability reduces cognitive strain: https://lnkd.in/e2rrKC5N 5. Allow personalisation & adaptive modes Let users reduce clutter, choose simpler layouts, alter colours or spacing. Research: Neurodivergent-inclusive design & adaptive interfaces: https://lnkd.in/eJcWnxuV 6. Support focus → minimise distractions Avoid auto-playing video, flashing banners, notification loops. Example: “Reader Mode” in Firefox & Safari is a real-world model of reducing distractions: https://lnkd.in/er8UsxDw 7. Provide emotional reassurance in UI Use confirming messages, check-ins, progress indicators, and reduced ambiguity. 8. Use multimodal presentation → support different processing styles Provide text + visuals + examples; avoid relying on one cognitive channel. Cognitive accessibility by W3C WAI: https://lnkd.in/enTWiJdJ 9. Avoid memory-heavy interfaces Don’t force users to remember steps, data, or locations → keep key actions visible. Principle: Recognition over recall, supported by decades of UX & cognitive psychology: https://lnkd.in/eGsr_9bi 10. Test with diverse minds, not only sensory disabilities Include people with ADHD, PTSD, anxiety, dyslexia, brain fog, burnout. Study: UX design for mental-health needs, comparing typical vs. cognitive users: https://lnkd.in/eZ_7mmE6 Which of these do you already apply in your design or development process? And what other good strategies have you seen that support mental wellbeing online? #WebAccessibility #InclusiveDesign #CognitiveAccessibility #MentalHealth #A11y #DesignForGood #EmpathyInDesign #UXDesign #DigitalWellbeing #AccessibilityMatters

New tech rarely dies in testing. It dies when real people have to use it. The pilot works. The demo lands. The use case makes sense. And still, it never scales. Why? Adoption measures behavior. And behavior is where the brain gets involved. Here’s the neural map to getting past the pilot phase: 👇 1️⃣ Don’t assume a successful pilot means people are ready Do this: ↳ Design for behavior change, not just proof of concept The science: ↳ The brain can like an idea and still resist changing routines ↳ The basal ganglia prefers familiar patterns over new effort 2️⃣ Don’t lead with technical performance Do this: ↳ Lead with what gets easier, safer, or faster for the user The science: ↳ The brain scans for personal relevance first ↳ If value doesn’t feel immediate, attention drops 3️⃣ Don’t ignore the fear underneath adoption Do this: ↳ Surface and reduce the emotional risk of using the tech The science: ↳ New tools can trigger fear of failure, exposure, or replacement ↳ People protect status before they embrace change 4️⃣ Don’t make the new workflow feel too different Do this: ↳ Anchor adoption to behaviors users already know The science: ↳ The brain prefers familiarity and predictability ↳ High perceived effort creates resistance fast 5️⃣ Don’t treat training like a side task Do this: ↳ Make training simple, repeated, and tied to real use moments The science: ↳ The brain learns through repetition and reward ↳ Memory strengthens when learning is applied in context 6️⃣ Don’t overload users with too much information Do this: ↳ Simplify the message and narrow the actions The science: ↳ Working memory is limited ↳ Cognitive overload reduces confidence and follow-through 7️⃣ Don’t assume logic will override politics Do this: ↳ Make adoption feel safe socially and professionally The science: ↳ Social pain lights up many of the same brain regions as physical pain ↳ If adoption feels politically dangerous, scale dies 8️⃣ Don’t make the first experience slow or clunky Do this: ↳ Create a fast first win users can feel The science: ↳ Early wins create dopamine ↳ If the first experience feels frustrating, the brain tags it as costly 9️⃣ Don’t leave the middle managers out Do this: ↳ Equip frontline leaders to reinforce the change daily The science: ↳ The brain looks to authority and peer behavior for safety cues ↳ Local managers shape whether a new behavior feels normal 🔟 Don’t stop at proving the tech works Do this: ↳ Prove people can adopt it consistently under real conditions The science: ↳ The brain trusts repeatability more than novelty ↳ Scale requires lower friction, lower threat, and clearer reward P.S. What's the last pilot you saw fail? ➡️ If your new tech is getting interest but still not making it past pilot, try this --> https://lnkd.in/gvZNBKq9 -------------------------------------------------------------------- ♻️ Share this with a founder building new tech ➕ Follow Shannon for more brain-based GTM tactics