Cognitive Learning Processes

Why do some training programs drive real behavior change while others fade from memory immediately? 🤔 In my new LinkedIn Learning course "Learning Foundations: Theory to Practice," I tackle this fundamental question. The answer often lies in how well our learning designs align with how people actually learn. When we ignore the science, we get: ·     Information overload that overwhelms working memory ·     Content that fails to connect with prior knowledge ·     Practice activities disconnected from real-world application ·     Reinforcement that doesn't sustain behavior change Learning theory isn't just academic—it's the foundation for creating learning experiences that work WITH rather than AGAINST our natural learning processes. As one example from the course: Cognitive load theory shows us that breaking complex information into manageable chunks and providing clear mental frameworks dramatically improves retention and application. This isn't just theoretical—it translates directly to better learning outcomes. What learning challenges are you facing where theory might offer practical solutions? https://lnkd.in/gSr-yhkQ #LearningDesign #WorkplaceLearning #InstructionalDesign #LinkedInLearning

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.

“Cognitive Functions Are Not a Luxury—They Are the Core of Literacy and Learning.” I was trained in Dr. Reuven Feuerstein’s cognitive work, but what I saw went beyond theory. I saw how the brain could be taught to think—and how thinking had to come before the reading strategy, before the worksheet, before the intervention plan. Feuerstein didn’t assess students by measuring their deficits. He studied their thinking. He observed how students engaged with questions, connected ideas, classified information, recognized patterns, and hypothesized. He noticed that when students were guided through introspective questioning—especially when connected to familiar experiences—what emerged was not compliance, but cognition. This became the foundation of Structural Cognitive Modifiability: That the mind can change—if we change how we teach it. Feuerstein identified three phases of thinking every student must strengthen: Input – How a student receives and perceives information Elaboration – How they process, compare, and draw meaning from it Output – How they communicate their understanding to others When these phases break down—because students haven’t had mediated, guided learning interactions—we see cognitive dysfunctions. Not because students can’t think, but because the teaching hasn’t reached the mind. Feuerstein called these deficiencies, not to label the learner, but to demand intervention. He made it clear: what schools often label as poor behavior, low motivation, or “attention problems” are really signs of untrained cognitive functions. Let’s be clear: Executive functions are important—but they’re built on cognitive functions. You can’t prioritize planning, goal setting, and attention if the brain never learned how to compare, analyze, and evaluate. When one of my LinkedIn followers dismissed cognitive functions as “just neuroscience,” I realized we still have work to do. This isn’t neuroscience—it’s pedagogy. It’s soul work. And it belongs in every classroom.

What if you had a simple guide to understanding how your learners’ brains work? Would you use it? As someone working at the intersection of games, learning and neuroscience, I know that understanding the brain can seem daunting. It’s complex—but with the right framework, it becomes a bit more accessible and actionable for those of us designing and facilitating learning experiences. Through my work with Evivve (20,000 game containers) , I’ve distilled the brain’s engagement process into five key stages, called the AFERR model: Activation, Forecasting, Experimentation, Realization, and Reflection. These stages reveal how learners process and respond to new experiences, and understanding them can help us as learning professionals to design more meaningful, impactful sessions. 🧠 I’ve attached a quick resource on the AFERR model to give you a look into each stage and some reflective questions to consider as you think about the learner’s journey. Here are some reflections to try as you explore these stages: 💎 Which of these processes aligns most with the goals of your learning experiences? 💎 Where could learners benefit from deeper reflection or experimentation in your sessions? 💎 How might understanding the AFERR model transform the way you design and facilitate learning? If these insights resonate, I’ll be sharing more on AFERR and cognitive engagement at my keynote this weekend at Indian Institute of Technology, Madras with some incredible voices in the industry. And for more on my recent UN talk, check the comments for a link. Would love to hear how this model connects with your approach to learning design in the comments! #aferr #learningdesign #neuroscience #cognitivescience #Evivve #facilitation

Daily Drop | How to Learn Anything 5x Faster Mastering a new skill or subject doesn’t always mean working harder — it means working smarter. These 10 evidence-backed learning techniques can dramatically improve how quickly and deeply you learn: 1. Feynman Technique • Pick a topic and explain it as if you’re teaching a 12-year-old. • Identify any gaps in your understanding and study them. • Refine and simplify your explanation. Why it works: Teaching forces clarity of thought and deeper comprehension. 2. Dual Coding • Combine verbal and visual information (e.g., notes + diagrams). • Describe visuals in your own words. Why it works: Activates different parts of the brain for better retention. 3. Spaced Repetition • Review material over increasing intervals (1 day, 3 days, 1 week, etc.). • Helps beat the “forgetting curve.” Why it works: Reinforces memory just before it fades, making it stronger. 4. Interleaving • Switch between related subjects while studying. • Apply knowledge across multiple contexts. Why it works: Improves critical thinking and transfer of knowledge. 5. Mind Maps • Start with a central concept, then branch into related subtopics. • Mimics how the brain organically connects ideas. Why it works: Visual mapping aids memory and helps organize thoughts. 6. Chunking • Group related bits of information into meaningful units. • Focus on one “chunk” at a time. Why it works: Reduces cognitive overload and makes complex material manageable. 7. Pareto Principle (80/20 Rule) • Focus on the 20% of content that delivers 80% of the value. • Identify the core concepts and prioritise them. Why it works: Efficiently allocates your time and attention. 8. SQ3R Method • Survey: Preview the content • Question: Ask what you expect to learn • Read: Engage actively with the material • Recite: Summarize what you learned • Review: Revisit key ideas Why it works: Builds deep comprehension and long-term recall. 9. Overcome “The Dip” • Motivation dips after initial excitement fades. • Push through the plateau by staying consistent. Why it works: True progress often follows persistence. 10. Chunked Practice • Not a label on the image, but implied: group sessions with breaks outperform long cramming. Why it works: Prevents fatigue and boosts cognitive endurance. Final Thought Learning is a skill in itself. When you master how to learn, you unlock anything you want to know.

Learning Design Tip Working memory is your brain's short-term workspace, where it processes and manipulates information needed for immediate tasks. It's the post-it note that says "don't forget to remember..." 🧠You use it to remember the beginning of this sentence as you get to the end. 🧠You use it to have a conversation, which involves listening, formulating what you are going to say, and then saying it. 🧠You use it when you're in a hurry to get out of the house and are trying to remember not to leave your phone charger on the table (I left it...it was annoying) How this impacts your learning design: Be intentional about the content you choose, the modalities you choose and always keep in mind what the working memory is doing.   Protecting the cognitive load and working within the limitations of the memory system is critical to a successful design and someone’s learning experience. Ask yourself: What does someone need to keep in mind in order to complete the task at hand? Too many things at once can cause interference in the brain of the person trying to learn and increase the demands of the working memory system. Translation: You'll be working against the brain as opposed to with it. #learningdesign #instructionaldesign #edtech #learninganddevelopment #process

HOW LAYERS OF BRAIN CELLS CREATE INTELLIGENCE: A UNIVERSAL DESIGN FOR HUMANS, AI, AND VLSI CHIPS Why does the cerebral cortex use variations of a SINGLE CANONICAL LAMINAR CIRCUIT (see Figure, left panel)? How do variations of this SINGLE CIRCUIT represent AUDITORY and VISUAL PERCEPTION, ATTENTION, LEARNING, RECOGNITION, COGNITION, and PLANNING? Why do cells in all PERCEPTUAL and COGNITIVE cortical circuits fall into SIX MAIN LAYERS? HOW DO LAYERS SUPPORT BIOLOGICAL INTELLIGENCE? My LAMINART cortical model (see Figure, right panel, for the first few stages of visual cortical processing) proposes how BOTTOM-UP, HORIZONTAL, and TOP-DOWN interactions in neocortex combine the BEST PROPERTIES OF: FAST FEEDFORWARD PROCESSING AND FEEDBACK PROCESSING whereby unambiguous input patterns can rapidly be processed in a FAST FEEDFORWARD SWEEP of activation through multiple cortical areas, slowing down when there are multiple possible groupings of the input data, until it can CONTRAST-ENHANCE and AMPLIFY the groupings with the MOST EVIDENCE before speeding up again. This is SELF-ORGANIZING SYSTEM TRADES CERTAINTY AGAINST SPEED, running as fast as it can, with the evidence that is available with which to make its decisions. FEEDBACK PROCESSING at every cortical area shapes these groupings using the ATTENTIVE LEARNING that is described below. ANALOG AND DIGITAL COMPUTING whereby the property of ANALOG COHERENCE combines the STABILITY of dig­ital computing— due to the way in which feedback loops enhance and store ANALOG decisions in SHORT-TERM MEMORY, or STM—without sacrificing the SENSITIVITY of analog computing. PREATTENTIVE AND ATTENTIVE LEARNING whereby automatic BOTTOM-UP ADAPTIVE FILTERING and HORIZONTAL GROUPING combine with task-selective TOP-DOWN LEARNED EXPECTATIONS that FOCUS ATTENTION upon, and LEARN, predictive combinations of CRITICAL FEATURES, and dynamically STABILIZE THESE LEARNED MEMORIES against CATASTROPHIC FORGETTING. These properties enable: (1)  brain DEVELOPMENT and LEARNING to match environmental constraints, and to dynamically SELF-STABILIZE MEMORIES of these processes, thereby preventing CATASTROPHIC FORGETTING; (2) automatic BINDING that groups distributed data into context-appropriate representations in multiple brain processes, ranging from PERCEPTUAL GROUPING to LANGUAGE LEARNING; and (3) ATTENTION whereby cortex selectively LEARNS and STABILIZES MEMORIES of im­portant events, while suppressing irrelevant ones. The processes in (1) solve what I call the STABILITY-PLASTICITY DILEMMA. Solving (1) implies (2) and (3). Chapter 10, among others, of my Magnum Opus CONSCIOUS MIND, RESONANT BRAIN: HOW EACH BRAIN MAKES A MIND https://lnkd.in/eiJh4Ti provide complete explanations. #mind #brain #neuralnetwork #resonance #perception #language #attention #learning #consciousness #laminar #neocortex #learning #forgetting #ai #cognition #memory #plasticity #stability #binding

Better learning design = better knowledge absorption. Cognitive Load Theory (CLT) is a game-changer for online learning. Most people overcomplicate it. But you don't need to be an expert. Here's how to optimize for cognitive load: 1️⃣ Simplify Content Delivery • Strip away the fluff. Focus on essentials. • Present info in clear, digestible formats. • Your learners' brains will thank you. 2️⃣ Chunk Complex Tasks • Break big concepts into bite-sized pieces. • It's like solving a puzzle, one piece at a time. • Learners process information more easily. 3️⃣ Leverage Microlearning • Short, focused lessons are your secret weapon. • They align perfectly with immediate learning needs. • Engagement skyrockets. Retention improves. 4️⃣ Guide Practice Intentionally • Step-by-step instructions are your best friend. • Continuous feedback keeps learners on track. • It's how you build expertise, bit by bit. 5️⃣ Tailor to Expertise Levels • One size doesn't fit all in training. • Adapt strategies to learners' existing knowledge. • Novices and experts need different approaches. Pro Tips: ☑️ Use multimedia wisely (but don't overdo it) ☑️ Provide external memory supports ☑️ Personalize with adaptive learning systems Master these steps, and your online training will shine. Cognitive load isn't just theory. It's the foundation of learning that sticks. ✔️ Which technique will you try first?

How Experts 🧑🏫 Learn Hard Things One of the world’s top competitive programmers just dropped a video that breaks down exactly how to learn hard things — and actually remember them. It’s not about grinding longer, it’s about learning smarter. Here’s the 6-step method he uses to master complex concepts intuitively (and permanently): 1️⃣ Convince your brain to care You need motivation. Passion is ideal, but even external goals (like a job or prestige) can do the trick. 2️⃣ Understand the big picture Zoom out. Don’t get lost in the weeds. Start with a high-level view before diving into details. 3️⃣ Break it down Don’t overwhelm yourself. Slice the concept into smaller chunks and master each one. 4️⃣ Try to solve it Before learning the “official” method, try solving the problem yourself. This builds deep, intuitive understanding. 5️⃣ Use your subconscious Let your brain chew on it in the background. The trick? Care about it enough that your mind keeps circling back. 6️⃣ Reinforce the learning Practice it: Apply it in different contexts. Explain it: Teach it in your own words. Explore it: Tweak the idea and see if it still holds. 🗯️ Let us know in the comments below what helps you the most #CognitiveScience #Growth #Learning

Just published: "Cognitive Load Theory: How AI Can Reduce Extraneous Cognitive Burden" - Part 3 in my series on educational psychology and AI! Working memory is the bottleneck of learning. But what if AI could dynamically manage cognitive load—presenting information at just the right pace, in just the right format, and with just the right level of support? This piece explores how intelligent content sequencing, dynamic displays, and just-in-time assistance are transforming how we approach complex learning. I also examine emerging neuroadaptive systems that may soon detect cognitive overload before the learner even notices it. Have you experienced AI tools that effectively managed cognitive load in your learning or teaching? What strategies do you use to balance cognitive demands? #CognitiveLoadTheory #AIinEducation #WorkingMemory #PersonalizedLearning #EdTech #LearningScience #EducationalPsychology New Publication: My comprehensive guide "AI-Assisted Assessment in Education" (Springer, 2025) is now available! Discover how AI is transforming educational assessment with practical implementation strategies for creating assessments that truly measure meaningful learning while reducing extraneous cognitive burden. https://lnkd.in/e7ZWc7e8