Cognitive Learning Processes

Unpopular opinion: Microlearning is making your workforce dumber. There. I said it. And the neuroscience backs me up. For the past 5 years, L&D has been obsessed with bite-sized content. “Learners are busy!” “Attention spans are shrinking!” “Make it 3 minutes or less!” But here’s what we’re ignoring: The brain doesn’t learn through accumulation of facts. It learns through pattern recognition, contextual embedding, and effortful retrieval. When you fragment complex skills into 90-second modules, you’re triggering three cognitive failures: 1. The Illusion of Mastery Short bursts feel productive. You get that dopamine hit of completion. But fluency ≠ competence. Neuroscience shows that difficult, sustained engagement—not easy wins—creates durable learning. 2. Context Collapse The brain stores information in rich networks of association. When you strip away context to hit a 3-minute target, you’re removing the very scaffolding that makes knowledge transferable to real work situations. 3. No Desirable Difficulty Bjork’s research is clear: learning should be hard. Microlearning optimizes for convenience, not for the cognitive struggle that rewires neural pathways. The Real Problem: We’re confusing information access with capability building. Yes, give people microlearning for just-in-time answers—“How do I format this PowerPoint?” But stop pretending 47 three-minute videos will develop strategic thinking, leadership presence, or complex problem-solving. What Actually Works: ∙ Spaced repetition over weeks (not days) ∙ Interleaving different concepts to force discrimination ∙ Extended practice with feedback loops ∙ Cognitive struggle followed by consolidation Deloitte runs thousands of learning programs annually. The ones that measurably change behavior? They’re never the short ones. Maybe it’s time we stopped optimizing for completion rates and started designing for neural change. What’s your experience? Are we sacrificing deep learning on the altar of engagement metrics? *** The brain comparison imagery is a conceptual illustration, but the science behind it is solid. Decades of research on spacing effects (Cepeda et al.), desirable difficulties (Bjork), and neural consolidation (McClelland et al.) consistently show that sustained, effortful learning creates more robust neural changes than fragmented exposure.

Knowing, Doing, Being: The Real Test of Learning The other day, I got a call from an ex-colleague who had just completed a course. He was excited, almost glowing with pride. I asked him a simple question: “Now that you’ve learnt something new, how will you use it?” There was a pause. The silence told me what words didn’t. So, I gave him an analogy: “Imagine you’ve joined the army and trained to use a pistol, an LMG, a drone, and a grenade. The real question is—not how many tools you know, but which one you will use, when and how.” That’s where true wisdom lies. 👉 In learning, the journey isn’t just about knowing. 👉 It’s about doing. 👉 Ultimately, it’s about being. Today, everyone is experimenting with AI tools. But many struggle to connect those tools to real-world applications. That bridge between knowledge and impact, is where domain expertise and systems thinking come in. When I design any learning, OD, or change intervention, my focus is always on translating knowledge → action → lived behaviour. Whether it’s the 70:20:10 model or the Education–Exposure–Experience approach, the goal is the same: moving people through the learning journey: 🔹 Unconscious Incompetence → not knowing what we don’t know 🔹 Conscious Incompetence → recognising the gap 🔹 Conscious Competence → deliberate practice 🔹 Unconscious Competence → mastery in flow Freud might say this is the evolution from the id (raw curiosity), through the ego (applied practice), to the superego (responsible mastery). True growth is not in collecting tools, but in integrating them into who we become. During the COVID-19 return-to-office phase, our security staff had to manually collect health details; temperature, oxygen levels, RTPCR reports. The process was chaotic, risky, and time-consuming across distributed offices. I helped the admin team reimagine the process: A simple automation using MS Forms, OneDrive, and Power Automate. Employees scanned a QR code, data flowed instantly to the central team, and potential risks were flagged in real time. No registers, no pens, no bottlenecks, no unnecessary human involvement. It was a simple example of turning learning into impact. As we embrace AI, the real opportunity is not in creating yet another fancy image or auto-drafted message—it’s in re-engineering processes, solving real problems, and freeing human potential. So, the question is not what tools you know.The real question is: Where and how will you use them? #LearningAndDevelopment #OrganisationalDevelopment #Leadership #FutureOfWork #AIInHR #SystemsThinking #BehaviouralScience #HumanPotential #ChangeManagement

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

💡 Are You a "Top Trainer" or Just a Trade Expert? I see incredible tradespeople being instantly labeled "top trainers" in the vocational sector. We celebrate their industry expertise, but often skip a crucial step: understanding how humans actually learn. My personal journey began back in 1997, when I started spending my own money - ultimately over £20,000 - to study educational psychology and instructional design. I became a dual professional, studying everyone from foundational theorists such as Piaget and Vygotsky to experts on multimedia learning like Richard E. Mayer. This investment taught me that even state-of-the-art simulated environments are only part of the solution. As David Hargreaves argued in 1996, we must adopt evidence-based practice - respecting both trade science and learning science. 🧠 Stage 1: Design Smartly (Mayer's Tips) You don't need to spend £20k to improve, just apply a few research-backed principles. Since almost everyone uses slides, make your PowerPoints and e-learning effective using principles from Mayer's Cognitive Theory of Multimedia Learning (CTML), which reduces cognitive load: 1. Stop Reading Your Slides (Redundancy Principle): Use images and graphics while you speak. Slides should complementyour speech, not duplicate it. 2. Cut the Clutter (Coherence Principle): Remove all decorative elements or text not essential to the core goal. If it doesn't support learning, delete it. 3. Put Graphics and Text Together (Contiguity Principle): Place labels, arrows, and key definitions immediately next to the relevant graphic. 📉 Stage 2: The Retention Crisis (Ebbinghaus's Reality) Even with perfectly designed slides, training often fails because we ignore the most fundamental reality of memory, researched over a century ago by Hermann Ebbinghaus (1885). Ebbinghaus's Forgetting Curve shows that unless knowledge is actively used or reviewed (as later explored by Bartlett), it dissipates dramatically within days. The problem with many courses is that students leave with a certificate but never engage in post-course practice. The knowledge is lost. The hallmark of a great engineer is continuous application and engagement with peers. Trainers must encourage all learners - including the 9,000 people tax payers have paid for to be lifelong learners by encouraging them to continually apply that knowledge. Being a true "top trainer" means respecting the learner's brain across the entire learning lifecycle. #EvidenceBasedEducation #VocationalTraining #InstructionalDesign #ForgettingCurve #LifelongLearning Charlotte Lee Alex Butcher Katy King Matt Isherwood Andrew Johnson Tom Arey John Hancock Madeleine Gabriel BPEC LCL Awards Dr Matthew Aylott Rhiannon de Wreede SNIPEF

If you feel like you’re sprinting through the curriculum you’re not alone. 🏃♂️ But here’s the catch: Cognitive science says fast teaching doesn’t equal deep learning. Cognitive Load Theory (Sweller, 1988) reminds us that the brain’s working memory is limited. When we overload it, learning stalls no matter how great the content is. This isn’t just about students. It’s about teacher sustainability too. So many of us are under pressure to “cover everything.” But here’s the truth: Trying to do too much leads to shallow learning and teacher burnout. What works better? Teaching with the brain in mind: • Chunking content into manageable parts (Miller, 1956; 7±2 rule) • Using worked examples to reduce extraneous load (Sweller, 2006) • Providing pause time so students can consolidate and process • Eliminating distractions—less “busywork,” more focus • Building schemas through repetition, connection, and reflection • Focusing on one learning intention at a time As Willingham (2009) puts it: “Memory is the residue of thought.” We must give students time to think deeply not rush to the next thing. Slow learning is strong learning. Let’s ditch the overload and create space for what really matters: Clarity. Connection. Purpose. And yes - our own wellbeing too. #CognitiveLoadTheory #EvidenceBasedTeaching #TeacherWellbeing #DeepLearning #PrimaryTeaching #CurriculumDesign #BrainBasedLearning #EducationResearch #NeuroaffirmingPractice #LessIsMore

📚 A Pedagogically Intentional Framework for Lesson Planning High-quality instruction is the result of deliberate instructional design, not chance. This HyperDoc-based lesson planning framework functions as a conceptual and practical guide for educators seeking to design learning experiences that are rigorous, inclusive, and learner-centered. 🔹 Engage – Activating Curiosity & Prior Knowledge Instruction begins with a cognitively stimulating provocation that activates schema, builds relevance, and establishes purpose. Strategic hooks foster intrinsic motivation and emotional investment in learning. 🔹 Explore – Inquiry-Driven Knowledge Construction Learners interact with multimodal, curated resources that promote investigation, sense-making, and conceptual exploration. This phase privileges student voice, choice, and agency while supporting constructivist learning practices. 🔹 Explain – Conceptual Clarification & Explicit Instruction Through targeted instruction, guided discourse, and formative checks for understanding, educators address misconceptions and consolidate conceptual clarity. Learning intentions and success criteria are made explicit to anchor understanding. 🔹 Apply – Authentic Transfer & Skill Integration Students engage in performance-based tasks that require the application, synthesis, and transfer of learning. This stage deepens understanding by situating knowledge in authentic, real-world contexts. 🔹 Share – Feedback, Discourse & Knowledge Co-Construction Learners communicate their thinking, engage in peer critique, and respond to feedback. This social dimension of learning strengthens metacognition, accountability, and collaborative competence. 🔹 Reflect – Metacognitive Awareness & Goal Orientation Structured reflection enables learners to evaluate their learning strategies, monitor progress, and set intentional goals—cultivating self-regulated and reflective learners. 🔹 Extend – Deep Learning & Cognitive Stretch Extension opportunities provide pathways for enrichment, interdisciplinary connections, and higher-order thinking, ensuring sustained engagement beyond core instructional time. ✨ This framework serves as a pedagogical roadmap for lesson planning, firmly aligned with Universal Design for Learning (UDL) principles. It ensures accessibility, differentiation, and equity while maintaining high expectations and cognitive demand. 💡 Intentional lesson design transforms classrooms into spaces of deep inquiry, authentic engagement, and meaningful learning. #PedagogicalDesign #LessonPlanning #InstructionalExcellence #UDL #StudentAgency #InquiryBasedLearning #AssessmentForLearning #DeepLearning #EducationLeadership

The Cognitive Dissonance of AI Literacy As AI transforms education, it exposes a fundamental split in how we understand cognition. I call it a gap between an empirical model and a cybernetic model of cognition. The former sees learning as the accumulation of discrete knowledge, measured by outputs. The latter sees it as a recursive and relational process of meaning-making. Yet most education policy, AI integration, and AI literacy efforts continue to operate within the empirical view—often without realizing it. But in the age of AI, this unspoken assumption becomes a liability. The empiricist view of learning emerged from the Enlightenment, where knowledge was grounded in sensory observation. Locke and Hume imagined minds as blank slates, awaiting input. This framework was institutionalized in 19th-century factory-style schooling—sequenced, standardized, and optimized for efficiency. In the 20th century, behaviorist psychology (e.g., Skinner) reinforced the idea that learning could be engineered through inputs and outputs. These logics persist in modern education policy, where accountability systems and “evidence-based” reforms reduce learning to what can be measured, flattening interpretation, context, and complexity. The cybernetic mode of cognition, by contrast, challenges the premise that knowledge is static, extractable, and internal to the individual. Drawing from systems theory, second-order cybernetics, and embodied cognition, this mode sees learning as relational, contextual, and co-emergent. N. Katherine Hayles offers a crucial reframing: “cognition is a process that interprets information in contexts that connect it to meaning.” In this view, meaning is emergent, not delivered. Hayles builds on Maturana and Varela’s theory of autopoiesis, Margulis and Sagan’s symbiogenesis, and Lovelock’s Gaia hypothesis—each modeling life as recursive interaction. Michael Peters' bioinformationalism and postdigital knowledge ecologies reimagines education as a recursive, relational, and ecologically situated process of knowledge formation. Robert Rosen’s relational biology along with Fields and Levin’s Reference Frame Theory show how biological systems generate dynamic internal models across space and time. Ilkka Tuomi extends these ideas into education, emphasizing the social and epistemic embeddedness of cognition resistant to empirical capture. And Rachel Horst situates AI literacy within machinic ecologies. This genealogy converges with Mark Hansen’s work on affective infrastructures, and the materialism of Barad, Haraway, and others who refuse the divide between knower and known. To adopt the cybernetic mode is to rethink what learning is, what counts as knowledge, and how we participate in cognition. If AI changes how we write, read, and learn, then AI literacy programs must begin with how we think about learning and knowledge construction. Right now, they don't. And until they do, we will keep building the wrong systems. #AI_Literacy

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.

Key Scientific Support Neuroscience research strongly backs this idea. Mental imagery and simulation activate overlapping brain networks with actual perception and action: - Similar Neural Activation: Imagining an object (like an apple) or action engages many of the same regions as seeing or performing it, including visual cortices, motor areas, and reward-related structures like the nucleus accumbens. For example, vivid mental rehearsal in athletes strengthens neural pathways akin to physical practice, improving performance via neuroplasticity. - Learning from Imagination: Recent studies show people can learn preferences or associations from purely imagined scenarios. In one 2025 experiment, participants who vividly imagined positive interactions with neutral individuals developed stronger liking for them, with brain scans revealing reward prediction errors—similar to real rewarding experiences. This suggests endogenous (internal) prediction errors drive learning from hypotheticals. - Fear and Extinction: Imagined exposure to threats can reduce fear responses (extinction learning) almost as effectively as real exposure. A landmark 2018 study found that repeatedly imagining a feared sound (paired initially with mild shock) diminished fear, activating threat-processing regions like the ventromedial prefrontal cortex and amygdala similarly to actual exposure. - Vicarious and Observational Learning: We also learn from others' experiences (e.g., stories or observation), which aligns with your point about children learning caution from tales. Mirror neurons and related systems fire when observing actions or emotions, simulating them internally and building lessons without direct risk. - Predictive Simulation: The brain constantly runs "what-if" simulations for prediction and preparation. The hippocampus and default mode network replay past events and construct future/hypothetical ones, treating them as preparatory experiences. This efficiency allows growth from anticipated joy, avoided pain, or rehearsed skills. These findings highlight the brain's predictive, simulation-based nature—it's not just reacting to reality but actively constructing and learning from internal models. Reflection and mindful imagery can indeed "train" biology, with applications in therapy (e.g., imaginal exposure for anxiety), education, and resilience. On the Quantum Comparison Analogy to quantum ideas—multiple possibilities coexisting mentally until one is "chosen"—

Mental models shape the way humans think, interpret, and make decisions. Understanding and cultivating them--individually and collectively--is vital, as we imagine new systems. Mental models are not rigid or exact--like maps, they simplify and compress complexity. The underlying mechanisms or terminology continue to evolve with scientific debate. But from a practitioner's perspective the concept is helpful. Let's explore. THE ORIGIN OF MENTAL MODELS First outlined by Kenneth Craik in 1943, the concept describes how our minds build internal representations--"models"--of reality to predict outcomes and solve problems. Over the decades, theorists like Johnson-Laird refined this to reveal how these models underpin everyday reasoning. BOUNDED RATIONALITY Herbert Simon's principle suggests that people are only rational within the limits of what their mental models and cognitive resources can handle, implying that mental models can lead to systematic errors and biased reasoning. COGNITIVE PSYCHOLOGY In psychology, Aaron Beck's work showed that our automatic thoughts--immediate, unconscious responses--stem from deeper schemas, essentially mental models about ourselves and the world. These models (or schemas) guide rapid judgments and emotional reactions, influencing everything from confidence to anxiety. COGNITIVE DIVERSITY IN TEAMS Scott Page's research on cognitive diversity reveals the power of varied mental models. Diverse teams bring different perspectives, heuristics, and approaches--their collective mental models--leading to greater creativity and more effective problem solving. In today's organizations, encouraging cognitive diversity is key to innovation and adaptability. THINKING FAST AND SLOW Thinking fast and slow, popularized by Daniel Kahneman and Amos Tversky, connects directly: System 1 leverages intuitive, model-based thinking for quick decisions, while System 2 engages slower, analytical processing often rooted in deliberate mental models. Both systems rely on internal frameworks, but in different modes and speeds. DOUBLE-LOOP LEARNING: QUESTIONING OUR ASSUMPTIONS Double-loop learning offers a crucial perspective for understanding mental models and driving genuine growth. Our mental models are built atop a series of assumptions, often formed unconsciously. Day-to-day, we mostly engage in single-loop learning, making small adjustments to routines without challenging our underlying beliefs or thought patterns. Double-loop learning, pioneered by Chris Argyris, prompts us to step back and question these deep-seated assumptions and the mental models they support. This kind of reflection leads to powerful shifts in thinking, enabling innovation, adaptability, and transformation. By continuously revisiting and revising our mental models, we become more effective learners, leaders, and collaborators--equipped to thrive in rapidly changing environments. We baked these insights into the development of Strategic Doing.