Neuroscience-Backed Learning Structure: Concept → Application → Detail

If you want #learning or #change to stick, structure the session differently. #Neuroscience shows we retrieve memories backwards. We recall the concept first, then application, then detail. So a more brain‑aligned structure is: Concept → Application → Detail What that looks like: ☑️Training: Start with the core idea, apply it to real work, then introduce tools or steps ☑️Change or strategy updates: Lead with meaning, clarify implications, finish with detail ☑️Status or exec updates: What it means, what it affects, then the data ☑️Facilitated sessions: Name the concept, let people apply it, use detail to sharpen thinking This makes learning easier to retrieve and change easier to adopt when pressure hits. If you’re interested in how we apply neuroscience to learning, change, and adoption strategies, let’s connect and share what’s working. Learn more here https://lnkd.in/gx2GjnY5

I am delighted to share one of my works titled “Social Neuroscience: AI for Education,” published as a book chapter with Elsevier (Academic Press) — a globally recognized platform for high-quality research. This chapter explores the fascinating intersection of Social Neuroscience, Artificial Intelligence, and Education, focusing on how understanding human cognition and behavior can transform learning systems and policy frameworks. Key Highlights: 1. Bridging Neuroscience and Public Administration perspectives 2. Role of AI in enhancing educational outcomes 3. Multilevel understanding of human behavior in social systems 4. Implications for future research, public policy, and pedagogy 🙏 Grateful to my institution, colleagues, and students for their constant support and inspiration. #Elsevier #AcademicResearch #FacultyLife #PhD #Innovation #EducationMatters #PublicAdministration #AIinEducation #SocialNeuroscience #Research

The "Interval" of learning Neuroscience just confirmed what we’ve suspected: Practice doesn’t make perfect. Timing does. A new study has upended the "Pavlov’s Dog" model of learning. It turns out the brain doesn't just count repetitions; it calculates the time between rewards. If you space a lesson 10x further apart, the brain learns 10x more from that single experience. Why does this matter for AI? In the world of LLMs and agentic systems, we are often obsessed with "more"—more data, more tokens, more RAG chunks. But this biological discovery points to a different path: Structural Context over Sheer Volume. This is the core of AIgogy™: 1. The Brain is a Pattern-Value Engine: It ignores the "noise" and scales learning based on the structure of the experience. 2. AI is a Mathematical Mirror: Just as the brain "works backward" from a reward to find the predictor, AI works backward from a prompt to find the most probable structural pattern. The "AIgogy™ Connection": We often fail with AI because we "cram" it with unstructured data (the digital equivalent of repetitive, meaningless clicks). If we want AI to truly "learn" a domain—rather than just mimic it—we have to stop treating it like a database and start treating it like a learner. By providing structured "intervals" of high-value context, we bridge the gap between "vibe-coding" and reliable intelligence. The takeaway: Whether it’s a neuron or a neural network, the goal isn't to see the pattern more often. It’s to understand the significance of the pattern within a clear structure. #AIgogy #Neuroscience #MachineLearning #AITraining #FutureOfWork #BrainScience #ContextEngineering

Published in Nature Neuroscience, new research reveals that timing, not repetition, drives associative learning. By showing the brain prioritizes the delay between rewards, the study upends century-old assumptions and suggests new ways to understand habit formation.

I am pleased to share my recent review article: A Review on Stanislas Dehaene’s Model of How the Brain Thinks and Hierarchical Model of Conscious Processing and Metacognition In this paper, I examine Stanislas Dehaene’s Global Neuronal Workspace theory and propose a hierarchical extension in which metacognition is modeled as a higher-level regulatory layer. The model introduces a computational interpretation of conscious processing using a UNet-like autoencoder architecture combined with a top-level metacognitive autoencoder that can evaluate, refine, and modify lower-level representations. This framework aims to connect cognitive neuroscience, predictive coding, and modern deep learning architectures in order to simulate reflective and ethical aspects of human cognition. The article has been published in Advances Brain Research and Neuroscience. I would be happy to receive comments, feedback, or suggestions from researchers working in neuroscience, cognitive science, artificial intelligence, and philosophy of mind. #Neuroscience #Consciousness #Metacognition #ArtificialIntelligence #CognitiveScience #DeepLearning

This year’s Discovery Day explores The Illusive Brain. We warmly invite you to join us for a creative, interactive and inspirational learning journey like no other. Our Discovery Morning will take students on a fascinating journey through the brain, packed with mind-blowing facts, hands-on experiments and awe-inspiring discoveries. We are also thrilled to welcome Dr Dan Adams, who will be joining us live from California to speak with students about his work with Neuralink, the pioneering neurotechnology company founded by Elon Musk. About Dr Dan Adams Dr Dan Adams leads the Vision Group at Neuralink. He is a globally recognised visual neuroscientist specialising in binocular vision in humans and non-human primates. His research uses psychophysical, behavioural, electrophysiological and anatomical techniques to study the primate visual system, from the retina all the way to the cortex. An exciting day of curiosity, science and discovery awaits. 🧠✨ . . . #DurhamInternationalSchool #DurhamKenya #DiscoveryDay #Education #ScienceDiscovery

For years, scientists thought the brain learned by simplifying information from the world around us. But new research from the University of Rochester and our Del Monte Institute for Neuroscience suggests learning may actually depend on something more sophisticated: neurons constantly combining what the brain sees with what it expects. “It’s a bit like a group of people solving a problem,” says Professor Adam Snyder Adam Snyder. “Instead of everyone working in isolation as efficiently as possible, learning makes them communicate more. That shared information makes each individual better informed and potentially makes the group more flexible and adaptive.” These findings from graduate student Shizhao Liu and professors Snyder and Ralf Häfner are challenging long-standing neuroscience theory and could reshape how scientists think about perception, learning disorders, and artificial intelligence. #URochesterResearch | https://uofr.us/47BnDaC

The College Theatre was packed this week with Biology, Psychology, Applied Science, Health & Social Care, and Human Biology students all wanting to hear from neuroscientist Dr Paul Taylor from the University of Zurich. He gave a fascinating talk titled “What we don’t know about how the brain works… and how we’re figuring it out: An insider’s guide to brain decoding and machine learning.” Dr Taylor shared insights into some of the major challenges currently facing neuroscience. In particular, he explored the tension between traditional views that specific brain regions control certain behaviours and newer ideas that complex brain networks work together to generate cognition and behaviour. He also discussed the challenge of applying laboratory-based neuroscience research to real-world contexts, and how AI and machine learning are increasingly helping researchers tackle these questions. Student Jess C said: “What captured my imagination was that there are still so many unanswered questions about how the brain works. It’s exciting to think about the different theories and how they might develop.” Student Yahya B said: “Dr Taylor helped me see how scientists can decipher brain activity while people view specific stimuli. The complexity of the brain makes it fascinating, and the talk made me curious about how far neuroscience could go in the future.” Dr Taylor also encouraged students considering careers in science — or any field requiring analytical thinking and curiosity — to make the most of the growing availability of open scientific data, preprint research, and AI tools to explore real research questions. A huge thank you to Dr Taylor for sharing his expertise and inspiring our students to think about the future of neuroscience. #Neuroscience #STEM #BiologyALevel #AI #FutureScientists #SixthForm

Facebook just built a model that can predict how your brain responds to videos, audio, and text. TRIBE v2 is a deep multimodal brain encoding foundation model. It predicts fMRI responses to naturalistic stimuli across three modalities. The system integrates pretrained feature extractors: LLaMA 3.2 for text, V-JEPA2 for video, and Wav2Vec-BERT for audio. A Transformer architecture maps these representations to cortical surfaces. This enables zero-shot predictions across subjects, languages, and tasks. The model was trained on over 500 hours of fMRI data from 700 individuals. That's massive scale for neuroscience research. What makes TRIBE v2 groundbreaking: → Simulates neuroscience experiments in silico → Denoises noisy fMRI data to produce canonical brain patterns → Supports brain visualization and ROI analysis → Built with PyTorch Lightning and PyVista tools → Available on Hugging Face with 89 likes showing community interest This isn't just academic research. It's a foundation model for in-silico neuroscience. Researchers can now run virtual brain experiments without new fMRI scans. The implications for understanding cognition are profound. 📷 Image credit: Gemini Nano Banana #TheAIConsultant #AI #Neuroscience #MachineLearning #BrainEncoding #FMRI #DeepLearning

A new document from the BackupME® research program is now available. “From the Subjective Neural Network to the Cognitive Device — Computational Modeling of Cognitive Identity.” This article explores a central question emerging at the intersection of neuroscience, cognitive science, and artificial intelligence: if cognitive identity emerges from a network of neural relationships, can that structure be modeled computationally? The document outlines the conceptual and technical foundations behind the BackupME® approach, focusing on the modeling of subjective cognitive architectures through high-density semantic graphs and their potential integration into a dedicated cognitive device capable of interacting with a modeled cognitive network. To make this research accessible across different scientific and technological communities, the article has been published in four languages: • Italian • English • Russian • Chinese This multilingual format reflects the global nature of the discussion around artificial intelligence, cognitive modeling, and the future of memory and identity. You can explore the full document here:📄 #BackupME #Neuroscience #ArtificialIntelligence #CognitiveArchitecture #FutureOfMemory