Social Neuroscience and AI in Education: A New Approach

💬 “As students become more familiar with how the scientific process works, whether they answer their question definitively or not, this can lead to real insights, enthusiasm, and possibly a research career.” 🏅 Andrew Delamater has spent his career asking fundamental questions about how minds—human and nonhuman alike—learn from experience and how brains and artificial neural networks encode various forms of knowledge based on those experiences. Most recently, Delamater co-authored (with Michael Domjan) an undergraduate textbook, The Essentials of Conditioning and Learning, and he concluded his tenure as editor-in-chief of the Journal of Experimental Psychology: Animal Learning and Cognition, where he helped shape the direction of the field by guiding rigorous, theory-driven empirical research to publication.

🗽 Last week, New York became a hub for exploring best practices to systematically apply brain science to strengthen memory, attention, and emotional regulation in learning. With growing evidence of decline, as highlighted in a 2024 working paper by MIT cognitive neuroscientist Nancy Tsai, student executive functioning is facing a challenge that calls for attention and an active response from education stakeholders. Across grades and demographics, students are showing declines in key cognitive skills—from reasoning and flexible thinking to working and visual memory. 🧠 Neuroscience, cognitive approaches, and emerging brain-based technology solutions are offering practical, evidence-backed ways to rebuild these foundations for learning. Representing CheckIT Learning’s efforts in this field, Peg Mischler and Myriam Da Silva attended the Learning & the Brain conference, connecting with education experts to discuss effective strategies for supporting students’ cognitive growth. Thank you to everyone who stopped by our booth—we loved the conversations and are excited to be part of a community shaping the future of education ✨ #education #AI #neuroscience #teaching

Over the past years, I've spent considerable time studying books and research in Neuroscience while working behind the scenes in AI. What stands out most for me is not just the brain's complexity, but the combination of efficiency, adaptability, and partial opacity that still challenges scientific understanding. Despite decades of progress, we remain far from a comprehensive account of how the brain produces behavior, cognition, and learning. This gap becomes even more pronounced when we consider subjective experience: explaining how physical processes in the brain give rise to conscious awareness remains unresolved. At the same time, neuroscience has established several robust principles. The brain is highly plastic, continuously reorganizing itself in response to experience. Cognitive abilities can be strengthened or degraded depending on how they are used. And small differences in environments and inputs can lead to large differences in outcomes over time. Taken together, these observations suggest a practical conclusion: investing in the development and preservation of cognitive capacities, both individually and collectively, is likely to have long-term benefits that extend beyond any single lifetime. While neural systems are biologically constrained and ultimately finite, the information they generate is not. Ideas, once communicated, can persist, propagate, and influence future minds. In that sense, I think the most durable impact of cognition may lie not in the brain itself, but in the networks of people it shapes over time. #computerscience #neuroscience #humanbrain #learning #thoughts

🧠 *Interested in Computational Neuroscience, Brain Modeling, Quantum Biology, or Advanced Med-Tech Research?* _Many students and early researchers interested in NeuroTech often face a common challenge:_ ❓ How do we approach the mathematical foundations required for computational neuroscience? ❓Where should we begin if we have lost touch with mathematics and physics after school? ❓ How do mathematical frameworks actually translate into real neuroscience research and AI models? *To address these questions, we are organizing a Free Interactive Session* 🌟 focused on building the conceptual and mathematical roadmap for entering Computational Neuroscience. 📅 Date: April 12 @ 11.00 Am 🎙 *Speaker* *Prof. V. Srinivasa Chakravarthy, PhD* Head, Computational Neuroscience Lab Department of Biotechnology Indian Institute of Technology, Madras _🌟Prof. Chakravarthy is an internationally recognized researcher working on computational models of brain dynamics, neural systems, and AI-inspired neuroscience frameworks._ 👥 *Who should attend* ✔️ Medical students interested in Neurotechnology and Brain Research ✔️ Students who want to reconnect with math & physics for neuroscience ✔️ Researchers are curious about brain modeling and computational approaches 💡 _Participants are encouraged to prepare their questions to make the session highly interactive._ " _Don't let your research ideas remain unexplored just because you're unfamiliar with the mathematical tools."_ 🔗 *Join the Session:* https://lnkd.in/g9fB2nCZ 🤝 *Interested in collaborative learning, NeuroTech discussions, or research outreach?* Join our Computational Neuroscience Discussion Forum: https://lnkd.in/gA3CkJpG ~regards *QM Nexus Scholars Forum* _A Non-Profit Global Student-Led Community, India_ _Interdisciplinary Learning & Research_ Srinivasa Chakravarthy Prateek ‎ Mehwish Shoaib #ComputationalNeuroscience #Maths #NeuroTech

🧠 Neuroscience Insight: How We Learn Faster (Inspired by the TEDx Talk by Chris Lonsdale) ~ Watch the talk here: https://lnkd.in/gq9fdUrB Language learning is often seen as a slow, difficult process, but from a neuroscience perspective, the brain is actually optimized for rapid learning when the right conditions are met. In this talk, the focus is on how we learn rather than what we learn, and this aligns strongly with principles from Neuroplasticity, the brain’s remarkable ability to adapt and reorganise itself over time. ~ What neuroscience tells us: • The brain rewires itself through repetition, relevance, and emotional engagement • Learning accelerates when input is meaningful and context-based, not memorized in isolation • Active use of information strengthens neural pathways more than passive exposure • The brain prioritises patterns, not rules especially in language acquisition This explains why immersion, listening first, and real-world interaction often outperform traditional rote learning methods. ~ A simple neuroscience framework to remember: Meaning → Relevance → Attention → Memory These four elements are tightly connected in the brain: • Meaning makes information emotionally and cognitively important • Relevance signals the brain that the information matters • Attention allows the brain to focus resources on that information • Memory forms when meaningful and relevant information is repeatedly attended to In short: The brain remembers what feels meaningful and useful. ~Key takeaway: The brain is not slow; it is selective. When learning is aligned with how neural circuits naturally adapt, progress becomes faster and more sustainable. Understanding the brain changes how we approach learning from effort-heavy to strategy-driven. #Neuroscience #LearningScience #Neuroplasticity #BrainBasedLearning #Memory #Attention #LanguageLearning #CognitiveScience #StudySmart #Education #ScienceCommunication #CognitiveNeuroScience #StudySmart #Education #SkillDevelopment #LinkedInLearning #Neurosciencefacts #ChrisLonsdale #TEDxTalk #Neuroscientist #Neurosciencesociety #Fens

Well, well, well… what could be more exciting for a researcher than receiving a DOI 🚀  I’m excited to share that my thesis research paper titled “Attention-Based Multi-Feature Fusion Neuromarker for EEG-Driven Stress Classification in Learners” has been officially accepted and published in the International Journal of Clinical and Health Psychology (Springer). It is a Q1 journal with an SJR of 1.79 and an impact factor of 4.4. This work focuses on developing a robust, data-driven framework for stress detection by fusing multiple spatial and connectivity features, with the goal of enabling more reliable and interpretable neurocognitive assessments. The key contributions of this research include: • A multi-feature fusion strategy integrating complementary EEG features (Microstates, Granger Causality, and Transfer Entropy) to capture richer neural dynamics • An attention-based feature fusion mechanism to adaptively weight discriminative features, improving classification performance and interpretability • A subject-aware evaluation framework to ensure generalization and mitigate data leakage in EEG-based modelling • A new set of microstates for stress-based tasks, which was previously underexplored. I am deeply grateful to my supervisor,Soyiba Jawed, for her continuous guidance and insightful feedback throughout my thesis journey. I am also very thankful to my Co-supervisor,Imran Shafi, for his constant motivation and support throughout my master’s and research journey—especially during times when I doubted whether research was the right path for me. His mentorship has been instrumental in shaping both the technical depth and direction of this work. I would also like to thank my team and colleagues for their collaboration, valuable discussions, and encouragement at every stage of this research. 🔗 See the article here: https://lnkd.in/dC8ejud9 #EEG #BrainComputerInterface #StressDetection #MachineLearning #DeepLearning #Research #Publication #AI #Neuroscience

What if focus wasn’t something you forced, but something you could design? NeuroHack 2026: Designing the Future of Focus Hosted by CruX UCLA, UCLA Neuroscience Undergraduate Society, and UCLA Computational Biology Society, proudly sponsored by EMOTIV & Neuro. In a world of constant distraction, how do we rethink attention? NeuroHack 2026 is a week-long interdisciplinary hackathon bringing together students across neuroscience, AI/ML, computational biology, and neurotechnology to build solutions that enhance focus, cognition, and human performance. This is a space for cross-disciplinary collaboration and real-world innovation, no prior experience required. Timeline • Kickoff (In-Person): April 14 | 6–8 PM • Build (Hybrid): April 14–20 • Demo Day (In-Person): April 20 | 6–9 PM Prizes • 1st: EMOTIV INSIGHT EEG Headband • 2nd: Neuro Bundle • All teams: Exposure to leading neurotech companies, UCLA professors, and social platforms. Apply by April 12, 11:59 PM. Join us in building the future of focus. Register now: https://lnkd.in/g-3eQCHC

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

Speaker Spotlight We are pleased to have Dr. William Dorrell join our faculty for the ACNEI Computational Neuroscience Introductory School Dr. Dorrell is a theoretical neuroscientist interested in understanding the algorithms that underlie cognition and how they manifest in neural activity. He obtained his PhD at the Gatsby Computational Neuroscience Unit in London, where he developed normative theories to describe how computations are optimally represented in neural systems. His work focuses on building mathematical frameworks that explain how both biological and artificial neural networks carry out complex computations. He has applied these approaches to study representations in brain regions such as the prefrontal cortex, entorhinal cortex, and retina. More broadly, his research aims to provide a cohesive way to understand how the brain performs tasks like reasoning, timing, and decision-making. The experience we’re creating at the ACNEI-CNI School prioritizes this kind of theory-driven thinking and we’re really looking forward to Dr Dorrell’s session. For more info, visit acnei.org and stay tuned for more speaker spotlights! #ACNEI #ComputationalNeuroscience #NeuroscienceAfrica #TheoreticalNeuroscience #AI #BrainScience #ACNEICNISchool

Memory consolidation requires time, happens during REM sleep, with the cyclic-AMP Response Element Binding;(CREB) protein synthesis acting as a molecular switch.