The Mind in Motion: A Systems Model of Real-Time Cognitive Transitions

Hello, welcome everyone. It is April 21st, 2025. We're in Active Inference Guest Stream number 103.1 with Tobias Plotman discussing the mind in motion. So, we will have a presentation followed by a discussion. If you're watching live, feel free to write any questions in the live chat. Otherwise Tobias, thank you for joining. Looking forward to the presentation. Go for it. Hey, thank you, Daniel. So hi everyone, I'm Tobias and this is the mind in motion. Today I'll be sharing with you a model that I've spent the last few years developing, the neurocognitive systems model or NCSM. Before we go any further, I just want to do a little disclaimer, just saying I developed this framework independently outside of traditional academic channels, which did allow for novel insights into how cognition dynamically organizes and reconfigures itself. While this model does explain how cognition works, it also clarifies some systems sometimes fail and sometimes they break down under pressure. I encourage critical evaluation and I invite you all to consider how this might connect or contrast with your own research. So what is NCSM? NCSM proposes cognition as a recursive adaptive control architecture, not fixed or linear. It's dynamic, precision-weighted and regulated under varying constraints. The model compromises three integrated layers. MPPE, the Neurocognitive Predictive Processing Engine, is the generative core predicting and updating cognitive states. We then have CSTP, the arbitration layer, managing transitions between cognitive states. And then we have the dynamic cognitive systems or DCSM. These are five cognitive attractor modes, instinctual, social, executive, memory-driven and associative. Cognition here is framed as an ongoing inference over internal states regulated by dynamic precision and adaptive control. At the end of this presentation, you will have a mechanistic account of cognition as a recursive, hierarchical, and dynamically coupled system. A generalizable architecture grounded in neuroscience, cognitive psychology, and computational logic. A framework designed for structural adaptability capable of being reconfigured, extended, or embedded. And a model aligned with the principles of active inference enabling uncertainty minimization through predictive goal-directed dynamics. So, the way we'll be breaking this down is we'll go through DCSM first, the five cognitive systems. Then we'll go through CSTP, MPP, and once we've built out the full model, we'll be able to look at some cognitive loops in coherent systems. Building cognition, behavior, emotion into a mechanical system. And then we'll go into cognitive breakdowns, dysregulation, and maladaptive feedback loops. And then we'll get onto some edge case scenarios where we'll look at stuff like addiction, meditation, and some more interesting things. And finally, we'll end on part seven where we'll look at where this model is going, future work, and collaborations. So, first we're going to start with the dynamic cognitive systems model. Sorry. The dynamic cognitive systems model. So, there are five key attractive systems, each governing specialized cognitive domains. First, we have the instinctual core system, which governs reflexes, threat detection, urgency. Next, we have the social and environmental modulation system. This balances personal drives against social context. Next, we have ECHOS, the executive control and optimization system. This system is responsible for executive control, planning, inhibition, and strategic reasoning. Fourthly, we have NPRS, the memory pattern retrieval system. This system is responsible for memory-guided inference using past experiences to inform current cognition. And finally, we have the associative pattern recognition system. This system is responsible for symbolic reframing and facilitates creativity, insight, and symbolic adaptation. Crucially, these aren't isolated modules. They recursively influence, suppress, and reshape each…