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Today, we’re tackling something pretty intriguing: a new perspective in cognitive science called Kinematics of Brain Activities. We’ve got excerpts from a paper and an academic poster abstract you sent over.
Our goal is to understand the heart of this model—how it challenges or builds upon current thinking about how the brain works. The mission, let’s say, is to cut through the jargon and uncover potential “aha” moments, because this model seems to view cognition in a truly physical way.
Absolutely. And what’s striking is that this material isn’t just suggesting small tweaks to existing theories. No—it’s proposing an entirely different framework for how the brain creates thought, emotion, even consciousness. It’s aiming to fill what the authors see as a major gap by offering a theory that’s fully physical, grounded in dynamic processes.
Okay, so this isn’t just another variation on the standard cognitive science models. The sources clearly state that Kinematics of Brain Activities is distinct from the main paradigms.
Can you maybe paint a quick picture of that landscape? Where does this model fit—or not fit?
Sure. Cognitive science has long had a few major schools of thought:
- Symbolic cognition — the brain as a computer processing symbols and rules (think classical AI).
- Connectionism — modeling the brain with neural networks where properties emerge from patterns.
- Embodied cognition — emphasizing the role of the body and environment in shaping thought.
- Predictive coding — the brain as a prediction machine, always updating based on new data.
This new kinematic model offers a distinct alternative to all of those.
So how does it define cognition at its core? What’s the non-negotiable definition?
The sources define cognition as a physical kinematic phenomenon, arising from phase-coordinated strain flows through the brain’s viscoelastic architecture.
That’s a big shift.
“Viscoelastic architecture” and “phase-coordinated strain flows” are very different from just neurons firing.
Help us picture that—what does this mean?
Imagine the brain less like wiring and more like a complex gel—something that can deform and spring back. That’s viscoelasticity. Now imagine energy moving through this gel in coordinated waves. That’s strain flow.
“Phase-coordinated” means these waves are synchronized—locked in step.
They propose names like Kinematic Cognitive Science or Continuity-Based Neurokinematics, to sit alongside things like connectionism or predictive coding.
Instead of action potentials, we’re visualizing energy waves moving through brain tissue. That’s the core image.
So, what are the fundamental components of this model?
- Neural Strain Flow – the continuous, coordinated movement of energy (both mechanical and electrochemical) through the brain.
- Strain Packages (Memory Units) – stable, localized patterns of these waves. Memory is not stored data but a repeating dynamic pattern of physical energy.
- Strain Carrier Waves – oscillations mainly between layers of the cortex; think of them as background rhythms supporting higher-level thought.
- Balance Fields – gradients of strain across the cortex that influence which cognitive pathways are activated or suppressed.
It sounds holistic and dynamic—but does it connect with existing theories?
Yes. It explicitly draws on:
- Dynamical systems theory, where cognition evolves continuously over time.
- Embodied/enactive cognition, focusing on physicality—but it zooms into the brain’s internal mechanics.
- Neurophenomenology, aiming to link subjective experience with neural processes.
It offers a new physical language for this bridge.
What about predictive coding? That’s huge right now.
It doesn’t reject predictive coding—it reinterprets it.
Instead of minimizing informational surprise or free energy, the brain is seen as trying to maintain strain equilibrium—a physical balance of forces. Prediction, in this view, is rooted in the brain’s physical need for mechanical stability.
So where does it really break new ground? What are the novel interpretations?
Memory – Not static traces, but recurrent patterns of synchronized strain flows.
Forgetting – Maybe a loss of coherence in those patterns, or interference.
Emotion – Seen as vertical pulses of strain, possibly moving from deeper structures.
- The amygdala acts like a strain booster.
- The hippocampus shapes emotional waves based on memory themes.
Consciousness – Not an on/off switch, but a continuum of strain coherence.
- More coherence = more integrated awareness.
- “Continuity packages” are large, integrated patterns linking motion, form, and narrative.
Even dreaming gets a reinterpretation.
Dreaming is hypothesized to be a kinetic rebalancing mechanism.
During sleep, the brain experiences lower friction, allowing built-up tension to reorganize and restore balance. It’s not just noise—it may be physically necessary.
What are the broader implications if this model is valid?
- AI – Could inspire new architectures simulating dynamic strain flows, not just neural nets.
- Mental Health – Disorders could be seen as disruptions in strain coherence—new ways to diagnose or intervene.
- Evolution – Suggests that increasing cognitive complexity may reflect more advanced kinematic capacities.
Where does this model sit in the history of cognitive science?
It positions itself as a next big leap—just as past revolutions focused on computation or networks, this one introduces a physical-kinematic layer grounded in the brain’s material structure.
It aims to unify physics and neuroscience, potentially linking subjective experience with mechanical processes.
Can we map these ideas to familiar cognitive science concepts?
Yes—here are a few analogies:
| Kinematic Concept | Possible Analogy in Cognitive Science |
|---|---|
| Neural Strain Flow | Neural activation pathways |
| Strain Packages / Memory Units | Memory “chunks” or cognitive schemas |
| Strain Carrier Waves | Neural oscillations and synchronization |
| Phase Wave Locking | Binding problem solutions (multisensory integration) |
| Emotion Pulses | Affective states, somatic markers |
| Hippocampus as Theme Organizer | Narrative structure in memory |
| Amygdala as Strain Booster | Emotional arousal amplification |
| Continuity Packages | Dynamic integration of self and experience |
| Kinematic Loop | Perception–action cycles |
| Strain Balance Field | Homeostasis, predictive coding’s drive for stability |
| Dreaming as Redistribution | Emotional/memory consolidation theories |
| Fractal Motif Units | Hierarchical mental structures or schemas |
So where is this model most aligned, and where is it truly different?
- Closest to dynamical systems theory (focus on continuous change).
- Shares core ideas with embodied cognition and neurophenomenology.
- Touches on predictive coding, but its mechanism (physical strain vs. informational prediction) is different.
- Only partial overlap with connectionism.
- Major divergence from symbolic models—no symbols, no rules.
So what’s the takeaway? What’s the core message here?
The central idea is that this model offers a genuinely new and fundamentally physical way to think about how the brain creates the mind.
It shifts away from computational or informational metaphors and instead sees the brain as a dynamic, physical system, where cognition emerges from continuous flows of energy—mechanical and electrochemical—through brain tissue.
And this perspective could reshape how we think about:
- Memory, emotion, and consciousness
- Mental health
- Artificial intelligence
- The evolution of cognition
It invites us to stop thinking of the brain as a “wet computer” and start seeing it as a pulsing, flowing, dynamic physical system—creating our reality through strain and motion.
So here’s a final reflection:
If your thoughts and feelings are physical—tensions, releases, waves flowing—how does that change how you relate to them?
What if your consciousness emerges from a kinematic field of dynamic integration?
What new questions does that open for you—about who you are or what it means to be?
It’s a lot to ponder.