What happens when the neuro-buzzwords and theories everyone is using turns out to be incomplete?
When we build our explanations on someone else's terminology,
we inherit whatever happens to that terminology later.
Some of it gets debunked. Some of it turns out to be wrong. A lot of it turns out to be incomplete.
Systems language helps us keep the frame open.
It lets us say:
this is what we understand so far about how these networks are talking to each other…
Our words will keep updating as the understanding does.
“The systems-thinking lens allows us to reclaim our intuition about whole systems and hone our abilities to understand parts,
see interconnections, ask “what-if ” questions about possible future behaviors,
and be creative and courageous about system redesign.”
― Donella H. Meadows, Thinking in Systems: A Primer
Over the course of creating maps of people’s brains, heart rhythms and other physiological markers over the years - whether in intensive outpatient clinics, high performance centers, or brain training programs for kids, teens and adults with anxiety, adhd and learning challenges - one thing showed up again and again as we watched how people changed over time.
It was all about communication among networks and systems across the brain.
Sometimes signals were moving too fast. Sometimes too slow. Sometimes there was hyperconnectivity in certain areas.
And when we sat down to explain what we were seeing, we never talked about one part of the brain. It was hard to. All of it is working together as a system.
Researchers have put it this way: trying to understand how we perceive by studying only neurons is like trying to understand bird flight by studying only feathers.
So many things we care about show up between the pieces. You CANNOT see them by looking at any piece on its own.
When we say this area does “fear”. That one ‘does’ language. This ‘part’ or this ‘nerve’ is activated..
It’s clean and easy to explain.
But.. it doesn’t hold up.
Neuroscience explanations often age better when they're based on systems, networks and functions.
Our continuously evolving understanding of the brain shows us that:
- the same brain area joins different networks depending on what is happening. Change the context, and the role changes with it.
- there is no one right way to draw the lines around a network.
Researchers can slice the same brain a lot of different ways and get useful pictures each time. No single picture is the true one. - One ‘part’(for example, the amygdala) has more layers, regions and functions than we previously might have known - and each of those layers or sub-regions function differently depending on context
Reflection questions for your week
- Where am I leaning on one term or one theory to carry a whole explanation?
- What would change in how I describe someone's experience if I described how their systems are communicating instead of naming a location?
Our understanding is meant to keep evolving
The brain and nervous system are always changing.
Our language about it can change too.
Every time we choose a wider frame, we leave room for what we do not know yet*.
Wishing you continuously expanding spaces of delightful uncertainty and unknowing…
Stefanie
*(and this of course applies to everything I share! All of it incomplete, unfolding.. Continuously evolving… )
My absolute love and passion for Systems Thinking (and in particular ‘embodied systems intelligence’.. .is why I’m building micro-courses for professionals.
I hope to share the joy, empowerment and wonder that we can all gain from a systems perspective*.
In Teach the Nervous System, we do not memorize brain parts. We look at a whole landscape of nervous system states that is always moving and always emerging. We also look at nervous systems as interrelated systems, each one shaped by the ones around it, by our history, and by the history of those around us.
In Teach Mindset Science, we talk about the shadow side of plasticity, because brain ‘growth’ is not all positive. And we hold onto the fact that nervous system states still matter, even when the topic is mindset.
In The Brain Science Foundations Toolkit, we start with the language itself. Across the nine audio seminars, we build a vocabulary you can keep using as the science keeps moving, so that when the terminology shifts, your way of explaining it can shift too instead of collapsing. No brain myths. No buzzword neuroscience.
In Reach the Resistant Brain, we look at resistance as information about how someone's systems are predicting and protecting. We look at what has to be happening across those systems for new information to get through, and we hold onto the fact that no message ever reaches someone in isolation, each one arriving inside a history and a room full of other nervous systems.
A wider systems view lets us paint with more colors and with a richer texture for what people are actually going through. That matters most with the hard things, like trauma and stress. Those are the places where a narrow explanation can constrain our understanding and how we support ourselves and others.
If you want a more systems way of talking about nervous system regulation and mindset with the people you work with, these two courses is where I’d start.
*It’s also why I’m so deeply honored to have been selected as a leader for MIT’s Human Agency Platform.. Where we are all about systems thinking and bridging realms of human experience to re-imagine human-planetary flourishing. We’ll have some events this fall, and I’ll keep you posted!
**All of my micro-courses are designed to be used together. The order to do them in is more based on what sparks your interest first! You can binge watch the 5-10 minute videos and then return to them and the supporting material later 🙂
References and Further Reading:
Chang, E. H. (2024). Bridging complexity through integrative systems neuroscience. Frontiers in Systems Biology, 4, 1487298. https://doi.org/10.3389/fsysb.2024.1487298
Pessoa, L. (2014). Understanding brain networks and brain organization. Physics of Life Reviews, 11(3), 400–435. https://doi.org/10.1016/j.plrev.2014.03.005
Marr, D. (1982). Vision: A computational investigation into the human representation and processing of visual information. W. H. Freeman.
