The Distributed Brain thoughts below are about how our understanding of localization of function is changing based on new info. Things change. The old ideas are no longer correct.

Many of you grew up with a very tidy story of the brain and behavior: this has one patch that does vision, another strip does movement, another chunk “doing” decision‑making and so forth. A lot of popular neuro‑leadership and neuro-learning work leans on that localization view because it’s simple and teachable. But is it true?

(Note: I am doing this article for me, researching the new ideas about brain functioning because I wanted to re-educate myself on the current findings. My doctorate on brain functioning was completed in 1976 and I have been “keeping up” in general but wanted to do a deeper dive into the new science.)

Speaking as “Scott, the old Ph.D. behavioral neurophysiologist” and business consultant who now spends his time writing about leadership, team building, innovation, thinking and collaboration, I don’t throw that old localization map stuff away, but the newer research findings from 2025–2026 push us to a much richer view of how things really work: those local “centers” only make sense as visible peaks in a much more distributed, whole‑brain landscape. Consciousness, for example, is very widely distributed with its auditory, visual and kinesthetic components. And it is this distribution that gives us plasticity. 

From “where is it?” to “how does it work altogether?”

Classical localization studies—lesions, fMRI hotspots, TMS over single regions—have been invaluable in showing that some brain areas are specialists for certain kinds of information processing. That’s the backbone of a lot of what you read in the better neuro‑leadership blogs: prefrontal areas for planning and control, anterior cingulate for conflict monitoring, amygdala for threat and salience, and so on. Understandable.

Just for color! A Perplexity-generated image of a human brain.

The newer work doesn’t say that’s wrong; it says it’s incomplete in at least three ways:

• Functions are implemented by broad distributed networks, not solitary regions. Even when we see a strong “face area” or “control area,” it sits inside a larger, coordinated pattern of neural activity spanning many regions.

• That same region also participates in multiple networks depending on context. A patch that looks “visual” in one task may be part of a different coalition when you’re trying to think creatively, solve a problem, regulate emotion, or when one is learning from failure.

• Brains share a common scaffold, but the fine‑grained patterns and which regions team up with which, actually vary a good bit across people and change with development and experience. The brain IS plastic and it learns. And we are discovering more and more about this. Power is in the process.

So instead of asking, “Which bit of the brain does X?” we should be asking, “How does this person’s brain recruit and coordinate different regions when they’re doing X, today, and in this context?”

What “wide‑field imaging” is teaching us

One of the big drivers of this shift in understanding is the explosion of wide‑field imaging and large‑scale network analysis using AI and other computing tools. In animals, wide‑field calcium imaging lets us watch activity unfold across essentially the entire cortex in real time while a person is behaving. When you analyze those data with modern network tools, a very different picture emerges than the tidy little hotspot view.

What we see is:

• When even a “simple” task is performed, like making a choice, initiating a movement, or using a learned association—activity does not stay confined to a single patch of cortex. Waves and motifs of activation sweep across multiple regions in reproducible patterns.

• The same neurons and regions show different participation patterns depending on behavioral state – resting, planning, moving, learning – highlighting flexible, distributed neuronal recruitment rather than fixed localities.

• If you take those imaging movies and do proper network analysis, what we call a “function” is better described as a recurring pattern across many nodes, not one node’s job description. And patterns are all about learning and retention.

In consulting language, that’s like moving from a static org chart (“here are the boxes”) to a living dynamic map of project teams, alliances, and informal networks that form, dissolve, and re‑form around the work. It is in the flexibility that we gain the power of divergent thinking and problem solving.

Local peaks as the tips of distributed icebergs

There’s a nice bridge between the old and new views in recent modeling work on perception and category recognition. Papers looking at the ventral visual stream ask: if coding is really distributed, why do our scanners still see such apparently local selectivity (like face‑selective or word‑selective patches)?

The answer that’s emerging is:

• Information about categories is widely distributed across a network.

• Because of the way signals flow through that network, some locations show sharper tuning—these are the “hotspots” we pick up in imaging experiments.

• Those local peaks are the visible tips of an iceberg whose base is broad, distributed computation.

So when thoughtful practitioners talk about region X being “for” Y, they are usually pointing at one of these peaks. The science I track now suggests we treat those peaks not as isolated modules, but as signatures of deeper, distributed flows.

Whole‑brain organization and intelligence

On the human side, large‑scale imaging and network studies are converging on the idea that general intelligence and complex cognition are properties of how the whole network is organized, far more than they are properties of any single “intelligence center.”

Across big datasets, the strongest predictors of performance look to:

• How efficiently different networks integrate and share information.

• How flexibly networks can reconfigure when you switch tasks or demands.

• How well the system balances segregation (specialization) and integration (coordination).

That resonates strongly with what I see in organizations: success has less to do with one “genius” unit and more to do with how well the system can bring the right people, perspectives, and processes together at the right time.

Massive human brain maps: lots of parts, repeated patterns

In parallel, the new human brain atlases, that profile millions of individual neurons across the brain, are showing us that there are thousands of distinct cell types sprinkled across many regions. The patterns are nuanced and overlapping; there is not a neat one‑area‑one‑type organization.

Some of the same cellular “building blocks”—particular inhibitory neurons, projection patterns, neuromodulatory systems—show up in many places, recombined into different microcircuits. That makes localization look even less like “one box per function” and more like “many kitchens reusing the same ingredients to cook different recipes.”

What this means for teams, innovation, and collaboration

Here’s how I translate this more distributed brain science into practical insights around improving organizational performance:

• Performance is an emergent property of networks of interaction, not star performers or single teams in isolation. You get better outcomes by improving coordination and information flow than by obsessing over one unit.

All of the wagons can be aligned to a shared mission and vision and collaborative network.

• The same person (like the same brain region) is going to show up differently in different coalitions. Someone quiet in a status‑report meeting may be the key connector in a crisis or an innovation sprint.

• Development is less about “installing new modules” and more about rewiring patterns of coordination and collaboration for changing habits of thinking, communication, and cooperation so more of the system can contribute to a decision or solution.

So when you read a good, localized brain story from folks, my suggestion isn’t to discard it; it’s to add a second lens:

“Yes, that region and function matters. And what’s the network it works in, for this person, in this situation—and how can we build our organizations to mirror that kind of flexible, distributed intelligence?”

That’s the bridge I’m trying to build: from a brain model of tidy spots to a brain model of dynamic, distributed coalitions and then from that model to how we design teams, cultures, and leadership practices that actually perform.

This is also a pretty readable article about some of the ideas above.