Before I became a dog trainer, I was an automation engineer. I worked with complex control systems—essentially wiring up little artificial brains to run industrial processes. I didn’t always have diagrams, so I’d spend my days fault-finding: visualising how the system should work, tracing signals, watching outputs, and figuring out where it all went a bit “gremlin in the wires.”

Truth be told, I learned the most by making mistakes.

And not just oops mistakes. I mean glorious, confusing, pull-it-all-apart-and-start-again mistakes. The kind of mistakes that force you to think deeper, look closer, and ultimately rewire your own internal systems.

That hands-on, trial-and-error learning didn’t just teach me engineering—it trained my brain to think in processes. Now I find myself doing something eerily similar in dog training… only the systems are fuzzier, messier, and come with significantly more feelings (and fur).

💥 The Study That Sparked All This

A 2024 study from Ruhr University Bochum looked at how animals learn tasks—and it caught my attention like a loose wire catches a sleeve.

Researchers found that mice who made mistakes during learning actually activated more brain regions than mice who didn’t. Makes sense: if everything goes smoothly, the brain can coast. But errors? Errors make the brain work.

Here’s the kicker: The part of the brain that lit up the most wasn’t the “thinking” area. It was the sensory cortex—the regions that process vision, touch, and body awareness.

Translation?

The brain isn’t just thinking harder during mistakes—it’sfeelingharder.

It’s reprocessing input, adjusting internal models, and fine-tuning how it interprets the world—not through logic, but through sensation.

And suddenly, the way I see learning—human, dog, or otherwise—shifted entirely.

🧠 Not Just Thinking—Simulating

Temple Grandin writes in Visual Thinking that visual learners don’t always “think in pictures.” Some of us (hi!) think in processes—mental simulations that unfold like animated flowcharts. Not static images, but dynamic models. A kind of brain-based CAD software, but messier and with more coffee.

That was my experience as an engineer: I could “see” the system in motion, even if it was all happening inside my head.