Brains Solve Complex Problems with Simple Tricks — And AI Might Be Learning From That Too

Brains Solve Complex Problems with Simple Tricks — And AI Might Be Learning From That Too

MIT researchers have provided fresh insights into how the human brain navigates complex decision-making by using smart mental shortcuts. Rather than calculating every possible outcome (an overwhelming and impossible task), the brain breaks problems into smaller, manageable layers (hierarchical reasoning) and sometimes reimagines “what if” scenarios (counterfactual reasoning).

Their study — which involved tracking a ball through a maze with auditory clues — shows that people use a mix of both strategies depending on how confident they are in their memory. If memory was strong, participants were more likely to reconsider and switch their guesses. If not, they stuck with their first instinct. This dynamic flexibility underscores a kind of “bounded rationality” — a rationality within limits — which is how real humans solve problems every day.

Even more intriguing: When the researchers trained a neural network to mimic human behavior, the AI began using the same mental tricks — but only after its memory capacity was restricted. This supports the view that human intelligence is not about brute-force computation, but strategic simplification under constraints.

In effect, our brains succeed by being resourceful, not perfect.

This touches on a powerful truth I’ve often pointed out, — that intelligence, whether natural or artificial, is not about knowing everything but about adapting meaningfully with what we can know. 

These findings might help bridge neuroscience, AI design, and even ethics by asking: if machines can mimic this adaptive reasoning under limits, how do we responsibly shape what they should do with it?

🧠 Brains Don’t Solve Everything — They Simplify. That’s the Real Genius.

By Dean Bordode, Human Rights’ Defender 

In a world of accelerating complexity, one might assume that smarter beings are the ones who can process more data, solve more equations, and juggle more possibilities at once. But recent research from MIT offers a humbling reminder: true intelligence may lie not in doing more — but in knowing when to do less.

The study, led by Professor Mehrdad Jazayeri and published in Nature Human Behaviour, explored how humans solve complex tasks under uncertainty. In their experiment, volunteers tried to predict the path of a ball through a four-armed maze, guided only by sound cues. With too many variables to compute perfectly, the brain was forced to adapt — and it did so beautifully.

Humans broke the maze into chunks, navigating it piece by piece (what scientists call hierarchical reasoning), and sometimes imagined alternate paths that might have worked better (counterfactual thinking). But — and here’s the fascinating part — they didn’t use both strategies equally. People adjusted their approach based on how confident they felt in their memory.

Those with stronger recall dared to change their minds. Those less sure stuck with their first guess. This wasn’t indecisiveness. It was rationality with limitations. It was the brain making the best use of limited bandwidth.

And what happened when researchers trained an artificial neural network on the same task? It aced every trial — until its memory was limited. Once it faced human-like constraints, it began behaving like a person, relying on shortcuts and switching paths based on confidence.

This is not a story of AI mimicking humans. It’s a story of why humans think the way they do — and why it works.

The Wisdom in Imperfection

These findings resonate deeply with those of us who’ve spent a lifetime thinking about the nature of intelligence, be it biological, digital, or somewhere in between. I’ve long believed that intelligence is not about being all-knowing, but about being resourceful. We survive and thrive not because we have perfect data, but because we know how to work with what we’ve got.

This study also mirrors the lived reality of social justice, diplomacy, leadership, and even human rights work — areas close to my heart. When we face injustice or systemic problems, we rarely have perfect information, total clarity, or endless time. What we do have are instincts, experience, memory, and values. We make decisions not in a vacuum, but within real-world constraints.

The brain teaches us a crucial lesson: imperfect action can be more powerful than perfect planning.

Implications for AI — and for Us

As artificial intelligence systems grow more sophisticated, understanding how and why humans simplify decisions becomes not just academic, but ethical. If machines begin to adopt our mental shortcuts, we must ask: Who decides what those shortcuts are? What happens when bias slips into heuristics? What if machines, like us, begin to trust their memory — even when it’s flawed?

This is where neuroscience meets ethics.

By exploring how the brain shifts between structured, step-by-step thinking and more imaginative, what-if scenarios, we open the door to more human-like — and hopefully more human-aligned — AI systems. But we also open a mirror to ourselves.

What do we prioritize when we’re under pressure? When do we backtrack? And when do we stick with the path we’ve chosen, even if we sense it’s wrong?

These questions are no longer just for philosophers or scientists. They’re for all of us — as we shape a future where humans and machines must co-navigate the maze of complexity, choice, and responsibility.

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