Tag Archives: Cognitive Science

Think First, AI Second

Here’s a cool new study on learning using AI:

Think First, ChatGPT Later: Guiding Human–AI Collaboration for Learning Gains in Independent Human Creativity (April 2026)

The authors, Sarah Shi Hui Wong and Sophia Xuefei Qiu, ran a simple experiment with 196 university students and published the paper earlier this year in Educational Psychology Review. What struck me about the study was that the results seemed so obvious. I must be missing something. But maybe I came to that same conclusion after using various AI tools and just figured out on my own what they do best and what they don’t do very well at all. For me, I found that if I engaged the AI with something already in mind I was far more productive and the experience was more satisfying, whereas if I just randomly poked around with the AI with an empty mind then we both just ended up running around in circles and I learned nothing.

From the abstract:

“Thinking of one’s own ideas first, then collaborating with ChatGPT to improve them, promotes learning gains in independent human creativity.”

And with that, I knew I was on the right track and read the rest of the paper.

Wong and Qiu are researchers working in cognitive and educational psychology, and their study draws heavily on the memory and learning research that those fields have built up over the past several decades. The tradition also traces back to people like Robert Bjork, whose PhD is in mathematical psychology, and other researchers studying how the human brain encodes and retrieves information. These questions predate generative AI, but now they can be studied in the context of AI to explore new possibilities to use the technology. The findings of the ChatGPT study support my own experience since I’ve been using AI specifically for learning from the beginning. It’s the best teaching tutor I’ve ever had by far. The possibilities for education are endless. I wish I had these tools when I was a kid in school.

Here’s what they did with ChatGPT, but it works with any chat-based AI tool. They split students into three groups. The first group solved a creative task alone. The second group used ChatGPT however they liked. The third group followed what the authors called a “think first, ChatGPT later” method. So that third group of students had to think of their own ideas first and then iterate with the AI to improve those ideas. And then they’d submit a final answer on their own like the other groups.

On the first task, the group that used ChatGPT freely did the best. And if you’ve used AI at all, it should be obvious that the first group came out ahead initially. They seemed more productive. But they outsourced their own thinking entirely. Why does that matter? Well, from a longer term learning perspective, this is where it gets most interesting when you look more carefully.

Next, all three groups did a second and harder creative task with no AI access at all. At that point, the free-use group’s advantage vanished. Their scores dropped back to the level of students who never touched AI. But the guided group using AI was different. They hadn’t looked especially strong on the first task, but on the second task working alone they ended up beating the other groups. The researchers then read the chat transcripts and found a possible reason. Students using ChatGPT freely mostly just asked it for external ideas outright, just like I did when I was aimlessly messing around with AI. The guided group, however, mostly brought their own ideas to the chat conversation and asked ChatGPT to push on them. In other words, they iterated. That difference in human behavior, not the AI itself, predicted who actually learned something that resonated over time.

So why is this important? Use AI to help you learn! Simple. And we now have some data in a controlled study to substantiate that AI is a useful tool for learning beyond just anecdotal experience. Sure, AI can make you more productive really quickly, and I use it for that purpose all the time. But to actually leverage the tool it makes more sense to go in with a plan in mind so you can be both more productive and learn something valuable in the process. Then you can take what you’ve learned to focus your prompts to the AI so that you can move even more deeply into the subject. Again, this process is obvious if you’ve spent any time poking around with AI.

Others have also discovered this phenomenon as well. Tamara Tate published a piece called Think First: Why the First Idea Shouldn’t Come from AI in March 2026 before the ChatGPT study, and she made a similar argument about generative AI and writing.

As I read both pieces I found the concepts familiar because they reminded me of a book on learning I read years ago: Make It Stick: The Science of Successful Learning. Make it Stick is probably the best book on leaning I’ve ever read. Every page is pure gold for people who are motivated to learn on their own. The book is based on the same body of cognitive psychology research as the ChatGPT study, particularly the work of Robert and Elizabeth Bjork on what they called “desirable difficulties.” Wong and Qiu cite that same research to help explain why the AI guided group may have performed weaker initially but excelled later. The key is that the students were doing more work up front. Attempting retrieval on your own (thinking first) before instruction beats instruction alone followed by retrieval (testing) afterwards. That’s not a new idea. It’s just being tested on a chatbot now. We now have a new tool at our fingertips to implement an old principle.

Aside from going into an AI conversation with at least a rough plan up front, it also makes sense to spend some significant time iterating with the AI at length. The ChatGPT study only covered a 12 minute timeframe before answers were submitted. But try going longer. I can talk to the damn thing for hours just going back and forth asking and answering questions, brainstorming ideas, taking quizzes, working thorough tutorials, and anything else I can think of. I tend to have hand-written notes to the side, though, to track things that are important and need more probing. I learned this from an engineering manager I worked for at Sun in Solaris engineering where I observed her managing meetings of engineers. She asked one question after another until they solved the problem. I was surprised how deep she could go to get the engineers to think differently about the issue. She always had another question to ask, which led to additional questions the engineers would ask each each other as well. Those meeting sessions would also often lead to further conversations on the team’s mailing list. It works. And AI has infinite patience for this iteration. I guess Socrates was right.

Here’s the final sentence in the ChatGPT study:

“Generative AI is not to be a substitute for human creativity but, when strategically harnessed, a powerful tool for enhancing it.”

Makes sense to me. Working it every day.

Redefining Aging: Wisdom Wins

The Myth of Cognitive Decline: Non-Linear Dynamics of Lifelong Learning
Michael Ramscar, Peter Hendrix, Cyrus Shaoul, Petar Milin, Harald Baayen

I’ve always had a hunch about this. Back in my 20s, I remember being quick. Conversations, thinking, decisions, reactions, movements. All fast. Now, though, things are obviously much slower. It’s also, obvious that my brain is juggling a ton more info. Massively more! I never had that feeling when I was a kid. And my memory is so much more comprehensive now, whereas when I was young I never even thought about memory. I also notice I’m not just reacting these days. Instead, I’m actively sifting through years of experience, global connections, interesting new ideas, and some really painful lessons learned since birth. And, apparently, all that processing that takes some time.

I see this phenomenon everywhere now. Kids fly through problems with fresh, uncluttered minds. Then young adults start layering on some complexity and slow down just a bit. Older people, though, like my parents or my older mentors, pause much longer — but they then drop insights that blow me away with their depth. How do they know that? Where did that come from? These patterns are obvious when you watch people wrestle with intellectual challenges, whether coding a complex algorithm, debating politics, arguing over sports teams, or solving intractable real world geopolitical problems. It’s not a hard and fast rule, bit the pattern is observable is you pay attention long enough.

I stumbled on the article above while digging into learning techniques for some projects on training, networking, and memory. It confirms what I’ve felt for years but I rarely hear people talking about it in casual conversation. Even when I bring it up, most people just dismiss the issue.

A New Spin on Aging

The article flips the script on how we think about our aging brains. Everyone seems to assume we just fall apart mentally as we get older, right? Wrong. The authors tackle the “aging as decline” myth head on, pulling from the Greek story of Tithonus, who was immortal but stuck without youth, to slam the idea that growing older means mentally breaking down. And they’re not messing around:

“Many of the assumptions scientists currently make about ‘cognitive decline’ are seriously flawed and, for the most part, formally invalid.”

They say slower reaction times or shaky test scores don’t necessarily mean your brain’s failing, and it’s just natural decline due to aging. Instead, it’s the natural result of continually laying knowledge over a lifetime. It’s just your brain working overtime parsing all that wisdom.

With 72 million Americans hitting 65 by 2030, we need to get this right. The authors say buying into the decline myth wastes human potential:

“The myth of cognitive decline is leading to an absurd waste of human potential and human capital.”

I love that assertion in the face of never-ending media propaganda telling us that our brains are doomed. The authors push back hard and say we need to rethink how we support older people. Aging isn’t the problem. Our misconceptions are.

Breaking the Decline Myth

The core idea here is that your brain doesn’t just stay the same forever. Psychometric tests, those standard measures of cognitive ability, miss the mark because they ignore how experience shapes us over time. Learning tunes out useless noise and crams your head with more info, which naturally takes longer to parse. Language is a big deal here as well. Words follow a long tail pattern, where common ones rule in the present moment but rare ones pile up over time. Traditional tests miss this growth, so older people seem slower. Of course the are. They’re processing more!

Could it be that simple? The authors point out that current tests miss how our knowledge grows over time, and that mainstream aging researchers haven’t controlled for the sheer amount of information an older brain has to search through. That gap is what’s kept the science off for so long.

And the authors clearly nail the paradox of this experience:

“Learning is a discriminative process that serves to locally reduce the information processing demands associated with specific forms of knowledge and skill… [but] age and experience will inevitably increase the overall range of knowledge and skills any individual possesses, increasing the amount information in (and complexity of) his or her cognitive systems.”

Translation? Your brain’s sharper as a result of education but much busier. They sum it up late in the paper:

“The results reported here indicate that older and younger adults’ performance in psychometric testing are the product of the same cognitive mechanisms processing different quantities of information: Older adults’ performance reflects increased knowledge, not cognitive decline.”

To me that’s a nice big fat middle finger to decades of so-called settled science. No. The science is never settled. You gotta question it, like these authors do with some serious attitude.

Simulations and Real Life

The authors use an interesting data collection tool, the Naive Discriminative Reader, to simulate how brains handle tasks like picking out words. Models with more data, mimicking a lifetime of experience, get pickier with rare words and vary more, just like older people in studies. Same deal with non lexical tasks like letter classification. Slower times come from bigger mental databases, not a broken brain. Paired associate learning, where you link words like baby and cries, shows older adults deeper knowledge makes hard pairs trickier, not memory loss.

Name recall? That’s a mess because names got crazier since the 1880s. American first name complexity jumped from 100 to over 2,000 options. Simulations say it takes half a second longer to recognize names over a lifetime. The authors throw a curveball:

“Confounding name recall problems with cognitive decline is akin to asking older adults to accept personal responsibility for a social problem.”

Ouch! Even tasks like the COWAT FAS test, where older people often shine, support the point. Experience helps you nail word retrieval despite proper noun chaos.

On brain science, the authors are bold:

“Except in the case of neurological diseases where there is evidence of pathology, there is no neurobiological evidence for any declines in the processing capacities of healthy older adults.”

So, without models tying brain changes to behavior, those decline claims seem shaky.

Why This Matters

There’s one more bit worth knowing about because it answers the obvious skeptical question. If cognitive decline isn’t real, why does it show up in study after study? The authors dug into a meta-analysis of 134 studies using the FAS fluency test and found something they hadn’t expected. The size of the study mattered more than anyone realized. In small samples, the kind they call “artisanal,” performance barely changed with age at all. Thirty-five-year-olds and sixty-five-year-olds scored about the same. It was only in the larger studies that the familiar decline showed up, and it grew steadily as the samples got bigger. That’s a strange thing for a real biological decline to do. It suggests some of what we call cognitive decline might be an artifact of how psychology studies are run, not a fact about how brains age normally.

Same deal with retirement: less variety in daily life can jumble memory, not because your brain’s failing, but because learning and memory thrive on context. The authors want us to dump the myths that waste talent and build models to tap lifelong intelligence. Remember this blunt statement from earlier in the article:

“The myth of cognitive decline is leading to an absurd waste of human potential and human capital.”

That’s a paradigm shift, not just for better science, but for creating spaces where wisdom shines. Understanding learning’s ups and downs can help people manage memories better. So, next time someone says aging means decline, tell ’em to read this research from the authors above. Knowledge acquired from years of experience isn’t the problem. It’s the superpower.

And always question science. Question everything. Even this research that questions the previous research should be questioned. The science is never settled.

Jim Grisanzio at Jfokus in Stockholm in April 2022.
Jim Grisanzio at Jfokus in Stockholm in April 2022.