Camilla Pang is, in a word, arresting. She peers at me through her computer screen with an affable mixture of curiosity and—what I hope is the result of a wobbly internet connection—confusion. The exchange is sometimes intimidating, but also fascinating. Pang is neurodivergent; she takes my questions literally and responds earnestly, reminding me of the 2020 speech that preceded her receipt of the Royal Society science book prize. “I find humans very confusing to the point where I don’t understand the social nuances that everything is built upon,” she says.
Pang is an outlier not only in terms of brain function, but also success: She is the youngest and the first writer of color to win the prestigious Royal Society prize (also awarded to Pang’s inspiration, Stephen Hawking) and has become a powerful voice for those who experience the world a bit differently. “We all have our own realities,” she tells me, “and they deserve to be heard and shared.”
At age 8, Pang was diagnosed with what she describes as a “turbulent cocktail” of Autism, ADHD, and generalized anxiety disorder. To manage, she buried herself in her uncle’s science textbooks, diving into a discipline that helped her make sense of human behavior. As she studied and documented her responses to everyday challenges, she became her own living science experiment and later transformed years of musings scribbled on Post-it Notes, book margins, and in over 60 notebooks into a guide on understanding human behavior. Her book was released in the United States under the title, An Outsider’s Guide to Humans: What Science Taught Me About What We Do And Who We Are, and it uses science as a lens through which to understand relationships, find purpose, embrace fear, and cultivate empathy. It is best described as a manual created out of necessity, Pang, now 29, explains. “I wrote this to survive.”
WIRED: How did science become what you describe as your “armor”?
Pang: I couldn’t relate to people. The only signals that I picked up on were what impacted me, the ways I was affected by things I saw and related to. For example, with dust particles in my bedroom …
In your book, you use dust particles as an example of conformity and individuality, writing: “It’s no more or less normal for a particle to be an outlier than part of the main grouping over the lifetime of a system … In the same way, every person who has ever been treated as an outsider has in some ways been typical …”
I felt like [the dust particles] were clearly a lot more like me than people at school. They got me.
When did you realize the scientific insights you had amassed could help other people?
I had actually wanted my PhD thesis [in bioinformatics] to be the manual, but obviously it wasn’t academic writing. I had to cut it out, but I didn't want to chuck it in the bin because it was the bit that I actually wanted to show people. The thing is, when you’re neurodivergent, you always feel behind; you feel like everyone knows something that you don't.
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But that changed when you were helping a friend work through a challenge …
I was like, “Just think of graph theory.” And she was like, “What do you mean graph theory?” Like, really confused. Well, graph theory, obviously! I presumed she knew what I meant. From that point, I realized not everyone knows everything that I know, and that maybe I had an edge and could actually help people. I was looking for a book that needed to be written, and now I've written it, which is great.
You write, “Where humans are ambiguous, often contradictory and hard to understand, science is trustworthy and clear. It doesn’t lie to you …” But science is a human endeavor. Doesn’t that make it as fallible as the people who have created it?
Yes, that is actually the case! At first, I found it quite worrying, like, “Oh, my God, the infrastructure that was so consistent [to me] before is now actually as confused as the rest of us.” But the whole point of the scientific process isn't just to be storage machines; it is the ability to use our instincts to know when to take the leap and question, and to also have the patience to troubleshoot. Through working at it, questioning it, and realizing an inconsistency to the process, I realized [science] is beautiful in its uncertainty. It's not just logic, it's also a very instinctive endeavor.
To the point of uncertainty, you explain that we can’t optimize our lives unless we “study and understand the noise, errors, and deviations from the mean.” Are you saying to err is scientific?
Completely. A lot of people think to be the perfect scientist, you need to not get things wrong, but “wrong” is a bit of an elusive concept. Wrong in one context is actually right in another. Like, when it comes to evolution, what is the right way to fertilize an egg to develop into an embryo? Evolution isn't perfection. It's an adaptive process where many different ways of doing things can evolve. Science isn't perfectionist.
So let’s take a specific example of how science has informed and shifted your behavior. You argue machine learning can help us parse information and make better decisions. How so?
Growing up neurodiverse, you hold on to fixed categories come hell or high water because you need that sense of security. But I realized that this was a very unsustainable and inflexible way of working—to be what I call “box-based.”
What in the machine learning context would be known as supervised learning where, you explain, “you have a specific outcome in mind and you program the algorithm to achieve it.”
Yes. Over time, I learned I needed to be more flexible so I could enjoy my life. For that, machine learning [offers] another discrete process called unsupervised learning, which is specifically looking at the data around you and, from that, clustering accordingly. You don't try and cluster the data to your preset conditions. You look around, sort out what you have, and, from that, ask “what are the best options?”
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You describe unsupervised learning as looking for patterns without knowing what or where they are. “If we want to be more scientific about how we make decisions,” you write, “we need to embrace disorder.” We need to “be more like trees because life isn’t linear but branched.”
I can be quite categorical at times, and that’s great because it helps me make a decision. But most of the time, I try thinking in trees because, for me, I need that sense of contingency in order to be able to navigate my day. To be flexible enough to navigate but decisive enough to know what you want—these two data structures [supervised and unsupervised machine learning] bridge that gap.
In your chapter on learning from your mistakes, you explore neural networks that underpin a lot of artificial intelligence. The networks can be described as simple processing elements that are loosely modeled on neurons with weighted feedback loops that can be changed through learning.
By becoming more aware of our internal feedback loops and the weight we give certain memories, you say, we can become better at learning from our mistakes. But as someone who is neurodivergent, do you have a hard time assessing, or trusting, your judgment?
That's an interesting question. If you're autistic and you've got a no filter and a hypersensitivity tendency—a sensory processing disorder—you can [weigh] signals that aren't actually dangerous because they trigger you. And because you're triggered constantly, you can take things that shouldn't be taken personally, personally. Imagine trying to decipher all that in conjunction with, you know, normal decisions. It's really exhausting. And it isn’t because we're picky; it is because we are affected differently to most people.
What I am saying is, you have to choose your battles on the things that you remember because you can easily remember all the negatives and not want to get up in the morning. You have a choice to be aware of the feedback and how you feed it back to yourself.
People in the public eye are often told “all feedback is good feedback,” and when we run it through that scientific lens, it becomes true, right? Because feedback improves the system?
It can, but we do need to also critique the nature of feedback. If I took every negative comment that is directed at my person, as opposed to my book, as feedback, that would be quite inaccurate, not to mention depressing. You need to, I wouldn't say grow a thicker skin, but know the difference between the two—and process them accordingly.
You retest assertions about your own life and say it's important to reevaluate the ideas we hold—practices that are very much in line with the scientific method.
A lot of people are scared to reevaluate because they feel like they're going back on themselves, but actually I want to look [at my life and] say, “Hang on a minute, this isn't refined—or this is refined to a certain point.” To innovate, you need to be able to feel like you can experiment. And it's OK if someone disagrees with it. You carry on anyway because you love it.
I know I run the risk of being reductive, but what’s the single greatest thing science has taught you about being human?
With all its security and facts and stability, that it isn't a solution, it's a process. It's about giving things a go. And that everyone's just trying to get through and seek the truth of what is going on. I think that's wicked.
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