You built the onboarding. You tested it yourself. It makes perfect sense.
Then real users arrive — and 60% drop off before step three.
You assume they're lazy, distracted, or not your target audience. But the research points somewhere else entirely: the problem is you.
The Curse You Can't Escape
In 1989, economists Colin Camerer, George Loewenstein, and Martin Weber coined the term "curse of knowledge." Their finding: once you know something, you literally cannot reconstruct what it was like not to know it.
This isn't a soft preference or a tendency you can override with effort. They tested financial incentives — paying people to ignore their private information. Didn't work. They tested market forces — aggregating many judgments. Reduced the bias by approximately 50%, but never eliminated it.
The curse of knowledge is structural. You can't opt out.
Hindsight Bias: The "Obviously" Problem
A related effect, hindsight bias — the "I knew it all along" feeling — has been quantified across two major meta-analyses:
Christensen-Szalanski & Willham (1991): 122 studies, overall effect r = .17 (small but consistent). Interestingly, more experience with a task reduces the bias. The opposite of what you'd expect.
Guilbault et al. (2004): 95 studies, 252 independent effect sizes, overall d = 0.39 (95% CI: 0.36–0.42). A medium-small effect, but one that operates every time you look at your own onboarding and think "this is clearly laid out."
The d = 0.39 means your assessment of clarity is systematically inflated. Not by a random amount — by a predictable, measurable amount.
The Illusion of Explanatory Depth
Yale researchers Matthew Fisher and Frank Keil published a fascinating finding in 2015: experts are most miscalibrated within their own area of specialty.
People accurately assess what they know in areas outside their expertise. But in their own domain? They think they can explain things they actually can't. When forced to write out a full explanation step by step, they're surprised by the gaps.
The mechanism: experts forget how much detailed information they've forgotten. You automated the knowledge. It became invisible. And invisible knowledge can't be taught.
This is the "Illusion of Explanatory Depth" — you think you understand how your product works until you try to write down every single micro-step a beginner would need.
"Go to Settings" is clear to you. But a beginner needs: "Click the gear icon in the top right corner of the screen. You'll see a dropdown menu. Select 'Settings' — it's the third option from the bottom."
The Einstellung Effect: Your Familiar Path Blocks Better Ones
Merim Bilalić and colleagues studied chess masters in 2008, investigating the Einstellung (set) effect. When a familiar solution existed, experts failed to find the optimal solution — even when the optimal one was objectively better.
The key finding: the familiar solution reduced experts' problem-solving ability to approximately 3 standard deviations below their actual skill level. Eye-tracking confirmed it — experts literally didn't look at the features associated with the better solution. Their eyes tracked to the familiar pattern.
Applied to onboarding: when you design the tutorial, you default to YOUR workflow. The way YOU use the product. The path YOU find natural. And your brain doesn't even consider that a beginner might approach it from a completely different direction. You don't see it because you're not looking.
The more expert nuance: extremely high expertise partially protects against this. But "partially" still leaves most of the bias intact.
Earned Dogmatism: The Social Permission to Ignore Feedback
Ottati and colleagues (2015) documented the "earned dogmatism" effect across six experiments: people who perceive themselves as experts adopt more closed-minded cognition. The social norm is clear — experts are "entitled" to be dogmatic (d = 0.45 for the norm itself).
A replication by Calin-Jageman (2018) found something interesting:
- People believe experts should be dogmatic: d = 0.45 (replicated)
- People predict they'd be closed-minded as experts: d = -0.54 (replicated)
- But actual behavioral closed-mindedness from expertise: d = 0.00 (not replicated)
The belief is there. The expectation is there. The actual behavioral compulsion isn't. Which means: you can override earned dogmatism if you recognize it's happening. The norm tells you your expertise entitles you to dismiss beginner feedback. But you don't have to listen to the norm.
What About Dunning-Kruger?
You might think the flip side applies — beginners don't know what they don't know. But the Dunning-Kruger effect is in serious trouble.
Gignac and Zajenkowski (2020) found that the relationship between self-assessed and actual ability is essentially linear — no special "bump" for low performers. Blair Fix (2022) demonstrated that the Dunning-Kruger effect emerges even from random data, suggesting it's largely a statistical artifact of autocorrelation and regression to the mean.
Dunkel et al. (2023) replicated and found a statistically significant but "minimal" effect, questioning its practical meaning.
The bottom line: the bigger miscalibration is on the expert side (Fisher & Keil 2015), not the beginner side. Don't design onboarding assuming users are incompetent — design it assuming YOUR understanding of their experience is flawed.
Shoshin: The Structural Practice of Beginner's Mind
The Japanese Zen concept of Shoshin (beginner's mind) addresses this directly. Shunryū Suzuki wrote in 1970: "In the beginner's mind there are many possibilities, in the expert's mind there are few."
Research supports the core claim:
- Cognitive flexibility decreases as expertise solidifies
- Experts generate fewer hypotheses when problem-solving than novices
- The Einstellung effect confirms: familiar patterns suppress alternatives
- Awe-inducing experiences increase intellectual humility and gap-awareness
But Shoshin isn't a switch you flip. You can't decide to "think like a beginner." The curse of knowledge prevents exactly that. Instead, it requires structural practices:
- Watch beginners use your product — not team members pretending to be beginners
- Pre-commit to metrics — "80% complete step 3 within 2 minutes" before you launch
- Write every micro-step — then add the ones you accidentally skipped
- Cross-train your team — have marketing try onboarding, not just the developer who built it
- Quarterly "new user" sessions — delete your test account and start from zero
What This Means for Your Nervous System
Here's the recovery angle: if you're building a product while recovering from burnout, your cognitive resources are already limited. The curse of knowledge operates automatically — it doesn't cost extra energy. But correcting for it does.
This means your onboarding might be MORE affected by the curse during recovery periods. Your working memory is constrained, making it harder to model someone else's perspective (which requires holding two mental models simultaneously).
The structural approach — user testing, metrics, micro-steps — is also the recovery-friendly approach. It externalizes the correction rather than relying on cognitive effort you may not have available.
The Practical Takeaway
Every expert-designed onboarding is systematically miscalibrated. This isn't an insult — it's a measurement. Hindsight bias (d = 0.39) inflates your sense of clarity. The Einstellung effect (3 SD reduction) locks you into your own workflow. The Illusion of Explanatory Depth makes you think you've explained things you've actually skipped.
You can't think your way out of these biases. You need structures that reveal them: beginner feedback, pre-committed metrics, explicit micro-steps, and the humility to know that your expertise is simultaneously your greatest asset and your biggest blind spot.
The curse of knowledge is real, robust, and not reducible by good intentions. Design accordingly.
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Sources:
- Camerer, C., Loewenstein, G., & Weber, M. (1989). The Curse of Knowledge in Economic Settings. Journal of Political Economy, 97(5), 1232–1254.
- Guilbault, R. L., et al. (2004). A Meta-Analysis of Research on Hindsight Bias. Basic and Applied Social Psychology, 26(2–3), 103–117.
- Christensen-Szalanski, J. J., & Willham, C. F. (1991). The hindsight bias: A meta-analysis. Organizational Behavior and Human Decision Processes, 48(1), 147–168.
- Fisher, M., & Keil, F. C. (2015). The Curse of Expertise. Cognitive Science, 40(5), 1251–1269.
- Bilalić, M., McLeod, P., & Gobet, F. (2008). Inflexibility of experts. Cognitive Psychology, 56(2), 73–102.
- Ottati, V., et al. (2015). When Self-Perceptions of Expertise Increase Closed-Minded Cognition. Journal of Experimental Social Psychology, 61, 131–138.
- Calin-Jageman, R. J. (2018). Direct replications of Ottati et al. (2015). Journal of Experimental Social Psychology, 75, 105–112.