Learn it → Apply it → Save as Skill
Once you know something well, it is almost impossible to remember what it felt like not to know it. That gap quietly wrecks explanations, onboarding docs, product copy and meetings, because the expert keeps skipping the steps that feel too obvious to say out loud.
The Dr. Wallace’s Favorite Mental Models is designed to turn useful ways of thinking into reusable tools. Each model gives you a fast visual overview, deeper interactive learning, practical application exercises and an AI-ready Markdown skill you can save for future use.
Instead of merely reading about mental models, you can gradually build your own AI skills library: a collection of portable reasoning skills that can help ChatGPT, Claude, Grok and other AI assistants apply the mental models you value.
Experts stop noticing which steps they skip, which words are jargon, and which context they are silently supplying from memory. The listener has none of that background, so an explanation that feels complete to the expert can feel like a wall to everyone else.
Important: the tapper could hear the tune perfectly in their own head. The listener heard only disconnected taps.
The model matters most for anyone writing, teaching, briefing or designing for people who do not share their background: managers, teachers, engineers, doctors, product teams and support staff.
The cycle is easy to skip because the first draft always feels clear to the person who already understands it.
Flag the terms, steps and background facts you are using without defining, especially ones you learned so long ago they feel like common sense.
Picture a specific real person who lacks your background. Read your explanation as if you were them, not as the author.
Hand the explanation to an actual outsider before you ship it, and watch where they hesitate, re-read or ask "wait, what?"
Replace jargon with concrete language, add the missing step, or add one worked example instead of another abstract sentence.
Open a use case to see what the model changes.
If several new hires stumble on the same step, the documentation is not lazy, it is written by someone too close to the process. Rewrite the confusing step with a true beginner reading over your shoulder.
A label, icon or flow that reads as self-explanatory to the team that built it may carry none of that meaning for a first-time user. Repeated tickets on the same screen are a signal, not a coincidence.
Nodding signals politeness, not comprehension. Ask a student to explain the idea back in their own words before assuming an explanation landed.
Internal shorthand, project code names and metrics that are obvious to the team can be opaque to an executive audience seeing the project for the first time. Translate the acronym before the ask.
The step that feels too obvious to mention is frequently the exact step a reader is stuck on. Walk the instructions on a machine or account that has none of your defaults already set up.
What looks like resistance is sometimes confusion: the client is missing the background context that made the recommendation feel obvious to you. Rebuild the case from what they already know.
This is the well-known 1990 Stanford tapping study, run by Elizabeth Newton as part of her doctoral research and later popularized by Chip Heath and Dan Heath in "Made to Stick."
Expand for where the name and the evidence actually come from.
The phrase was coined by economists Colin Camerer, George Loewenstein and Martin Weber in their 1989 paper "The Curse of Knowledge in Economic Settings: An Experimental Analysis," published in the Journal of Political Economy. They used it to describe better-informed parties in a market who could not fully ignore what they knew, even when it would have been to their advantage to reason as if they didn't.
For her 1990 doctoral dissertation at Stanford, Elizabeth Newton had one group of participants ("tappers") tap out the rhythm of well-known songs, such as "Happy Birthday," while a second group ("listeners") tried to name the song from the taps alone. Tappers predicted listeners would succeed about half the time. Listeners actually identified only a small fraction of the songs, commonly reported as roughly 2.5 percent.
Chip Heath and Dan Heath featured Newton’s tapping study prominently in their 2007 book "Made to Stick: Why Some Ideas Survive and Others Die," using it to explain why clear communicators deliberately work to un-know what they know before they explain it.
Yes. Cognitive scientist Steven Pinker discusses the curse of knowledge at length in his 2014 book "The Sense of Style," calling it "the single best explanation" he knows of why good writers so often produce bad, jargon-heavy prose.
A term that is standard inside your team or field may be unfamiliar or mean something different to everyone else. Define it once, plainly, the first time it appears.
Experts often compress reasoning into a conclusion. A newcomer needs the logic that got you there, not just the destination.
Confused people frequently stay quiet to avoid looking slow. Ask them to restate the idea back to you instead of asking "does that make sense?"
Having a colleague with the same background review your explanation will not catch the gaps a true outsider would find.
Fixing this bias means adding missing context, not stripping out all nuance or talking down to a capable audience.
The step an expert omits because "everyone knows that" is disproportionately likely to be exactly where a newcomer gets stuck.
you are writing for expert peers who need exact technical language, not a simplified version that could blur a real distinction.
legal, medical, financial or safety content must state a specific term, warning or figure precisely, even if it reads as dense to a lay reader.
a mixed audience needs both: a plain-language summary up top and the exact technical version available underneath for the readers who need it.
This browser tool structures your thinking. It does not send your information anywhere or perform AI analysis.
Save as Skill converts the model into portable Markdown instructions that teach an AI when and how to apply the Curse of Knowledge, what questions to ask, what evidence to seek, what mistakes to avoid and how to structure its analysis.
Save the skills you find useful and organize them into your own reusable library. The Markdown is intentionally model-neutral, so it can be used wherever an AI system accepts uploaded files, project knowledge, custom instructions or reusable text context. Platform capabilities differ, so this is a portable skill file rather than a claim of one-click installation.
No. Fixing the curse of knowledge means adding the missing context, definitions and reasoning steps a newcomer needs, not removing accuracy or nuance. Precise language paired with a plain explanation beats either one alone.
They point the same direction but aren't identical. The curse of knowledge names the bias: experts forget what not-knowing feels like. Explaining a topic in simple terms as a self-check, an approach popularly associated with physicist Richard Feynman, is one practical technique for catching that bias in your own understanding before you try to teach it to someone else.
No. It affects anyone with more context than their audience, including a manager briefing a new hire, a parent explaining a rule to a child, or a long-time employee describing "how things work here" to someone on their first day.