Annotations (12)
“There's a beautiful idea from computer science and physics called simulated annealing. You have some problem to solve, some space of solutions. One thing you can do is make little changes to what you already know, a low-temperature search. The other kind is a high-temperature search: bounce around the space, try wild, crazy things, a random walk. The strategy in computer science is start with wild, crazy, out-of-the-box high-temperature search, then cool off and fill in the details.”— Alison Gopnik
Creativity & Innovation · Psychology & Behavior · Technology & Engineering
DUR_ENDURING
High temp search then cool: kids do this naturally
“Eric Turkheimer found that you had much more convergence in twin studies in upper-class contexts than in poorer families. The explanation: if you're in a poor family, small differences in your environment can make a big difference in how the rest of your life goes. And that's less likely to be true in a richer family. The effect of having a protective caregiver is that it allows more variability.”— Alison Gopnik
Psychology & Behavior · Economics & Markets · Biology, Ecology & Systems
DUR_ENDURING
Safe environments allow more variability, not less
“Going back to Plato and Aristotle, they talked about the problem of how we can know as much as we do about the world given such a small amount of data. Two solutions: one is that structure couldn't have been learned from the data, it must have been there innately (Plato's past world, Chomsky's evolution). The other solution: it looks like there's abstract structure, but really it's just statistical combinations of the data (like current deep learning AI).”— Alison Gopnik
Philosophy & Reasoning · Psychology & Behavior
DUR_ENDURING
Rational constructivism: neither innate nor purely statistical
“Kids are better Bayesians than scientists. If you have a very peaked prior, a lot of experience giving you reason to believe an assumption is right, then it's rational not to change it when you have just a little bit of evidence. Kids are better at solving problems with unusual outcomes than scientists are.”— Alison Gopnik
Psychology & Behavior · Philosophy & Reasoning · Strategy & Decision Making
DUR_ENDURING
Kids update faster: flatter priors enable learning
“When people say there isn't an effect of the family environment on development, what they mean is they don't see high correlations between kids in the same family and some measure of what an adult is like. Siblings, the non-shared environment, things that don't seem to be the same, play a larger role than the shared environment. If you have two kids with the same parents, you might think the kids are going to be more similar. But if caregiving allows variability, you might expect the opposite.”— Alison Gopnik
Psychology & Behavior
DUR_ENDURING
Good parenting increases divergence, not convergence
“Imagine if we tried to teach baseball the way we teach science. We would tell everybody about great baseball games when they were little. In high school they could throw the ball a lot to second base. In college they could reproduce great baseball plays. But they wouldn't actually get to play the game until they were in graduate school. If you taught baseball that way, you wouldn't think people would be as good at baseball. What kids end up being incredibly good at is going to school.”— Alison Gopnik
Leadership & Management · Creativity & Innovation
DUR_ENDURING
We teach about science, not how to do science
“Consciousness is very unlikely to be a single thing. It's like life: in the 19th century we thought life would turn out to be a simple thing, and there were research programs about what makes a living thing living. It turned out that was just the wrong question. There are many different kinds of processes involved in different things we think of as life. The same is likely true with consciousness.”— Alison Gopnik
Philosophy & Reasoning · Psychology & Behavior
DUR_ENDURING
Consciousness isn't one thing, like life isn't one thing
“My family: there were 6 of us in 11 years. My parents were graduate students. We had this combination of a great deal of warmth, a great deal of love, an enormous amount of stuff around us, books and ideas. We got taken to the Guggenheim when Adam was 3 and I was 4. We were also completely free. We were in regular public schools. We came home after school and did whatever we wanted. The kids were taking care of each other a lot of the time. The result is you get a lot of variation.”— Alison Gopnik
Psychology & Behavior
DUR_ENDURING
Nurture's effect: variance not mean
“My view about generative AI: our intuitive lay conception of how AI works is really misguided. We have this Gollum view: here's this non-living thing we've given a mind to, and that always works out badly. It's going to be superintelligence for good or ill. That's not the right narrative. The right narrative is to think of it as a cultural technology. It's a way of getting information from other people.”— Alison Gopnik
Technology & Engineering · Philosophy & Reasoning
DUR_CONTEXTUAL
AI as cultural tech, not intelligence
“Babies' brains are very plastic. They're surrounded by novelty. They're not just focusing on one thing at a time the way we do when we're grownups. If you think about contexts as grownups where we are in that state, like traveling to a new place or trying to do something new, it's not like we're unconscious. On the contrary, it feels like we're full of experience. We're vividly experiencing the world around us. That's what it's like with babies.”— Alison Gopnik
Psychology & Behavior · Philosophy & Reasoning
DUR_ENDURING
Less memory compression means more vivid present awareness
“Philosophers of science and computer scientists have found systematic ways to talk about how we get from data to theory. Scientists, mostly not consciously but as part of what they do, and little kids, are looking at data and systematically figuring out what kind of structure out there in the world could have caused this pattern of data.”— Alison Gopnik
Philosophy & Reasoning · Psychology & Behavior · Technology & Engineering
DUR_ENDURING
Both kids and scientists infer world structure from data
“People with aphantasia don't retain images very well, but they vividly experience the visual world. They just don't use that experience when trying to imagine something or generate an image. What aphantasia shows is that generating an image when you're solving a problem is sort of epiphenomenal. You're not solving the problem by looking in your head and seeing the picture. My friend Ed Catmull, co-founder of Pixar, has aphantasia. He's an animator who doesn't see pictures inside his head.”— Alison Gopnik
Psychology & Behavior · Creativity & Innovation
DUR_ENDURING
Mental imagery is epiphenomenal, not causal
Frameworks (2)
Simulated Annealing Search Strategy
High-Temperature Exploration Followed by Low-Temperature Refinement
A two-phase approach to problem-solving borrowed from physics and computer science: begin with wild, random, high-temperature exploration of the solution space to discover novel possibilities, then cool off and shift to focused, incremental, low-temperature refinement to optimize the discovered solution. Children naturally operate in high-temperature mode; experts must balance both phases.
Components
- High-Temperature Exploration Phase
- Low-Temperature Refinement Phase
Prerequisites
- Willingness to appear foolish during exploration
- Ability to recognize when to shift phases
Success Indicators
- Discovery of non-obvious solutions
- Breakthroughs that wouldn't emerge from incremental search
Failure Modes
- Getting stuck in exploration mode
- Skipping exploration and optimizing a suboptimal solution
Apprenticeship-Based Skill Development
Do the Thing, Get Feedback, Iterate
For school-aged children (7+), the optimal learning model is apprenticeship: students do the actual activity they're trying to learn, receive immediate feedback from a skilled teacher, and iterate. This contrasts with the common approach of teaching about a subject for years before allowing practice. Music and sports succeed because they follow this model; most academic subjects fail because they don't.
Components
- Do the Real Activity
- Get Immediate Feedback
- Iterate and Refine
Prerequisites
- Access to skilled practitioners who can give feedback
- Willingness to let beginners do real work
Success Indicators
- Students develop practical skill faster than in lecture-based approaches
- Students request more practice time
Failure Modes
- Teacher gives feedback but student doesn't get to iterate
- Activity is too simplified to be authentic
Mental Models (4)
Causal Inference from Data Patterns
Decision MakingThe cognitive process of observing patterns in data and systematically working backward to infer what causal structure in the world could have generated those patterns.
In Practice: Gopnik describes how children and scientists infer causal structure from data patterns
Demonstrated by Leg-ag-001
Bayesian Updating with Flat Priors
Probability & StatisticsA decision-making approach where you start with relatively weak prior beliefs and update rapidly in response to new evidence.
In Practice: Gopnik explains that kids are better Bayesians than scientists
Demonstrated by Leg-ag-001
Exploration vs. Exploitation Tradeoff
Decision MakingThe fundamental tension between exploring new options and exploiting known options.
In Practice: Gopnik describes simulated annealing as a balance between exploration and exploitation
Demonstrated by Leg-ag-001
Protected Environments Enable Phenotypic Variability
Biology & EvolutionIn biology and ecology, when an environment is safe and resource-rich (a protected garden rather than harsh wilderness), the organisms in that environment can express greater phenotypic variability. This applies to human development: children in safe, nurturing environments have more freedom to develop in diverse directions rather than being forced into narrow adaptive strategies by harsh conditions.
In Practice: Gopnik uses the garden metaphor: protected gardens have more variety than harsh environments
Demonstrated by Leg-ag-001
Connective Tissue (3)
Pleistocene brain structures enabling both prehistoric survival and modern scientific inference
The cognitive architecture that evolved in the Pleistocene epoch (2.6 million to 11,700 years ago) to help early humans survive by inferring causal structure from limited environmental data is the same architecture that enables modern scientists to infer theoretical structure from experimental data. The brain's capacity for world-modeling, hypothesis testing, and causal reasoning is not a recent invention but an ancient adaptation repurposed for modern science.
Gopnik argues that scientists use Pleistocene-era brain mechanisms to infer causal structure from data, the same mechanisms children use
Simulated annealing in physics and computer science
Simulated annealing is a technique from physics and computer science for solving optimization problems. It mimics the physical process of heating a material and then slowly cooling it to reduce defects and reach a low-energy state. In the algorithmic version, you start with high-temperature random exploration of the solution space, then gradually cool off and do focused refinement. This maps directly onto how children explore (high temperature) versus how experts refine (low temperature).
Gopnik uses simulated annealing as an analogy for how children explore solutions widely before adults refine them narrowly
The 19th century scientific quest to define 'life' as a single unitary phenomenon
In the 19th century, scientists believed they would discover a single principle or substance that made living things alive, something like a vital force. Research programs sought to identify what made a living thing living. It turned out this was the wrong question. Life is not a single thing but many different processes: metabolism, reproduction, homeostasis, growth, response to stimuli. The same mistake is being made with consciousness and intelligence: treating them as unitary phenomena when they are actually bundles of distinct processes.
Gopnik argues consciousness is like life: 19th century scientists thought life was one thing, but it turned out to be many processes
Key Figures (5)
Eric Turkheimer
2 mentionsBehavioral Genetics Researcher
Blake Gopnik
2 mentionsArt Critic and Biographer
Adam Gopnik
2 mentionsWriter for The New Yorker
Ed Catmull
1 mentionsCo-founder of Pixar
Co-founder of Pixar who has aphantasia.
- Ed Catmull has aphantasia and is an animator who doesn't see pictures inside his head
Henry Farrell
1 mentionsPolitical Scientist
Glossary (2)
simulated annealing
DOMAIN_JARGONOptimization technique mimicking metal cooling to find optimal solutions through random then focused search
“There's a beautiful idea from computer science and physics called simulated annealing.”
aphantasia
DOMAIN_JARGONInability to form mental images despite normal visual processing and cognition
“Aphantasia is a puzzle about why it is that we have the kind of imagery that we have.”
Key People (5)
Ed Catmull
(1945–)Co-founder of Pixar Animation Studios; has aphantasia
Plato
(-428–-348)Ancient Greek philosopher who argued innate knowledge
Aristotle
(-384–-322)Ancient Greek philosopher
Jean Piaget
(1896–1980)Swiss developmental psychologist
Eric Turkheimer
(1950–)Behavioral genetics researcher
Concepts (5)
causal structure inference
CL_PSYCHOLOGYThe cognitive process of determining cause-effect relationships by observing patterns in data
Bayesian prior
CL_STRATEGYInitial belief strength before observing new evidence; peaked priors resist updating
nativism
CL_PHILOSOPHYPhilosophical view that certain knowledge or cognitive structures are innate rather than learned
constructivism
CL_PSYCHOLOGYTheory that learners actively construct knowledge through experience
Goodhart Law
CL_ECONOMICSWhen a measure becomes a target, it ceases to be a good measure
Synthesis
Synthesis
Migrated from Scholia