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Alison Gopnik is both a psychologist and philosopher at Berkeley, studying how children construct theories of the world from limited data. Her central insight is that babies learn like scientists, running experiments and updating beliefs based on evidence. But Tyler wonders: are scientists actually good learners? It's a question that leads them into a wide-ranging conversation about what we've been systematically underestimating in young minds, what's wrong with simple nature-versus-nurture frameworks, and whether AI represents genuine intelligence or just a very sophisticated library.

Tyler and Alison cover how children systematically experiment on the world and what study she'd run with $100 million, why babies are more conscious than adults and what consciousness even means, episodic memory and aphantasia, whether Freud got anything right about childhood and what's held up best from Piaget, how we should teach young children versus school-age kids, how AI should change K-12 education and Gopnik's case that it's a cultural technology rather than intelligence, whether the enterprise of twin studies makes sense and why she sees nature versus nurture as the wrong framework entirely, autism and ADHD as diagnostic categories, whether the success of her siblings belies her skepticism about genetic inheritance, her new project on the economics and philosophy of caregiving, and more.

Read a full transcript enhanced with helpful links, or watch the full video on the new dedicated Conversations with Tyler channel.

Recorded October 30th, 2025.

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Timestamps

00:00:00 - How children—and scientists—learn

00:14:35 - Consciousness, episodic memories, and aphantasia

00:23:06 - Freud's and Piaget's theories about childhood

00:27:49 - Twin studies and nature vs. nurture

00:39:33 - Teaching strategies for younger vs. older children

00:44:07 - AI's ability to generate novel insights

00:53:57 - What Autism and ADHD diagnoses do and don't reveal

00:58:02 - The success of the Gopnik siblings

Photo Credit: Rod Searcey

Episode 563 - Auto-Man

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