
Your Baby Is Recruiting Your Eyes
Joint attention turns looking into a shared problem-solving loop. Here’s what infant brains, social robots, and a cardboard marble run reveal.
30 articles

Joint attention turns looking into a shared problem-solving loop. Here’s what infant brains, social robots, and a cardboard marble run reveal.

Preschoolers can generalize visual rules that challenge multimodal AI. What this gap teaches parents about confidence, understanding, and educational tools.

AI can predict human behavior without proving it understands us. Here’s how parents can separate impressive performance from genuine social awareness.

Why children often understand instructions through action, feedback, and revision—and what embodied robots reveal about helping learning stick.

How children learn hidden meanings, indirect requests, and social context—and why fluent AI still takes a very different route.

Why kids learn skills from wobbles, pauses, and prediction errors — and what brain replay can teach parents about practice.

Children’s curiosity can look chaotic, but research suggests their exploration follows a learning logic that parents can support without over-controlling.

Developmental dyscalculia shows why some children struggle with numbers — and how AI models can help us see learning differences more clearly.

Infants do more than watch objects fall. Their brains build predictions about the physical world — and curiosity helps tune the model.

Why back-and-forth interaction builds children's thinking in ways solo practice cannot — and what social AI still struggles to copy.

Why pruning, compression, and developmental timing matter more than simply adding more — for kids' brains and smarter AI.

Why children often generalize visual patterns better than powerful AI — and how parents can support the embodied, comparison-rich learning that builds flexible thinking.

Why children raised in the same home still develop differently — and what epigenetics, critical periods, and AI training can teach parents about individuality.

Sleep is not lost learning time. Brain research and AI replay systems both suggest that children need downtime to make learning stick.

The brain-runs-on-RL story is elegant, productive, and partially true. But 2025 has four new studies that make the simple version increasingly hard to defend — and the complicated version way more interesting.

Tania Lombrozo calls it "learning by thinking" — gaining genuinely new knowledge without new input. LLMs can now do something that looks identical. Here's why the difference between them might be the most important question in cognitive science right now.

The neurons that track where you are in a room are mathematically related to the ones that organize what words mean. Grid cells, conceptual space, and why AI independently stumbled onto the same solution — and what tiny 4-neuron networks reveal about the scale delusion.

A four-month-old staring at a teleporting ball isn't just cute — it's running a physics engine no language model was ever given. Here's why human causal cognition is a vertical stack, and AI only has the top floor.

Peer learning isn't a nice pedagogical bonus — it's load-bearing for cognition. New neuroscience shows what the socially interactive brain is actually doing, and why multi-agent AI isn't even close to replicating it.

Reading is an evolutionary hack — no brain region was born for it, and no child learns it without years of effortful, phonologically grounded work. LLMs never had to do any of that. The difference turns out to matter enormously.

Preschoolers outperform frontier AI on visual analogy tasks. AI models skip the developmental milestone sequence children follow. Fine-tuning isn't expertise — and the difference tells us something important about both.

Children say "goed" and "maked" because they extracted a rule. That's not an error — it's a generalization engine in action. Here's why the same thing that makes kids say wrong things is exactly what AI still can't do right.

Children spend years learning that language rarely means what it literally says. LLMs can generate grammatically perfect sentences — but whether they understand what those sentences actually do in conversation is a different, harder question.

A five-year-old adjusting the wrong wheel on a cardboard car for twenty minutes, a toddler reaching for a moved toy in the wrong spot, an AI model locked onto its training distribution — they all share the same problem. Here's what genuine cognitive flexibility actually requires, and why it's one of the hardest things any mind can do.

Children track who's reliable, who's overconfident, and who might be lying — by age 4. AI systems are trained to agree. That asymmetry has real consequences.

Children don't just add information when they're wrong — they rebuild entire frameworks. LLMs have no such thing. Here's why belief revision might be the deepest gap between biological and artificial minds.

Russian speakers distinguish blues faster than English speakers do — because Russian draws a categorical boundary English doesn't. LLMs are trained on language too. But what gets shaped, and what doesn't, reveals a surprising asymmetry.

Preschoolers beat GPT-o1, GPT-4V, and LLaVA on simple visual analogy tasks. The gap reveals something foundational about how children — and machines — actually reason about structure.

Stories aren't just how humans communicate — they're how we think. Language models can predict your brain's response to a sentence. They still can't tell a story. Here's why the gap is wider than it looks.

AI hallucinates because it doesn't know what it doesn't know. Children do. Here's why metacognition — the ability to track your own uncertainty — is the cognitive gap that actually matters.