Children use algorithm induction to discover patterns in data

Humans are unique in our ability to acquire diverse skills and inhabit myriad environments, but the cognitive mechanisms underlying such fast, flexible learning remain unresolved. Inspired by theories of artificial intelligence, here we show evidence for one such learning mechanism - program induction - in US American and indigenous Tsimane’ children in the Bolivian Amazon. Participants viewed novel patterns and were asked to generalize them to new stimuli, alphabets, and lengths, without feedback. Given very limited data, participants across ages, cultures, and conditions constructed response patterns that shared abstract structure with the sample patterns. Computational modeling shows that responses likely reflect discovery of latent rules, rather than simple heuristics or associations, even among children without formal schooling. The results suggest program induction serves as a domain-general learning mechanism from early in life, allowing children across cultures to rapidly infer the algorithmic structure of their natural and cultural environment, whatever it might be.

Approaching an unknown communication system by latent space exploration and causal inference | Royal Society Open Science | The Royal Society

Abstract. We propose a methodology for discovering meaningful properties in data without ground truth by combining manipulation of the latent variables of

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Manoel Horta Ribeiro (@manoelhortaribeiro.bsky.social)

Assistant Professor @ Princeton Previously: EPFL 🇨🇭, UFMG 🇧🇷 Interests: Computational Social Science, Platforms, GenAI, Moderation

Abeba Birhane on Twitter / X

a classic case of “the human mind is afforded less complexity than is owed, and the computer is afforded more wisdom than is due.” Baria and Cross (2021)that "vibe" is at the core of what makes us human. it defies formalization and datafication https://t.co/AXELAqlYcq— Abeba Birhane (@Abebab) February 17, 2024

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