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[2509.00074] Language and Experience: A Computational Model of Social Learning in Complex Tasks
Summary
This article presents a computational model that explores how humans and AI can integrate linguistic guidance and direct experience for effective social learning in complex tasks.
Why It Matters
Understanding the interplay between language and experience in learning processes is crucial for advancing AI systems that can collaborate with humans. This research provides insights into how AI can better mimic human learning patterns, potentially leading to more effective and safer AI applications in various fields.
Key Takeaways
- The model combines linguistic guidance with experiential learning for enhanced social learning.
- Behavioral experiments demonstrate how language can accelerate learning and reduce risks in complex tasks.
- Knowledge transfer between humans and AI can occur through structured, language-compatible representations.
Computer Science > Artificial Intelligence arXiv:2509.00074 (cs) [Submitted on 26 Aug 2025 (v1), last revised 18 Feb 2026 (this version, v2)] Title:Language and Experience: A Computational Model of Social Learning in Complex Tasks Authors:Cédric Colas, Tracey Mills, Ben Prystawski, Michael Henry Tessler, Noah Goodman, Jacob Andreas, Joshua Tenenbaum View a PDF of the paper titled Language and Experience: A Computational Model of Social Learning in Complex Tasks, by C\'edric Colas and 6 other authors View PDF HTML (experimental) Abstract:The ability to combine linguistic guidance from others with direct experience is central to human development, enabling safe and rapid learning in new environments. How do people integrate these two sources of knowledge, and how might AI systems? We present a computational framework that models social learning as joint probabilistic inference over structured, executable world models given sensorimotor and linguistic data. We make this possible by turning a pretrained language model into a probabilistic model of how humans share advice conditioned on their beliefs, allowing our agents both to generate advice for others and to interpret linguistic input as evidence during Bayesian inference. Using behavioral experiments and simulations across 10 video games, we show how linguistic guidance can shape exploration and accelerate learning by reducing risky interactions and speeding up key discoveries in both humans and models. We further explore ...
