# Meta-learning ecological priors from large language models explains human learning and decision making

arXiv: https://arxiv.org/abs/2509.00116 · HTML: https://mlsanity.com/paper/2509.00116/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 73 references that are in the map.
- First posted on arXiv 28 Aug 2025.

## Builds on (most cited first, 14 of 14)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Learning to reinforcement learn](https://mlsanity.com/paper/1611.05763/) (2016)
- [Transformers Can Do Bayesian Inference](https://mlsanity.com/paper/2112.10510/) (2021)
- [Data Distributional Properties Drive Emergent In-Context Learning in Transformers](https://mlsanity.com/paper/2205.05055/) (2022)
- [Language Models are Realistic Tabular Data Generators](https://mlsanity.com/paper/2210.06280/) (2022)
- [OpenML Benchmarking Suites](https://mlsanity.com/paper/1708.03731/) (2017)
- [The Road Less Scheduled](https://mlsanity.com/paper/2405.15682/) (2024)
- [Human-Timescale Adaptation in an Open-Ended Task Space](https://mlsanity.com/paper/2301.07608/) (2023)
- [Meta-learning of Sequential Strategies](https://mlsanity.com/paper/1905.03030/) (2019)
- [Eliciting the Priors of Large Language Models using Iterated In-Context Learning](https://mlsanity.com/paper/2406.01860/) (2024)
- [In-context learning agents are asymmetric belief updaters](https://mlsanity.com/paper/2402.03969/) (2024)
- [Modeling Human Exploration Through Resource-Rational Reinforcement Learning](https://mlsanity.com/paper/2201.11817/) (2022)
- [Human-like Category Learning by Injecting Ecological Priors from Large Language Models into Neural Networks](https://mlsanity.com/paper/2402.01821/) (2024)

## Built on by (most cited first, 4 of 4)

- [PriorProbe: Recovering Individual-Level Priors for Personalizing Neural Networks in Facial Expression Recognition](https://mlsanity.com/paper/2602.03882/) (2026)
- [Using Probabilistic Programs to Train Inductive Reasoning in Large Language Models](https://mlsanity.com/paper/2606.09856/) (2026)
- [Meta-learning as a principle for human-like visual representations](https://mlsanity.com/paper/2606.28399/) (2026)
- [More accurate behavioral predictions with hybrid Bayesian-connectionist models](https://mlsanity.com/paper/2608.22154/) (2026)

## Abstract (arXiv)

Human cognition is profoundly shaped by the environments in which it unfolds. Yet, it remains an open question whether learning and decision making can be explained as a principled adaptation to the statistical structure of real-world tasks. We introduce ecologically rational analysis, a computational framework that unifies the normative foundations of rational analysis with ecological grounding. Leveraging large language models to generate ecologically valid cognitive tasks at scale, and using meta-learning to derive rational models optimized for these environments, we develop a new class of learning algorithms: Ecologically Rational Meta-learned Inference (ERMI). ERMI internalizes the statistical regularities of naturalistic problem spaces and adapts flexibly to novel situations, without requiring hand-crafted heuristics or explicit parameter updates. We show that ERMI captures human behavior across 15 experiments spanning function learning, category learning, and decision making, outperforming several established cognitive models in trial-by-trial prediction. Our results suggest that much of human cognition may reflect adaptive alignment to the ecological structure of the problems we encounter in everyday life.