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

## TL;DR

This paper introduces ERMI, a meta-learning framework that models human cognition by capturing ecological regularities in naturalistic tasks, explaining human learning and decision making.

## Contribution

It develops a novel ecologically grounded meta-learning approach that predicts human behavior across diverse cognitive tasks without handcrafted heuristics.

## Key findings

- ERMI outperforms existing models in predicting human trial-by-trial behavior.
- The framework captures human responses in 15 experiments involving various cognitive tasks.
- Results suggest human cognition is adaptively aligned with ecological task structures.

## Abstract

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.