Ecologically rational meta-learned inference explains human category
  learning
Akshay K. Jagadish Julian Coda-Forno Mirko Thalmann Eric Schulz Marcel Binz
Ecological rationality refers to the notion that humans are rational agents
adapted to their environment. However, testing this theory remains challenging
due to two reasons: the difficulty in defining what tasks are ecologically
valid and building rational models for these tasks. In this work, we
demonstrate that large language models can generate cognitive tasks,
specifically category learning tasks, that match the statistics of real-world
tasks, thereby addressing the first challenge. We tackle the second challenge
by deriving rational agents adapted to these tasks using the framework of
meta-learning, leading to a class of models called ecologically rational
meta-learned inference (ERMI). ERMI quantitatively explains human data better
than seven other cognitive models in two different experiments. It additionally
matches human behavior on a qualitative level: (1) it finds the same tasks
difficult that humans find difficult, (2) it becomes more reliant on an
exemplar-based strategy for assigning categories with learning, and (3) it
generalizes to unseen stimuli in a human-like way. Furthermore, we show that
ERMI's ecologically valid priors allow it to achieve state-of-the-art
performance on the OpenML-CC18 classification benchmark.