Optimal Foraging in Memory Retrieval: Evaluating Random Walks and Metropolis-Hastings Sampling in Modern Semantic Spaces

Human memory retrieval often resembles ecological foraging where animals search for food in a patchy environment. Optimal foraging means following the Marginal Value Theorem (MVT), in which individuals exploit a patch of semantically related concepts until it becomes less rewarding and then switch to a new cluster. While human behavioral data suggests foraging-like patterns in semantic fluency tasks, it remains unclear whether modern high-dimensional embedding spaces provide representations that allow algorithms to match observed human behavior. Using state-of-the-art embeddings and prior semantic fluency data, I find that random walks on these embedding spaces produce results consistent with optimal foraging and the MVT. Surprisingly, introducing Metropolis-Hastings sampling, an adaptive algorithm expected to model strategic acceptance and rejection of new clusters, does not produce results consistent with human behavior. These findings challenge the assumption that more complex sampling mechanisms inherently lead to better cognitive models of memory retrieval. Instead, they show that appropriately structured embeddings, even with simple sampling, can produce near-optimal foraging dynamics. This supports the perspective of Hills (2012) rather than Abbott (2015), demonstrating that modern embeddings can approximate human memory foraging without relying on complex acceptance criteria.

TextWorld: A Learning Environment for Text-based Games

We introduce TextWorld, a sandbox learning environment for the training and evaluation of RL agents on text-based games. TextWorld is a Python library that handles interactive play-through of text games, as well as backend functions like state tracking and reward assignment. It comes with a curated list of games whose features and challenges we have analyzed. More significantly, it enables users to handcraft or automatically generate new games. Its generative mechanisms give precise control over the difficulty, scope, and language of constructed games, and can be used to relax challenges inherent to commercial text games like partial observability and sparse rewards. By generating sets of varied but similar games, TextWorld can also be used to study generalization and transfer learning. We cast text-based games in the Reinforcement Learning formalism, use our framework to develop a set of benchmark games, and evaluate several baseline agents on this set and the curated list.