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

TL;DR

This paper investigates how modern semantic embeddings can model human memory retrieval as an optimal foraging process, showing that simple random walks align well with human behavior, while more complex algorithms like Metropolis-Hastings do not.

Contribution

The study demonstrates that simple random walk algorithms on high-dimensional embeddings can effectively model human memory foraging, challenging the need for complex sampling methods.

Findings

Random walks on embeddings match human foraging patterns.

Metropolis-Hastings sampling does not align with human behavior.

Structured embeddings enable near-optimal foraging without complex algorithms.

Abstract

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.