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 strict adherences to the Marginal V alue Thereom (MVT) in which individuals exploit a "patch" of semantically related concepts until it becomes less rewarding, then switch to a new cluster. While human behavioral data suggests foraging-like patterns in semantic fluency tasks, it is still unknown whether modern high-dimensional embedding spaces provide a sufficient representation for algorithms to closely match observed human behavior. By leveraging state-of-the-art embeddings and prior clustering and human semantic fluency data I find that random walks on these semantic embedding spaces produces results consistent with optimal foraging and the MVT. Surprisingly, introducing Metropolis-Hastings, an adaptive algorithm expected to model strategic acceptance and rejection of new clusters, does not produce results consistent with observed human behavior. These findings challenge the assumption that sophisticated sampling mechanisms inherently provide better cognitive models of memory retrieval. Instead, they highlight that appropriately structured semantic embeddings, even with minimalist sampling approaches, can produce near-optimal foraging dynamics. In doing so, my results support the perspective of Hills (2012) rather than Abbott (2015), demonstrating that modern embed-dings can approximate human memory foraging without relying on complex acceptance criteria.