Diego Morales

Verbal fluency tasks reveal clustering and switching patterns traditionally explained by strategic search or stochastic processes like Lévy or random walks. However, previous comparisons ignored how search processes interact with semantic structure, leaving unclear whether model performance reflects strategic mechanisms or fortuitous alignment with semantic organization. This study developed and validated a novel Area Restricted Search (ARS) agent‐based model of semantic memory retrieval, then systematically compared it against Lévy Walk (LW) and Random Walk (RW) models to investigate when different search mechanisms succeed under varying structural conditions. The model implements incremental decision‐making based on local information, without predetermined switching points or complete semantic space access. Semantic structure parameters were treated as free variables during optimization, allowing examination of process–structure interactions across diverse configurations. Performance was evaluated against 50 participants across three semantic categories using clustering, switching, and temporal variables. Two simulations examined model fit and adaptability to varying semantic structures. Different mechanisms require distinct semantic configurations: ARS performed well in moderate clustering, LW in sparse arrangements, and RW under dense clustering, but RW generated response distributions different from participants. However, when semantic density was constrained while varying cluster dispersion, ARS maintained human‐like performance across multiple configurations, while LW showed limited flexibility, and RW consistently failed to get close to participants' response distributions. These findings show that human‐like semantic memory retrieval across diverse contexts requires strategic mechanisms capable of dynamic adaptation to varying semantic organizations, rather than universal superiority of any single approach or of models based on context‐independent stochastic processes.