Category fluency is a widely studied cognitive phenomenon, yet two conflicting accounts have been proposed as the underlying retrieval mechanism -- an optimal foraging process deliberately searching through memory (Hills et al., 2012) and a random walk sampling from a semantic network (Abbott et al., 2015). Evidence for both accounts has centered around predicting human patch switches, where both existing models of category fluency produce paradoxically identical results. We begin by peeling back the assumptions made by existing models, namely that each named example only depends on the previous example, by (i) adding an additional bias to model the category transition probability directly and (ii) relying on a large language model to predict based on the entire existing sequence. Then, we present evidence towards resolving the disagreement between each account of foraging by reformulating models as sequence generators. To evaluate, we compare generated category fluency runs to a bank of human-written sequences by proposing a metric based on n-gram overlap. We find category switch predictors do not necessarily produce human-like sequences, in fact the additional biases used by the Hills et al. (2012) model are required to improve generation quality, which are later improved by our category modification. Even generating exclusively with an LLM requires an additional global cue to trigger the patch switching behavior during production. Further tests on only the search process on top of the semantic network highlight the importance of deterministic search to replicate human behavior.

翻译：类别流畅性是一种被广泛研究的认知现象，然而其潜在检索机制存在两种相互矛盾的解释——一种是有意识搜索记忆的最优觅食过程（Hills等，2012），另一种是从语义网络中随机游走采样（Abbott等，2015）。两种解释的证据均集中于预测人类“补丁切换”行为，而现有类别流畅性模型却产生了看似一致的矛盾结果。我们首先通过剥离现有模型的假设（即每个命名示例仅依赖于前一个示例），采取两种改进方式：（i）直接添加额外偏置以建模类别转移概率，（ii）依赖大型语言模型基于整个现有序列进行预测。随后，我们将觅食模型重新表述为序列生成器，为解决两种解释间的分歧提供证据。为了评估模型，我们提出基于n-gram重叠的度量指标，将生成的类别流畅性序列与人类撰写的序列库进行比较。研究发现：类别切换预测器不一定能生成类人序列，实际上需采用Hills等（2012）模型中的额外偏置来提升生成质量，而我们的类别修正进一步优化了该效果。即使完全使用大型语言模型生成序列，也需额外全局线索触发生产过程中的补丁切换行为。仅对语义网络上的搜索过程进行测试进一步凸显了确定性搜索在复现人类行为中的重要性。