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.

类别流畅性是一种广泛研究的认知现象，然而关于底层检索机制却存在两种相互冲突的解释 —— 一种是有意识地通过记忆进行优化的觅食过程，另一种是从语义网络中进行随机游走采样。通过对现有模型的假设进行剥离，我们发现类别转换概率可以直接建模，并依赖于大规模语言模型进行预测；进一步，我们通过将模型重新构建为序列生成器来解决有关觅食的分歧。通过将生成的类别流畅性序列与人类编写的序列进行比较，我们提出了基于 n-gram 重叠的度量标准，并发现类别转换预测器不一定能产生类似人类的序列，事实上，Hills 等人 (2012) 模型使用的附加偏差是提高生成质量所必需的，这些偏差在我们的类别修改后得到了改进。即使仅使用 LLM 进行生成，也需要额外的全局提示来触发产生过程中的区块切换行为。对语义网络上的搜索过程进行进一步测试，突显了确定性搜索对于复制人类行为的重要性。

朝着一个路径依赖的类别流利性账户

Towards a path dependent account of category fluency

Word embedding has become ubiquitous and is widely used in various text
mining and natural language processing (NLP) tasks, such as information
retrieval, semantic analysis, and machine translation, among many others.
Unfortunately, it is prohibitively expensive to train the word embedding in a
relatively large corpus. We propose a graph-based word embedding algorithm,
called Word-Graph2vec, which converts the large corpus into a word
co-occurrence graph, then takes the word sequence samples from this graph by
randomly traveling and trains the word embedding on this sampling corpus in the
end. We posit that because of the stable vocabulary, relative idioms, and fixed
expressions in English, the size and density of the word co-occurrence graph
change slightly with the increase in the training corpus. So that
Word-Graph2vec has stable runtime on the large scale data set, and its
performance advantage becomes more and more obvious with the growth of the
training corpus. Extensive experiments conducted on real-world datasets show
that the proposed algorithm outperforms traditional Skip-Gram by four-five
times in terms of efficiency, while the error generated by the random walk
sampling is small.

提出了一种基于图形的单词嵌入算法 Word-Graph2vec，通过将大语料库转换为单词共现图然后从该图中随机抽取单词序列样本并在此抽样语料库上训练单词嵌入，在实验中表现出较高的效率并且随着训练语料的增加，其性能优势变得越来越明显。