How much information do language users need to differentiate potentially absolute synonyms into near-synonyms? How consistent must the information be? We present two simple experiments designed to investigate this. After exposure to two novel verbs, participants generalized them to positive or negative contexts. In Experiment 1, there was a tendency across conditions for the verbs to become differentiated by context, even following inconsistent, random, or neutral information about context during exposure. While a subset of participants matched input probabilities, a high proportion did not. As a consequence, the overall pattern was of growth in differentiation that did not closely track input distributions. Rather, there were two main patterns: When each verb had been presented consistently in a positive or negative context, participants overwhelmingly specialized both verbs in their output. When this was not the case, the verbs tended to become partially differentiated, with one becoming specialized and the other remaining less specialized. Experiment 2 replicated and expanded on Experiment 1 with the addition of a pragmatic judgment task and neutral contexts at test. Its results were consistent with Experiment 1 in supporting the conclusion that quality of input may be more important than quantity in the differentiation of synonyms.

语言用户需要多少信息来区分潜在的绝对同义词和近义词？信息必须有多一致？我们提出了两个简单的实验来研究这一点。在接触到两个新动词后，参与者将它们概括为积极或消极的语境。在实验 1 中，即使在暴露期间出现关于上下文的不一致、随机或中性信息，动词也有根据上下文进行区分的趋势。虽然一部分参与者与输入概率相匹配，但很大一部分参与者却没有。因此，总体模式是分化增长，并没有密切跟踪投入分布。相反，有两种主要模式：当每个动词在积极或消极的上下文中一致呈现时，参与者绝大多数会在他们的输出中专门化这两个动词。如果情况并非如此，动词往往会部分分化，其中一个动词变得专门化，而另一个动词则保持不那么专门化。实验 2 在实验 1 的基础上进行了复制和扩展，在测试中添加了务实判断任务和中性情境。其结果与实验1一致，支持了这样的结论：在同义词区分中，输入的质量可能比数量更重要。