Expectations over Unspoken Alternatives Predict Pragmatic Inferences
Expectations over Unspoken Alternatives Predict Pragmatic Inferences
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DOI：
10.1162/tacl_a_00579
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发表时间：
2023-07-25
影响因子：
10.9
通讯作者：
Schuster，Sebastian
中科院分区：
文献类型：
--
作者：
Hu，Jennifer;Levy，Roger;Schuster，Sebastian
标量推理（SI）是人类如何基于未说出的替代来解释语言的一个典型例子。虽然实证研究表明，人类SI率是高度可变的，无论是在一个单一的规模的情况下，并在不同的规模，有一些建议，定量解释跨和规模内的变化。此外，虽然人们普遍认为，通过推理的潜台词的替代品，它仍然存在争议，人类的原因是否替代语言形式，或在概念的水平。在这里，我们测试了一个共享的机制，解释SI率内和跨尺度：上下文驱动的期望的未说出口的替代品。使用神经语言模型来近似人类预测分布，我们发现SI率被强标度函数的预期性捕获作为替代。然而，至关重要的是，只有在基于意义的替代观点下，预期性才能有力地预测跨尺度变化。我们的研究结果表明，语用推理产生于上下文驱动的期望的替代品，这些期望在概念的水平上运作。
Scalar inferences (SI) are a signature example of how humans interpret language based on unspoken alternatives. While empirical studies have demonstrated that human SI rates are highly variable—both within instances of a single scale, and across different scales—there have been few proposals that quantitatively explain both cross- and within-scale variation. Furthermore, while it is generally assumed that SIs arise through reasoning about unspoken alternatives, it remains debated whether humans reason about alternatives as linguistic forms, or at the level of concepts. Here, we test a shared mechanism explaining SI rates within and across scales: context-driven expectations about the unspoken alternatives. Using neural language models to approximate human predictive distributions, we find that SI rates are captured by the expectedness of the strong scalemate as an alternative. Crucially, however, expectedness robustly predicts cross-scale variation only under a meaning-based view of alternatives. Our results suggest that pragmatic inferences arise from context-driven expectations over alternatives, and these expectations operate at the level of concepts.