Expectations over Unspoken Alternatives Predict Pragmatic Inferences

TL;DR

This paper proposes that pragmatic inferences, specifically scalar inferences, are driven by context-based expectations about unspoken alternatives, which operate at the conceptual level and explain variability in human inference rates.

Contribution

It introduces a quantitative model linking scalar inference variability to expectations over unspoken alternatives, emphasizing a meaning-based conceptual perspective.

Findings

Neural language models predict scalar inference rates based on expectedness of alternatives.

Expectedness of strong scalemates explains cross-scale variation in inference rates.

Pragmatic inferences are driven by expectations over concepts, not just linguistic forms.

Abstract

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.