Back to the Future: The Role of Past and Future Context Predictability in Incremental Language Production

TL;DR

This paper investigates how past and future contextual predictability influence incremental language production, introducing a new information-theoretic measure that better captures backward predictability effects and their role in speech errors.

Contribution

It proposes a novel measure of predictability that accounts for future context, demonstrating its effectiveness in explaining speech errors and advancing understanding of lexical planning.

Findings

The new measure aligns with backward predictability effects and explains unique variance in phonetic reduction.

Past predictability increases error likelihood, while future predictability decreases it.

The measure is the strongest predictor of error identity, surpassing traditional backward predictability.

Abstract

Contextual predictability shapes how we choose and encode words in production. The effects of a word's predictability given preceding or past context are generally well-understood in both production and comprehension, but studies of naturalistic production have also revealed a poorly-understood yet robust backward predictability effect of a word given only its future context, which may be linked to future planning. Across two studies of naturalistic speech, we revisit backward predictability using improved operationalizations, introducing a conceptually motivated information-theoretic measure that quantifies the information shared between a word and future context under the constraints imposed by the past context. Study 1 shows that this measure produces effects qualitatively similar to backward predictability while explaining unique variance in phonetic reduction. Study 2 examines substitution errors within a generative framework that models lexical, contextual, and communicative influences on word choice to predict the identity of the word that surfaces as an error. Within this framework, we find that past-conditioned predictability increases error likelihood, whereas future-conditioned predictability reduces it. Further, our proposed measure emerges as the strongest contextual predictor of error identity, subsuming backward predictability. Analysis of error types further reveals graded trade offs in how speakers prioritize form-, meaning-, and context-based information during lexical planning. Together, these findings illuminate how past and future context shape word choice and encoding, linking contextual predictability to mechanisms of incremental planning in sentence production.