Evidence map›Paper›PMID 41144721›Full record

ArticleCognitive science2025

The Relationship Between Surprisal and Prosodic Prominence in Conversation Reflects Intelligibility-Oriented Pressures.

Thomas Hikaru Clark, Moshe Poliak, Tamar Regev, A J Haskins, Caroline Robertson, Edward Gibson

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Article in Cognitive science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. A distinct set of brain areas process prosody-the melody of speech.bioRxiv : the preprint server for biology · 2025
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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Thomas Hikaru ClarkDepartment of Brain and Cognitive Sciences, Massachusetts Institute of Technology.
Moshe PoliakDepartment of Brain and Cognitive Sciences, Massachusetts Institute of Technology.
Tamar RegevDepartment of Brain and Cognitive Sciences, Massachusetts Institute of Technology.
A J HaskinsDepartment of Psychological and Brain Sciences, Dartmouth College.
Caroline RobertsonDepartment of Psychological and Brain Sciences, Dartmouth College.
Edward GibsonDepartment of Brain and Cognitive Sciences, Massachusetts Institute of Technology.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Conversation is a dynamic, multimodal activity involving the exchange of complex streams of information like words, prosody, gesture, eye contact, and backchannels. Understanding how these different channels interact in naturalistic scenarios is essential for understanding the mechanisms governing human communication. Past studies suggested that the duration of words is tied to their predictability in context, but it remains unclear whether this relationship is speaker-oriented (e.g., retrieval or production-based) or due to listener-oriented, intelligibility-based pressures (i.e., emphasizing unpredictable words to ease comprehension). This study aims to examine the relationship between predictability and additional acoustic variables, to test how much intelligibility-oriented principles impact conversation. We use the GPT-2 large language model to assess the relationship between surprisal, a measure of unpredictability, and several variables known to play an important role in conversation-the prosodic features of duration, intensity, and pitch. We perform this analysis on the CANDOR corpus of naturalistic spoken video call conversation between strangers in English. In keeping with previous results using n-gram predictability, we find that GPT-2 surprisal predicts significantly higher values for duration. Moreover, surprisal also predicts maximum pitch and pitch range even when controlling for duration, with mixed evidence for an effect of surprisal on intensity. Additionally, we investigated listener backchannels (short interjections like "yeah" or "mhm") and found that listener backchannels tended to be accompanied and followed by a spike in the surprisal of speakers' words. Finally, we demonstrate a divergence between the effect of context window size on the model fit of surprisal to maximum pitch and to other variables. The results provide additional support for intelligibility-based accounts, which hold that language production is sensitive to a pressure for successful communication, not just speaker-oriented pressures. Our data and analysis code are shared: https://osf.io/sqpn6/?view_only=e4d9e36c68b54863bc781e359463e1fe.

Indexed as

CommunicationLanguageSpeechSpeech AcousticsSpeech IntelligibilitySpeech PerceptionComprehensionHumansAudience designBackchannelsConversationLarge language modelsProbabilistic reductionProsodic prominenceSurprisalUniform information density

Identifiers

PMID41144721
PMCPMC12558650

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.