Presentations

Abstract

Usage-based approaches to language acquisition (e.g. Tomasello, 2003) propose that children use multi-word utterances – chunks – to build up grammatical knowledge from recurring patterns in their linguistic input. We investigate the changing influence of this statistical, chunk-based learning on children’s language production over time using the CAPPUCCINO model (McCauley & Christiansen, 2011). This model simulates child language production using chunks extracted from caregivers’ speech.
We selected orthographic transcriptions of conversations between 6 North American children and their caregivers, by sampling transcripts at 6-month intervals between 1;0 and 4;0 (Providence; Demuth, Culbertson, & Alter, 2006). The model parsed caregivers’ utterances for each child by comparing the transitional probabilities between words to a running average transitional probability, making splits between word chunks when the transitional probability between two words dropped below the current average. At the same time, the model also tracked the transitional probabilities between these discovered chunks. After training the model, we simulated children’s sentence production by reconstructing the utterances they actually used in the transcript from the chunk-to-chunk probabilities detected in the caregivers’ speech.
The number of child utterances that were reconstructed correctly based on transitional probabilities between chunks in the caregivers’ speech decreased over time (β = - 0.720, SE = 0.157, p < 0.001). However, the number of child utterances that contained words or chunks the caregivers did not use, increased (β = 0.547, SE = 0.064, p < 0.001). In other words, these results indicate that, over time, children’s speech less directly imitates chunk sequences in their caregivers’ speech, partly because their chunk combinations become more inventive. We discuss how these findings fit within broader usage-based approaches to language acquisition.

Abstract

We investigate children’s online predictive processing as it occurs naturally, in conversation. We showed 129 children (1;0–7;0) short videos of improvised conversation between puppets, controlling for available linguistic information through phonetic manipulation: normal, prosody only (lowpass filtered), lexical only (rhythm controlled and pitch flattened), and none (multi-talker babble). We tracked their eye movements during the videos, measuring their anticipatory looks to upcoming speakers at points of turn switch (e.g., after a question and before an answer). Even one- and two-year-old children made accurate and spontaneous predictions about when a turn-switch would occur: they gazed at the upcoming speaker before they heard a response begin. By age three, children distinguished between different types of response-eliciting speech acts, looking faster to question- than non-question responses—but only when all linguistic information was available. By age seven, children’s gaze behavior also distinguished between rising and non-rising turns in the prosody only condition. These predictive skills rely on both lexical and prosodic information together, and are not tied to either type of information alone. We suggest that children integrate prosodic, lexical, and visual information to effectively predict upcoming linguistic material in conversation.

Abstract

Combining data from two longitudinal studies of young children, we track the development of turn-timing in spontaneous infant-caregiver interactions. We focus on three aspects of timing: overlap, gap, and delay marking. We find evidence for early development of turn-timing skills, in-line with the Interaction Engine Hypothesis. (see attached .pdf for our 2-page abstract)