The use of intonation for turn anticipation in observed conversations without visual signals as source of information

Keitel, Anne; Daum, Moritz M.

doi:10.3389/fpsyg.2015.00108

Cited by 19 publications

(55 citation statements)

References 23 publications

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“…Recent experimental findings demonstrate that toddlers use both lexicosyntactic and prosodic information to predict upcoming speaker switches, but the relative importance of these information sources for prediction remains largely undetermined Frank, 2012, 2013;Keitel and Daum, 2015). The current study investigates how Dutch and English toddlers weigh lexicosyntactic and prosodic 1 cues against one another in their online prediction of upcoming speaker switch.…”

Section: Resultsmentioning

confidence: 99%

“…In line with previous studies investigating children's anticipation of turn structure Keitel and Daum, 2015), we used puppet dyads to capture children's attention while also conveniently removing the non-verbal cues to turn taking that often appear at turn boundaries (e.g., gaze and gesture; . The absence of non-verbal cues enabled us to focus on the role of linguistic cues.…”

Section: Resultsmentioning

confidence: 99%

“…Children do not often hear this kind of phonetically controlled speech in their natural language environment. ; Keitel and Daum (2015) performed a similar study, showing children videos of conversation and using children's age (rather than phonetic manipulation) to control for the availability of lexicosyntactic cues; they tested both pre-verbal (6-and 12-month-old) and verbal (24-and 36-month-old) children. To test for the role of intonation, half of the videos featured pitchflattened speech and the other half featured a full linguistic signal.…”

Section: Resultsmentioning

confidence: 99%

“…But, again, the primary linguistic control in the stimuli depended on phonetic manipulation of the speech signal. Thus, the results of these prior studies- and ; Keitel and Daum (2015)-are based on a comparison between natural (full signal) and non-natural (phonetically manipulated) stimuli.…”

Section: Resultsmentioning

confidence: 99%

“…Eye tracking is a natural and passive measure of attention, but provides an online measure of children's predictive processing during conversation Frank, 2012, 2013;Keitel and Daum, 2015). Prior work has shown that, compared to explicit measures of turn-end prediction (e.g., button-press; de , anticipatory eye movements from the prior to the next speaker tend to occur quite late at points of speaker transition (children: Frank, 2012, 2013;Keitel and Daum, 2015;and adults: Tice and Henetz, 2011;; but also see for earlier switching in adults).…”

Section: Resultsmentioning

confidence: 99%

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Turn-Taking in Human Communicative Interaction

Holler

Kendrick

Casillas

et al. 2016

Frontiers Research Topics

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The core use of language is in face-to-face conversation. This is characterized by rapid turn-taking. This turn-taking poses a number central puzzles for the psychology of language. Consider, for example, that in large corpora the gap between turns is on the order of 100 to 300 ms, but the latencies involved in language production require minimally between 600ms (for a single word) or 1500 ms (for as simple sentence). This implies that participants in conversation are predicting the ends of the incoming turn and preparing in advance. But how is this done? What aspects of this prediction are done when? What happens when the prediction is wrong? What stops participants coming in too early? If the system is running on prediction, why is there consistently a mode of 100 to 300 ms in response time? The timing puzzle raises further puzzles: it seems that comprehension must run parallel with the preparation for production, but it has been presumed that there are strict cognitive limitations on more than one central process running at a time. How is this bottleneck overcome? Far from being 'easy' as some psychologists have suggested, conversation may be one of the most demanding cognitive tasks in our everyday lives. Further questions naturally arise: how do children learn to master this demanding task, and what is the developmental trajectory in this domain? Research shows that aspects of turn-taking such as its timing are remarkably stable across languages and cultures, but the word order of languages varies enormously. How then does prediction of the incoming turn work when the verb (often the informational nugget in a clause) is at the end? Conversely, how can production work fast enough in languages that have the verb at the beginning, thereby requiring early planning of the whole clause? What happens when one changes modality, as in sign languages -- with the loss of channel constraints is turn-taking much freer? And what about face-to-face communication amongst hearing individuals -- do gestures, gaze, and other body behaviors facilitate turn-taking? One can also ask the phylogenetic question: how did such a system evolve? There seem to be parallels (analogies) in duetting bird species, and in a variety of monkey species, but there is little evidence of anything like this among the great apes. All this constitutes a neglected set of problems at the heart of the psychology of language and of the language sciences. This research topic welcomes contributions from right across the board, for example from psycholinguists, developmental psychologists, students of dialogue and conversation analysis, linguists interested in the use of language, phoneticians, corpus analysts and comparative ethologists or psychologists. We welcome contributions of all sorts, for example original research papers, opinion pieces, and reviews of work in subfields that may not be fully understood in other subfields

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Turn-Taking in Human Communicative Interaction

Holler

Kendrick

Casillas

et al. 2016

Frontiers Research Topics

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Social Processes: Self-supervised Meta-learning Over Conversational Groups for Forecasting Nonverbal Social Cues

Raman

Hung

Loog

2023

Lecture Notes in Computer Science

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Free-standing social conversations constitute a yet underexplored setting for human behavior forecasting. While the task of predicting pedestrian trajectories has received much recent attention, an intrinsic difference between these settings is how groups form and disband. Evidence from social psychology suggests that group members in a conversation explicitly self-organize to sustain the interaction by adapting to one another's behaviors. Crucially, the same individual is unlikely to adapt similarly across different groups; contextual factors such as perceived relationships, attraction, rapport, etc., influence the entire spectrum of participants' behaviors. A question arises: how can we jointly forecast the mutually dependent futures of conversation partners by modeling the dynamics unique to every group? In this paper, we propose the Social Process (SP) models, taking a novel meta-learning and stochastic perspective of group dynamics. Training group-specific forecasting models hinders generalization to unseen groups and is challenging given limited conversation data. In contrast, our SP models treat interaction sequences from a single group as a meta-dataset: we condition forecasts for a sequence from a given group on other observed-future sequence pairs from the same group. In this way, an SP model learns to adapt its forecasts to the unique dynamics of the interacting partners, generalizing to unseen groups in a data-efficient manner. Additionally, we first rethink the task formulation itself, motivating task requirements from social science literature that prior formulations have overlooked. For our formulation of Social Cue Forecasting, we evaluate the empirical performance of our SP models against both non-meta-learning and meta-learning approaches with similar assumptions. The SP models yield improved performance on synthetic and real-world behavior datasets.

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You cannot speak and listen at the same time: a probabilistic model of turn-taking

et al. 2017

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Turn-taking is a preverbal skill whose mastering constitutes an important precondition for many social interactions and joint actions. However, the cognitive mechanisms supporting turn-taking abilities are still poorly understood. Here, we propose a computational analysis of turn-taking in terms of two general mechanisms supporting joint actions: action prediction (e.g., recognizing the interlocutor's message and predicting the end of turn) and signaling (e.g., modifying one's own speech to make it more predictable and discriminable). We test the hypothesis that in a simulated conversational scenario dyads using these two mechanisms can recognize the utterances of their co-actors faster, which in turn permits them to give and take turns more efficiently. Furthermore, we discuss how turn-taking dynamics depend on the fact that agents cannot simultaneously use their internal models for both action (or messages) prediction and production, as these have different requirements-or, in other words, they cannot speak and listen at the same time with the same level of accuracy. Our results provide a computational-level characterization of turn-taking in terms of cognitive mechanisms of action prediction and signaling that are shared across various interaction and joint action domains.

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The use of intonation for turn anticipation in observed conversations without visual signals as source of information

Cited by 19 publications

References 23 publications

Turn-Taking in Human Communicative Interaction

Turn-Taking in Human Communicative Interaction

Social Processes: Self-supervised Meta-learning Over Conversational Groups for Forecasting Nonverbal Social Cues

You cannot speak and listen at the same time: a probabilistic model of turn-taking

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