Explaining interactive alignment: A multimodal and multifactorial account

Oben, Bert; Brône, Geert

doi:10.1016/j.pragma.2016.07.002

Cited by 28 publications

(40 citation statements)

References 50 publications

(47 reference statements)

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“…Thus in this case adjacent speech turns are taken as the unit of analysis 5 , where the aligned lexical item or gesture can occur in any position within those turns (e.g., Fusaroli et al, 2017, for lexical alignment in free interaction and Map Task interactions). It is also possible to look at adjacent behavior independent of speech turns, e.g., by comparing a gesture that depicts a particular object with the next gesture that is produced (by the other speaker) to depict that same object in a spot-the-differences game (Oben & Brône, 2016). Hence behaviors are "grouped" based on their sequential relationin this case, adjacency.…”

Section: Sequencementioning

confidence: 99%

“…The term (interactive) alignment was originally introduced by Pickering and Garrod (2004) to refer to the interpersonal alignment of mental representations underlying linguistic behavior. However, various scholars have used the same term to simply refer to observable similarities in communicative behavior itself (e.g., Bergmann & Kopp, 2012;Fusaroli et al, 2017;Howes et al, 2010;Oben & Brône, 2016). Of course, the two senses are related (since inferences about mental representations are often made on the basis of observed behavior), but in light of theoretical discussions it is important to keep them apart.…”

Section: Introductionmentioning

confidence: 99%

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Alignment in multimodal interaction: An integrative framework

Rasenberg¹,

Asli²,

Dingemanse³

2020

Preprint

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When people are engaged in social interaction, they often repeat each other’s communicative behavior, such as words or gestures. This kind of alignment has been studied across a wide range of disciplines and has been accounted for by diverging theories. In this paper, we review various operationalizations of lexical and gestural alignment. We reveal that scholars have fundamentally different takes on when and how behavior is considered to be aligned, which makes it difficult to compare findings and draw uniform conclusions. Furthermore, we show that scholars tend to focus on one particular dimension of alignment (traditionally, whether two instances of behavior overlap in form), yet underspecify, conflate or neglect other dimensions. This stands in the way of proper theory testing and building, which requires a well-defined account of the factors that are central to or might enhance alignment. To capture the complex nature of alignment, we identify five key dimensions to formalize the relationship between any pair of behavior: sequence, time, semantics, form and modality. We show how assumptions regarding the underlying mechanism of alignment (categorized into priming versus grounding) pattern together with the operationalization in terms of the five dimensions. This conceptual framework can help researchers in the field of alignment and related phenomena (including behavior matching, mimicry, entrainment and accommodation) to formulate their hypotheses and operationalizations in a more transparent and systematic manner. The framework also enables us to discover unexplored research avenues and derive new hypotheses from existing theories.

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Section: Sequencementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Alignment in multimodal interaction: An integrative framework

Rasenberg¹,

Asli²,

Dingemanse³

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Some variation across interlocutors may be due to differences in amount of exposure to their speech. More exposure to a construction or a lexical item increases the probability that a subject will subsequently produce that item (Kaschak et al, 2011;Oben & Brône, 2016), though cumulative effects in phonetic convergence are less clear (e.g. Babel, 2012;Gijssels et al, 2016).…”

Section: Social Mediationmentioning

confidence: 99%

Natural Leaders: Some interlocutors elicit greater convergence across conversations and across characteristics

Priva¹,

Sanker²

2020

Preprint

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Are there natural followers in human communication, speakers who consistently converge more than others? Similarly, are there natural leaders, speakers with whom others converge more? Are such tendencies consistent across different linguistic characteristics? We use the Switchboard Corpus, a large collection of telephone conversations, to perform a large-scale study of convergence of speakers in multiple conversations with different interlocutors, across six linguistic characteristics. Having data from each speaker in several conversations makes it possible to investigate whether there are individual differences in likelihood to converge, among speakers (more or less likely to converge), and among interlocutors (more or less likely to elicit convergence). We only find evidence for individual differences by interlocutor, not by speaker; this shows that there are natural leaders, who elicit greater degrees of convergence than others, across different characteristics and different conversations. The absence of a similar finding for natural followers, speakers who converge more than others, suggests that the role of social aspects in mediating convergence is stronger than that of putative individual differences in propensity to converge, or that such propensities are characteristic-specific.

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“…DID is sometimes used to quantify convergence in conversational tasks, looking at the change from the two speakers' starting distance and ending distance (e.g., Pardo, Cajori Jay, & Krauss, 2010), or their distance earlier and later in the conversation (e.g., Levitan & Hirschberg, 2011;Abel & Babel, 2017). When comparing interlocutors' productions within a shared conversation, conversations may appear to be convergent due to participants independently being similarly influenced by the task, e.g., speaking more quickly as they become familiar with the task or their interlocutor; some studies compare distance between interlocutors to distance between individuals who were not interacting with each other, to control for such effects (e.g., Sanker, 2015;Oben & Brône, 2016), though many do not. In AXB or similar perceptual testing of distance, the X reference point for the interlocutor is sometimes taken from the middle of the conversation, compared to the other speaker's productions before and after the conversation or before and during the conversation (e.g., Pardo, 2006).…”

Section: Modelling Convergencementioning

confidence: 99%

Limitations of difference-in-difference for measuring convergence

Priva

Sanker

2019

Laboratory Phonology: Journal of the Association for Laboratory Phonology

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Linguistic convergence is the phenomenon in which interlocutors' speech characteristics become more similar to each other's. One of the methods frequently used to measure convergence is the difference-indifference (DID) approach, comparing change in absolute distance between a subject and an interlocutor or model talker. We show that this approach is not a reliable measure of convergence when the starting values of the subject and the interlocutor or model talker are close, which can result in the measurement of apparent divergence, while extreme starting points can result in overestimation of convergence. These biases are of particular concern in studies that look for individual differences in convergence. We propose an alternative approach, linear combination, which does not have the same biases, and demonstrate the advantages of this method using data from convergence studies of four linguistic characteristics and simulated data.

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Explaining interactive alignment: A multimodal and multifactorial account

Cited by 28 publications

References 50 publications

Alignment in multimodal interaction: An integrative framework

Alignment in multimodal interaction: An integrative framework

Natural Leaders: Some interlocutors elicit greater convergence across conversations and across characteristics

Limitations of difference-in-difference for measuring convergence

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