Abstract
In this paper, we present an embodiment perspective on viewpoint by exploring the role of eye gaze in face-to-face conversation, in relation to and interaction with other expressive modalities. More specifically, we look into gaze patterns, as well as gaze synchronization with speech, as instruments in the negotiation of participant roles in interaction. In order to obtain fine-grained information on the different modalities under scrutiny, we used the InSight Interaction Corpus (Brône, Geert & Bert Oben. 2015. Insight Interaction: A multimodal and multifocal dialogue corpus. Language Resources and Evaluation 49, 195–214.). This multimodal video corpus consists of two- and three-party interactions (in Dutch), with head-mounted scene cameras and eye-trackers tracking all participants’ visual behavior, providing a unique ‘speaker-internal’ perspective on the conversation. The analysis of interactional sequences from the corpus (dyads and triads) reveals specific patterns of gaze distribution related to the temporal organization of viewpoint in dialogue. Different dialogue acts typically display specific gaze events at crucial points in time, as, e.g., in the case of brief gaze aversion associated with turn-holding, and shared gaze between interlocutors at the critical point of turn-taking. In addition, the data show a strong correlation and temporal synchronization between eye gaze and speech in the realization of specific dialogue acts, as shown by means of a series of cross-recurrence analyses for specific turn-holding mechanisms (e.g., verbal fillers co-occurring with brief moments of gaze aversion).
Acknowledgments
The authors would like to thank the two anonymous reviewers for their valuable comments, and especially also Lieven Vandelanotte for his numerous suggestions and corrections that greatly helped to improve the manuscript. Parts of this work were presented at various conferences and workshops over the last three years, and we are grateful for the many discussions that have helped to shape the argumentation in this paper.
Appendix A
A cross-recurrence analysis is a type of correlation analysis that looks for a time lag at which the overlap between two time-series is maximal. Consider the following fictive example of a 10 second interaction between two speakers (S1 and S2). The interaction between them is sampled at 1 Hz, i.e., there is one value per second. Say, in this fictive example we are looking at blinking. Red indicates a speaker has blinked during that second, green indicates there was no blink during the sample second. In the example below there is one moment of overlap: at second seven both speakers are blinking at the same time (indicated by the blue rectangle in Figure 4). At this point in time they are perfectly synchronizing their blinking.
Fictive example of two time-series for blinking.
To study the temporal relation between the blinking of S1 and S2, it is not only interesting to measure when both speakers are blinking at the same time, it is also relevant to check whether there is a systematic time lag between the blinking of S1 and that of S2. This is exactly what a cross-recurrence analysis does. When shifting the time-series of S2 one second down (see Figure 5), we see there is a lot more overlap between the two time-series (cf. the four blue rectangles in Figure 5). What this means is that, for this tiny example, typically S2 blinks first and S1 blinks one second later. In other words, the correlation between the two time-series is maximal at a time lag of 1 second.
Fictive example of two time-series for blinking in which the time-series for S2 is lagged by one second to that of S1.
It is of course possible to calculate the correlation between two time series for any time lag: by shifting the time-series for S2 another second down (and another and another, etc.) and by also shifting the time-series of S1 down (or that of S2 up, which is the same). In Figure 6 we see a plot in which this calculation has been done for the fictive example above. In the middle of the plot the value on the X-axis is zero (t0). This data point indicates the amount of correlation between the actual time-series, i.e., without any lagging (cf. the visual representation in Figure 4). For the value 1 on the X-axis, there is a clear peak: when lagging the time-series of S2 with one second, there is a lot more overlap (cf. the visual representation in Figure 5). When, switching speaker direction, lagging the time-series of S1 with one second (this corresponds to the value ‒1 on the X-axis), we do not see an increase in overlap. Basically two things can be read from this type of plot. First, the peak in the plot indicates at what time lag the correlation or overlap between two time-series is maximal. Second, the position of the peak relative to t0 indicates who follows whom: to the right of t0 we measure the correlation or overlap for S1 following S2 (as in Figure 5); to the left we see S2 following S1. For the fictive example here we can read from the plot in Figure 6 that S1 typically follows S2 (and not the other way around) at a time lag of one second. In other words, typically one second after a blink by S2, S1 also blinks.
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