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Look together: analyzing gaze coordination with epistemic network analysis.

Andrist S, Collier W, Gleicher M, Mutlu B, Shaffer D - Front Psychol (2015)

Bottom Line: In this analysis, network nodes represent gaze targets for each participant, and edge strengths convey the likelihood of simultaneous gaze to the connected target nodes during a given time-slice.We divided collaborative task sequences into discrete phases to examine how the networks of shared gaze evolved over longer time windows.In addition to contributing to the growing body of knowledge on the coordination of gaze behaviors in joint activities, this work has implications for the design of future technologies that engage in situated interactions with human users.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Sciences, University of Wisconsin-Madison Madison, WI, USA.

ABSTRACT
When conversing and collaborating in everyday situations, people naturally and interactively align their behaviors with each other across various communication channels, including speech, gesture, posture, and gaze. Having access to a partner's referential gaze behavior has been shown to be particularly important in achieving collaborative outcomes, but the process in which people's gaze behaviors unfold over the course of an interaction and become tightly coordinated is not well understood. In this paper, we present work to develop a deeper and more nuanced understanding of coordinated referential gaze in collaborating dyads. We recruited 13 dyads to participate in a collaborative sandwich-making task and used dual mobile eye tracking to synchronously record each participant's gaze behavior. We used a relatively new analysis technique-epistemic network analysis-to jointly model the gaze behaviors of both conversational participants. In this analysis, network nodes represent gaze targets for each participant, and edge strengths convey the likelihood of simultaneous gaze to the connected target nodes during a given time-slice. We divided collaborative task sequences into discrete phases to examine how the networks of shared gaze evolved over longer time windows. We conducted three separate analyses of the data to reveal (1) properties and patterns of how gaze coordination unfolds throughout an interaction sequence, (2) optimal time lags of gaze alignment within a dyad at different phases of the interaction, and (3) differences in gaze coordination patterns for interaction sequences that lead to breakdowns and repairs. In addition to contributing to the growing body of knowledge on the coordination of gaze behaviors in joint activities, this work has implications for the design of future technologies that engage in situated interactions with human users.

No MeSH data available.


Cross-recurrence plots adapted from work by Richardson and Dale (2005). Horizontal and vertical axes specify the gaze of a speaker and a listener. Diagonal slices (lower-left to upper-right) correspond to an alignment of the participants' gaze with a particular time lag between them. A point is plotted on the diagonal whenever the gaze is recurrent. These plots visually compare a “good” listener (well aligned with the speaker's gaze) to a “bad” listener (not as well aligned). They also show the poor alignment of random gaze with a speaker's gaze.
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Figure 1: Cross-recurrence plots adapted from work by Richardson and Dale (2005). Horizontal and vertical axes specify the gaze of a speaker and a listener. Diagonal slices (lower-left to upper-right) correspond to an alignment of the participants' gaze with a particular time lag between them. A point is plotted on the diagonal whenever the gaze is recurrent. These plots visually compare a “good” listener (well aligned with the speaker's gaze) to a “bad” listener (not as well aligned). They also show the poor alignment of random gaze with a speaker's gaze.

Mentions: Cross-recurrence analysis is a commonly used technique for analyzing gaze data captured from participant dyads, as it permits the visualization and quantification of recurrent patterns of states between two time series, such as the gaze patterns of two conversational participants (Zbilut et al., 1998) (Figure 1). This analysis approach can reveal the temporal dynamics of a dataset without making assumptions about its statistical nature. The horizontal and vertical axes of a cross-recurrence plot specify the gaze of each of the two partners in interaction. Each diagonal on the plot (lower-left to upper-right) corresponds to an alignment of the participants' gaze with a particular time lag between them. A point is plotted on the diagonal whenever the gaze is recurrent—their eyes are fixating at the same object at the given time. The longest diagonal, from bottom-left to top-right of the plot, represents the gaze alignment at a lag of 0. Diagonals above and below that line represent alignments with positive and negative offsets, shifting one of the participants' time-series gaze data in relation to the other participant.


Look together: analyzing gaze coordination with epistemic network analysis.

Andrist S, Collier W, Gleicher M, Mutlu B, Shaffer D - Front Psychol (2015)

Cross-recurrence plots adapted from work by Richardson and Dale (2005). Horizontal and vertical axes specify the gaze of a speaker and a listener. Diagonal slices (lower-left to upper-right) correspond to an alignment of the participants' gaze with a particular time lag between them. A point is plotted on the diagonal whenever the gaze is recurrent. These plots visually compare a “good” listener (well aligned with the speaker's gaze) to a “bad” listener (not as well aligned). They also show the poor alignment of random gaze with a speaker's gaze.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4508484&req=5

Figure 1: Cross-recurrence plots adapted from work by Richardson and Dale (2005). Horizontal and vertical axes specify the gaze of a speaker and a listener. Diagonal slices (lower-left to upper-right) correspond to an alignment of the participants' gaze with a particular time lag between them. A point is plotted on the diagonal whenever the gaze is recurrent. These plots visually compare a “good” listener (well aligned with the speaker's gaze) to a “bad” listener (not as well aligned). They also show the poor alignment of random gaze with a speaker's gaze.
Mentions: Cross-recurrence analysis is a commonly used technique for analyzing gaze data captured from participant dyads, as it permits the visualization and quantification of recurrent patterns of states between two time series, such as the gaze patterns of two conversational participants (Zbilut et al., 1998) (Figure 1). This analysis approach can reveal the temporal dynamics of a dataset without making assumptions about its statistical nature. The horizontal and vertical axes of a cross-recurrence plot specify the gaze of each of the two partners in interaction. Each diagonal on the plot (lower-left to upper-right) corresponds to an alignment of the participants' gaze with a particular time lag between them. A point is plotted on the diagonal whenever the gaze is recurrent—their eyes are fixating at the same object at the given time. The longest diagonal, from bottom-left to top-right of the plot, represents the gaze alignment at a lag of 0. Diagonals above and below that line represent alignments with positive and negative offsets, shifting one of the participants' time-series gaze data in relation to the other participant.

Bottom Line: In this analysis, network nodes represent gaze targets for each participant, and edge strengths convey the likelihood of simultaneous gaze to the connected target nodes during a given time-slice.We divided collaborative task sequences into discrete phases to examine how the networks of shared gaze evolved over longer time windows.In addition to contributing to the growing body of knowledge on the coordination of gaze behaviors in joint activities, this work has implications for the design of future technologies that engage in situated interactions with human users.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Sciences, University of Wisconsin-Madison Madison, WI, USA.

ABSTRACT
When conversing and collaborating in everyday situations, people naturally and interactively align their behaviors with each other across various communication channels, including speech, gesture, posture, and gaze. Having access to a partner's referential gaze behavior has been shown to be particularly important in achieving collaborative outcomes, but the process in which people's gaze behaviors unfold over the course of an interaction and become tightly coordinated is not well understood. In this paper, we present work to develop a deeper and more nuanced understanding of coordinated referential gaze in collaborating dyads. We recruited 13 dyads to participate in a collaborative sandwich-making task and used dual mobile eye tracking to synchronously record each participant's gaze behavior. We used a relatively new analysis technique-epistemic network analysis-to jointly model the gaze behaviors of both conversational participants. In this analysis, network nodes represent gaze targets for each participant, and edge strengths convey the likelihood of simultaneous gaze to the connected target nodes during a given time-slice. We divided collaborative task sequences into discrete phases to examine how the networks of shared gaze evolved over longer time windows. We conducted three separate analyses of the data to reveal (1) properties and patterns of how gaze coordination unfolds throughout an interaction sequence, (2) optimal time lags of gaze alignment within a dyad at different phases of the interaction, and (3) differences in gaze coordination patterns for interaction sequences that lead to breakdowns and repairs. In addition to contributing to the growing body of knowledge on the coordination of gaze behaviors in joint activities, this work has implications for the design of future technologies that engage in situated interactions with human users.

No MeSH data available.