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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.


Centroids and mean networks from the ENA that used gaze data from each phase that was shifted by the optimal lag for that phase. The data is modeled from the perspective of the instructor. Four nodes represent the possible gaze targets for the instructor as before, but there are only two nodes for the worker, signifying whether the worker is looking at the same target or a different target. W_Different and W_Same are largely vertically separated. Networks that are low on the y-axis have strong connections to W_Same, while networks high on the axis have strong connections to W_Different. Thus, the y-axis can be interpreted as signifying “alignment,” and we can observe a rise and fall of alignment in the phases as their corresponding networks fall and rise respectively in the ENA space.
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Figure 5: Centroids and mean networks from the ENA that used gaze data from each phase that was shifted by the optimal lag for that phase. The data is modeled from the perspective of the instructor. Four nodes represent the possible gaze targets for the instructor as before, but there are only two nodes for the worker, signifying whether the worker is looking at the same target or a different target. W_Different and W_Same are largely vertically separated. Networks that are low on the y-axis have strong connections to W_Same, while networks high on the axis have strong connections to W_Different. Thus, the y-axis can be interpreted as signifying “alignment,” and we can observe a rise and fall of alignment in the phases as their corresponding networks fall and rise respectively in the ENA space.

Mentions: We next shifted the gaze streams in each phase of the reference-action sequence by that phase's optimal time lag (Table 3) and conducted an analysis in ENA by modeling from the instructor's perspective (Figure 5). Four nodes represent the possible gaze targets for the instructor as before, but there are only two nodes for the worker: W.Same, signifying whether the worker is looking at the same target as the instructor, and W.Different, indicating a different target than the instructor.


Look together: analyzing gaze coordination with epistemic network analysis.

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

Centroids and mean networks from the ENA that used gaze data from each phase that was shifted by the optimal lag for that phase. The data is modeled from the perspective of the instructor. Four nodes represent the possible gaze targets for the instructor as before, but there are only two nodes for the worker, signifying whether the worker is looking at the same target or a different target. W_Different and W_Same are largely vertically separated. Networks that are low on the y-axis have strong connections to W_Same, while networks high on the axis have strong connections to W_Different. Thus, the y-axis can be interpreted as signifying “alignment,” and we can observe a rise and fall of alignment in the phases as their corresponding networks fall and rise respectively in the ENA space.
© Copyright Policy
Related In: Results  -  Collection

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

Figure 5: Centroids and mean networks from the ENA that used gaze data from each phase that was shifted by the optimal lag for that phase. The data is modeled from the perspective of the instructor. Four nodes represent the possible gaze targets for the instructor as before, but there are only two nodes for the worker, signifying whether the worker is looking at the same target or a different target. W_Different and W_Same are largely vertically separated. Networks that are low on the y-axis have strong connections to W_Same, while networks high on the axis have strong connections to W_Different. Thus, the y-axis can be interpreted as signifying “alignment,” and we can observe a rise and fall of alignment in the phases as their corresponding networks fall and rise respectively in the ENA space.
Mentions: We next shifted the gaze streams in each phase of the reference-action sequence by that phase's optimal time lag (Table 3) and conducted an analysis in ENA by modeling from the instructor's perspective (Figure 5). Four nodes represent the possible gaze targets for the instructor as before, but there are only two nodes for the worker: W.Same, signifying whether the worker is looking at the same target as the instructor, and W.Different, indicating a different target than the instructor.

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.