Close-recurrence analysis: a mixed-methods tool for studying joint attention in mobile interaction
Year: 2026
Type: article
Abstract: A foundational aspect of social interaction is the phenomenon of joint attention, typically defined as a shared focus between two or more interlocutors on a particular subject, object or event. While joint attention research in the fields of Cognitive Linguistics and psycholinguistics has predominantly relied on task-based interactions in static environments, studies in the tradition of Interactional Linguistics and Conversation Analysis have focused on the sequential-interactional organization of joint attention in unrestrained interaction but have often been limited to smaller datasets. Moreover, existing work has mainly adopted either quantitative methods (in cognitively oriented research) or qualitative approaches (in interactionally oriented studies), whereas mixed-methods analyses of joint attention remain scarce. To address these methodological research gaps, this study draws on a substantial eye-tracking dataset of naturally occurring interactions in a mobile setting (i.c. joint museum visits) and adopts a mixed-methods approach. On the one hand, we apply an established quantitative method for examining gaze behavior in controlled environments, namely cross-recurrence quantification analysis, to the novel context of mobile interactions. On the other hand, we integrate this quantitative perspective with a multimodal conversation analysis to explore the advantages and limitations of combining methods in joint attention research. Specifically, we also propose the idea of a close-recurrence analysis as a visualization technique that repurposes the central logic of cross-recurrence quantification for qualitative analytic work.
Source: Frontiers in Communication
Institution: KU Leuven
Language: English
Subfield: Language and Linguistics
Field: Arts and Humanities
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $1,418
Funder: Fonds Wetenschappelijk Onderzoek
OpenAlex ID: W7162775434
