Abstract This paper challenges the common belief that first language (L1) speakers simplify their language when communicating with second language (L2) users, which is captured in Charles Ferguson’s ‘Foreigner Talk’ hypothesis. Academic research has long suggested that, along with simplified vocabulary and syntax, L1 speakers use more illustrative and larger gestures to accommodate L2 addressees. Since this stereotype remains empirically unverified, we analyzed L1 gesture production in two video corpora, implementing automated motion-tracking techniques to measure gesture size. We found that L1 speakers produced larger gestures when describing a picture to an L2 addressee than to an L1 addressee, whereas this difference did not occur during free conversation. In both communicative tasks, however, they used more deictic gestures and organized their gesture space to structure the interaction. In sum, gesture qualifies as a versatile resource in L1–L2 interaction, which is tailored to the conversational task at hand.

Abstract Previous studies have shown that students’ gestures can support their learning, yet little is known about what might prompt students to use gestures while learning in mathematics classrooms. To address this, we observed the same fraction lesson, taught by 8 different teachers, in which we identified students’ mathematics-related gestures and traced back to how and who prompted the gestures. We also searched for all instances of these prompts within a 4-minute window, to assess the probability that those prompts led to student gestures. Logistic regressions indicated that students were likely to engage in gesturing when they spontaneously offered elaborations (without prompting), and after teachers’ higher-order prompts. We also found that students produced more concatenated gestures after teachers provided higher-order prompts (requesting strategies or rationale) than factual prompts (requesting facts or results of mathematical problems). This study provides new insights about the conditions that elicit students’ embodied mathematical thinking.

Abstract This chapter provides a brief conclusion to the book. In drawing together the threads from across the chapters, we can see how there are still many big, open questions in the field of Gesture Studies. Expanding the range of languages and contexts in which gesture is studied will help answer these questions. The interdisciplinary nature of Gesture Studies means that the field is well-placed to grow in exciting new directions as new scholars contribute new ideas. I hope that you will be able to use the insights from the work of Gesture Studies scholars, and now see the richness of multimodal human communication.

One striking commonality between languages is their Zipfian distributions: A power-law distribution of word frequency. This distribution is found across languages, speech genres, and within different parts of speech. The recurrence of such distributions is thought to reflect cognitive and/or communicative pressures and to facilitate language learning. However, research on Zipfian distributions has mostly been limited to spoken languages. In this study, we ask whether Zipfian distributions are also found across signed languages, as expected if they reflect a universal property of human language. We find that sign frequencies and ranks in three sign language corpora (BSL, DGS and NGT) show a Zipfian relationship, similar to that found in spoken languages. These findings highlight the commonalities between spoken and signed languages, add to our understanding of the use of signs, and show the prevalence of Zipfian distributions across language modalities, supporting the idea that they facilitate language learning and communication.

As of Gesture volume 13 (2013), the membership application form can be obtained from the International Society for Gesture Studies directly. To become a member, please visit the website and fill in the form on the website www.gesturestudies.com/join.php Membership is a two-year membership. The membership includes access to all three online issues of the journal Gesture for the year in which you became a member and the following year. Student members can choose whether or not they subscribe to the journal.

Abstract Teachers often use gestures to connect representations of mathematical ideas. This research examined (1) whether such linking gestures help students understand connections among representations and (2) whether sets of gestures that include repeated handshapes and motions – termed gestural catchments – are particularly beneficial. Undergraduates viewed one of four video lessons connecting two representations of multiplication. In the control lesson, the instructor produced beat gestures that did not link the representations. In the link-only lesson, the instructor used gestures to link representations, but the gestures did not form a catchment. In the consistent-catchment lesson, the instructor highlighted corresponding elements of the two representations using identical gestures. In the inconsistent-catchment lesson, the instructor highlighted non-corresponding elements of the two representations using identical gestures. Participants who saw the lesson with the consistent catchment – which highlighted similarities between representations – were most likely to understand the novel representation and to report learning from the lesson.

Abstract This study presents an automatic tool that allows to trace smile intensities along a video record of conversational face-to-face interactions. The processed output proposes a sequence of adjusted time intervals labeled following theSmiling Intensity Scale(Gironzetti, Attardo, and Pickering, 2016), a 5 levels scale varying from neutral facial expression to laughing smile. The underlying statistical model of this tool is trained on a manually annotated corpus of conversations featuring spontaneous facial expressions. This model will be detailed in this study. This tool can be used with benefits for annotating smile in interactions. The results are twofold. First, the evaluation reveals an observed agreement of 68% between manual and automatic annotations. Second, manually correcting the labels and interval boundaries of the automatic outputs reduces by a factor 10 the annotation time as compared with the time spent for manually annotating smile intensities without pretreatment. Our annotation engine makes use of the state-of-the-art toolbox OpenFace for tracking the face and for measuring the intensities of the facial Action Units of interest all along the video. The documentation and the scripts of our tool, the SMAD software, are available to download at the HMAD open source project URL pagehttps://github.com/srauzy/HMAD(last access 31 July 2023).

Abstract It is well known that signers and speakers routinely produce finger points during interaction. While the referential functions of such finger points have received great attention from researchers, they are also used to manage interactions between interlocutors. These functions are less understood and have received less research focus. The current study helps to redress this gap in the literature by investigating how finger pointing is used to index and coordinate turn-beginnings in a corpus of 11 semi-naturalistic (Norwegian) signed language conversations, involving between two to five signers (3.4 hours of signing). The data was initially annotated in ELAN and then further qualitative analysis was conducted. This investigation revealed that finger pointing effectively indexes previous and upcoming discourse, thereby binding sequences of conversational moves and guiding their trajectory, helping signers to coordinate turn transitions and interaction as it unfolds.