Research on language evolution is an established subject area yet permeated by terminological controversies about which topics should be considered pertinent to the field and which not. By consequence, scholars focusing on language evolution struggle in providing precise demarcations of the discipline, where even the very central notions of evolution and language are elusive. We aimed at providing a data-driven characterisation of language evolution as a field of research by relying on quantitative analysis of data drawn from 697 reviews on (...) 255 submissions from the Joint Conference on Language Evolution 2022 (Kanazawa, Japan). Our results delineate a field characterized by a core of main research topics such as iconicity, sign language, multimodality. Despite being explored within the framework of language evolution research, only very recently these topics became popular in linguistics. As a result, language evolution has the potential to emerge as a forefront of linguistic research, bringing innovation to the study of language. We also see the emergence of more recent topics like rhythm, music, and vocal learning. Furthermore, the community identifies cognitive science, primatology, archaeology, palaeoanthropology, and genetics as key areas, encouraging empirical rather than theoretical work. With new themes, models, and methodologies emerging, our results depict an intrinsically multidisciplinary and evolving research field, likely adapting as language itself. (shrink)

MyJay is an open-source robot designed to facilitate play between children with and without physical disabilities. The robot acts as a proxy for children with upper limb challenges, allowing them to participate in physical games with their peers. Our design was inspired by the FIRST Robotics Competition, which involves teleoperating robots to manipulate objects. Taking a user-centred perspective, we consulted therapists and conducted remote interviews with children with disabilities and their guardians at various stages of the design process. We then (...) conducted an in-person feasibility study with 18 typically developing children in a school setting. The study involved children teleoperating the robot to pick up and throw balls into a designated goal, and the interaction was evaluated using the user experience questionnaire and the Robotic Social Attributes Scale. The results of the study show great potential for MyJay to act as a play mediator in various scenarios, and the response from the children was positive. The ultimate aim of our research agenda is to pave the way towards creating more inclusive play environments through robot-mediated interactions, breaking barriers posed by physical limitations. (shrink)

Backchannel choices affect conversational development. Some backchannels invite interlocutors to continue to the next part of what they are saying and others invite them to elaborate on what they have just said. We tested how communicative modality (audiovisual, audio, text), environmental setting (wholly in-lab, partially in the wild), and conversational goals (on-task, off-task) influenced backchannel usage by participants. We found that backchannel production depends on modality, setting, and goals. For example, we found that specific backchannels played a more prominent role (...) in self-motivated dialogue. Knowledge of how backchannels are used has both theoretical and practical implications. (shrink)

Research on interaction in speaking assessment suggests that both verbal and nonverbal interaction are integral parts of the construct of interactional competence (Galaczi & Taylor, 2018; Plough et al., 2018; Young, 2011). However, little has been done to investigate which features significantly contribute to interactional competence scores. This study, therefore, examined which interaction features that raters noticed in individual scripted interview and paired discussion tasks to gain an insight into the interactional competence construct, providing validity evidence for an inclusion of (...) interactional competence in speaking assessment. Sixty-eight student performances were rated based on interaction rating scales. Exploratory factor analysis revealed four factors: nonverbal communication, topic management, interactional management, and interactive listening. Logistic regressions showed that while raters attended to more topic management features in the individual scripted interview task, they noticed more interactional management features in the paired discussion task. Simple regressions showed that nonverbal communication and topic management features predicted interactional competence scores in the individual scripted interview task, whereas nonverbal communication, topic management, interactional management, and interactive listening features were predictors of scores in the paired discussion task. The findings suggest that both nonverbal and verbal interaction features are important in the interactional competence construct with the paired task providing test-takers with more opportunities to demonstrate their interactional ability. (shrink)