Publications

Abstract

Conversation requires a substantial amount of coordination between dialogue participants, from managing turn taking to negotiating mutual understanding. Part of this coordination effort surfaces as the reuse of linguistic behaviour across speakers, a process often referred to as alignment. While the presence of linguistic alignment is well documented in the literature, several questions remain open, including the extent to which patterns of reuse across speakers have an impact on the emergence of labelling conventions for novel referents. In this study, we put forward a methodology for automatically detecting shared lemmatised constructions---expressions with a common lexical core used by both speakers within a dialogue---and apply it to a referential communication corpus where participants aim to identify novel objects for which no established labels exist. Our analyses uncover the usage patterns of shared constructions in interaction and reveal that features such as their frequency and the amount of different constructions used for a referent are associated with the degree of object labelling convergence the participants exhibit after social interaction. More generally, the present study shows that automatically detected shared constructions offer a useful level of analysis to investigate the dynamics of reference negotiation in dialogue.

Abstract

Gestures are integral components of face-to-face communication. They unfold over time, often following predictable movement phases of preparation, stroke, and re-
traction. Yet, the prevalent approach to automatic gesture detection treats the problem as binary classification, classifying a segment as either containing a gesture or not, thus failing to capture its inherently sequential and contextual nature. To address this, we introduce a novel framework that reframes the task as a multi-phase sequence labeling problem rather than binary classification. Our model processes sequences of skeletal movements over time windows, uses Transformer encoders to learn contextual embeddings, and leverages Conditional Random Fields to perform sequence labeling. We evaluate our proposal on a large dataset of diverse co-speech gestures in task-oriented face-to-face dialogues. The results consistently demonstrate that our method significantly outperforms strong baseline models in detecting gesture strokes. Furthermore, applying Transformer encoders to learn contextual embeddings from movement sequences substantially improves gesture unit detection. These results highlight our framework’s capacity to capture the fine-grained dynamics of co-speech gesture phases, paving the way for more nuanced and accurate gesture detection and analysis.

Abstract

In face-to-face dialogues, the form-meaning relationship of co-speech gestures varies depending on contextual factors such as what the gestures refer to and the individual characteristics of speakers. These factors make co-speech gesture representation learning challenging. How can we learn meaningful gestures representations considering gestures’ variability and relationship with speech? This paper tackles this challenge by employing self-supervised contrastive learning techniques to learn gesture representations from skeletal and speech information. We propose an approach that includes both unimodal and multimodal pre-training to ground gesture representations in co-occurring speech. For training, we utilize a face-to-face dialogue dataset rich with representational iconic gestures. We conduct thorough intrinsic evaluations of the learned representations through comparison with human-annotated pairwise gesture similarity. Moreover, we perform a diagnostic probing analysis to assess the possibility of recovering interpretable gesture features from the learned representations. Our results show a significant positive correlation with human-annotated gesture similarity and reveal that the similarity between the learned representations is consistent with well-motivated patterns related to the dynamics of dialogue interaction. Moreover, our findings demonstrate that several features concerning the form of gestures can be recovered from the latent representations. Overall, this study shows that multimodal contrastive learning is a promising approach for learning gesture representations, which opens the door to using such representations in larger-scale gesture analysis studies.

Abstract

Purpose

To perform a scoping review to investigate the psychosocial impact of having an altered facial expression in five neurological diseases.
Methods

A systematic literature search was performed. Studies were on Bell’s palsy, facioscapulohumeral muscular dystrophy (FSHD), Moebius syndrome, myotonic dystrophy type 1, or Parkinson’s disease patients; had a focus on altered facial expression; and had any form of psychosocial outcome measure. Data extraction focused on psychosocial outcomes.
Results

Bell’s palsy, myotonic dystrophy type 1, and Parkinson’s disease patients more often experienced some degree of psychosocial distress than healthy controls. In FSHD, facial weakness negatively influenced communication and was experienced as a burden. The psychosocial distress applied especially to women (Bell’s palsy and Parkinson’s disease), and patients with more severely altered facial expression (Bell’s palsy), but not for Moebius syndrome patients. Furthermore, Parkinson’s disease patients with more pronounced hypomimia were perceived more negatively by observers. Various strategies were reported to compensate for altered facial expression.
Conclusions

This review showed that patients with altered facial expression in four of five included neurological diseases had reduced psychosocial functioning. Future research recommendations include studies on observers’ judgements of patients during social interactions and on the effectiveness of compensation strategies in enhancing psychosocial functioning.
Implications for rehabilitation

Negative effects of altered facial expression on psychosocial functioning are common and more abundant in women and in more severely affected patients with various neurological disorders.

Health care professionals should be alert to psychosocial distress in patients with altered facial expression.

Learning of compensatory strategies could be a beneficial therapy for patients with psychosocial distress due to an altered facial expression.

Abstract

During face-to-face conversation, transitions between speaker turns are incredibly fast. These fast turn exchanges seem to involve next speakers predicting upcoming semantic information, such that next turn planning can begin before a current turn is complete. Given that face-to-face conversation also involves the use of communicative bodily signals, an important question is how bodily signals such as co-speech hand gestures play into these processes of prediction and fast responding. In this corpus study, we found that hand gestures that depict or refer to semantic information started before the corresponding information in speech, which held both for the onset of the gesture as a whole, as well as the onset of the stroke (the most meaningful part of the gesture). This early timing potentially allows listeners to use the gestural information to predict the corresponding semantic information to be conveyed in speech. Moreover, we provided further evidence that questions with gestures got faster responses than questions without gestures. However, we found no evidence for the idea that how much a gesture precedes its lexical affiliate (i.e., its predictive potential) relates to how fast responses were given. The findings presented here highlight the importance of the temporal relation between speech and gesture and help to illuminate the potential mechanisms underpinning multimodal language processing during face-to-face conversation.

Abstract

Most language use occurs in face-to-face conversation, which involves rapid turn-taking. Seeing communicative bodily signals in addition to hearing speech may facilitate such fast responding. We tested whether this holds for co-speech hand gestures by investigating whether these gestures speed up button press responses to questions. Sixty native speakers of Dutch viewed videos in which an actress asked yes/no-questions, either with or without a corresponding iconic hand gesture. Participants answered the questions as quickly and accurately as possible via button press. Gestures did not impact response accuracy, but crucially, gestures sped up responses, suggesting that response planning may be finished earlier when gestures are seen. How much gestures sped up responses was not related to their timing in the question or their timing with respect to the corresponding information in speech. Overall, these results are in line with the idea that multimodality may facilitate fast responding during face-to-face conversation.

Abstract

The typical pattern of alternating turns in conversation seems trivial at first sight. But a closer look quickly reveals the cognitive challenges involved, with much of it resulting from the fast-paced nature of conversation. One core ingredient to turn coordination is the anticipation of upcoming turn ends so as to be able to ready oneself for providing the next contribution. Across two experiments, we investigated two variables inherent to face-to-face conversation, the presence of visual bodily signals and preceding discourse context, in terms of their contribution to turn end anticipation. In a reaction time paradigm, participants anticipated conversational turn ends better when seeing the speaker and their visual bodily signals than when they did not, especially so for longer turns. Likewise, participants were better able to anticipate turn ends when they had access to the preceding discourse context than when they did not, and especially so for longer turns. Critically, the two variables did not interact, showing that visual bodily signals retain their influence even in the context of preceding discourse. In a pre-registered follow-up experiment, we manipulated the visibility of the speaker's head, eyes and upper body (i.e. torso + arms). Participants were better able to anticipate turn ends when the speaker's upper body was visible, suggesting a role for manual gestures in turn end anticipation. Together, these findings show that seeing the speaker during conversation may critically facilitate turn coordination in interaction.

Abstract

Human language is extremely versatile, combining a limited set of signals in an unlimited number of ways. However, it is unknown whether conversational visual signals feed into the composite utterances with which speakers communicate their intentions. We assessed whether different combinations of visual signals lead to different intent interpretations of the same spoken utterance. Participants viewed a virtual avatar uttering spoken questions while producing single visual signals (i.e., head turn, head tilt, eyebrow raise) or combinations of these signals. After each video, participants classified the communicative intention behind the question. We found that composite utterances combining several visual signals conveyed different meaning compared to utterances accompanied by the single visual signals. However, responses to combinations of signals were more similar to the responses to related, rather than unrelated, individual signals, indicating a consistent influence of the individual visual signals on the whole. This study therefore provides first evidence for compositional, non-additive (i.e., Gestalt-like) perception of multimodal language.

Abstract

The natural ecology of language is conversation, with individuals taking turns speaking to communicate in a back-and-forth fashion. Language in this context involves strings of words that a listener must process while simultaneously planning their own next utterance. It would thus be highly advantageous if language users distributed information within an utterance in a way that may facilitate this processing–planning dynamic. While some studies have investigated how information is distributed at the level of single words or clauses, or in written language, little is known about how information is distributed within spoken utterances produced during naturalistic conversation. It also is not known how information distribution patterns of spoken utterances may differ across languages. We used a set of matched corpora (CallHome) containing 898 telephone conversations conducted in six different languages (Arabic, English, German, Japanese, Mandarin, and Spanish), analyzing more than 58,000 utterances, to assess whether there is evidence of distinct patterns of information distributions at the utterance level, and whether these patterns are similar or differed across the languages. We found that English, Spanish, and Mandarin typically show a back-loaded distribution, with higher information (i.e., surprisal) in the last half of utterances compared with the first half, while Arabic, German, and Japanese showed front-loaded distributions, with higher information in the first half compared with the last half. Additional analyses suggest that these patterns may be related to word order and rate of noun and verb usage. We additionally found that back-loaded languages have longer turn transition times (i.e.,time between speaker turns)

Abstract

During face-to-face communication, recipients need to rapidly integrate a plethora of auditory and visual signals. This integration of signals from many different bodily articulators, all offset in time, with the information in the speech stream may either tax the cognitive system, thus slowing down language processing, or may result in multimodal facilitation. Using the classical shadowing paradigm, participants shadowed speech from face-to-face, naturalistic dyadic conversations in an audiovisual context, an audiovisual context without visual speech (e.g., lips), and an audio-only context. Our results provide evidence of a multimodal facilitation effect in human communication: participants were faster in shadowing words when seeing multimodal messages compared with when hearing only audio. Also, the more visual context was present, the fewer shadowing errors were made, and the earlier in time participants shadowed predicted lexical items. We propose that the multimodal facilitation effect may contribute to the ease of fast face-to-face conversational interaction.

Abstract

Face to face interaction is fundamental to human sociality but is very complex to study in a scientific fashion. This theme issue brings together cutting-edge approaches to the study of face-to-face interaction and showcases how we can make progress in this area. Researchers are now studying interaction in adult conversation, parent-child relationships, neurodiverse groups, interactions with virtual agents and various animal species. The theme issue reveals how new paradigms are leading to more ecologically grounded and comprehensive insights into what social interaction is. Scientific advances in this area can lead to improvements in education and therapy, better understanding of neurodiversity and more engaging artificial agents

Abstract

Face-to-face interaction is core to human sociality and its evolution, and provides the environment in which most of human communication occurs. Research into the full complexities that define face-to-face interaction requires a multi-disciplinary, multi-level approach, illuminating from different perspectives how we and other species interact. This special issue showcases a wide range of approaches, bringing together detailed studies of naturalistic social-interactional behaviour with larger scale analyses for generalization, and investigations of socially contextualized cognitive and neural processes that underpin the behaviour we observe. We suggest that this integrative approach will allow us to propel forwards the science of face-to-face interaction by leading us to new paradigms and novel, more ecologically grounded and comprehensive insights into how we interact with one another and with artificial agents, how differences in psychological profiles might affect interaction, and how the capacity to socially interact develops and has evolved in the human and other species. This theme issue makes a first step into this direction, with the aim to break down disciplinary boundaries and emphasizing the value of illuminating the many facets of face-to-face interaction.

Abstract

In face-to-face discourse, listeners exploit cues in the input to generate predictions about upcoming words. Moreover, in addition to speech, speakers produce a multitude of visual signals, such as iconic gestures, which listeners readily integrate with incoming words. Previous studies have shown that processing of target words is facilitated when these are embedded in predictable compared to non-predictable discourses and when accompanied by iconic compared to meaningless gestures. In the present study, we investigated the interaction of both factors. We recorded electroencephalogram from 60 Dutch adults while they were watching videos of an actress producing short discourses. The stimuli consisted of an introductory and a target sentence; the latter contained a target noun. Depending on the preceding discourse, the target noun was either predictable or not. Each target noun was paired with an iconic gesture and a gesture that did not convey meaning. In both conditions, gesture presentation in the video was timed such that the gesture stroke slightly preceded the onset of the spoken target by 130 ms. Our ERP analyses revealed independent facilitatory effects for predictable discourses and iconic gestures. However, the interactive effect of both factors demonstrated that target processing (i.e., gesture-speech integration) was facilitated most when targets were part of predictable discourses and accompanied by an iconic gesture. Our results thus suggest a strong intertwinement of linguistic predictability and non-verbal gesture processing where listeners exploit predictive discourse cues to pre-activate verbal and non-verbal representations of upcoming target words.

Abstract

Human communicative interaction is characterized by rapid and precise turn-taking. This is achieved by an intricate system that has been elucidated in the field of conversation analysis, based largely on the study of the auditory signal. This model suggests that transitions occur at points of possible completion identified in terms of linguistic units. Despite this, considerable evidence exists that visible bodily actions including gaze and gestures also play a role. To reconcile disparate models and observations in the literature, we combine qualitative and quantitative methods to analyse turn-taking in a corpus of multimodal interaction using eye-trackers and multiple cameras. We show that transitions seem to be inhibited when a speaker averts their gaze at a point of possible turn completion, or when a speaker produces gestures which are beginning or unfinished at such points. We further show that while the direction of a speaker's gaze does not affect the speed of transitions, the production of manual gestures does: turns with gestures have faster transitions. Our findings suggest that the coordination of transitions involves not only linguistic resources but also visual gestural ones and that the transition-relevance places in turns are multimodal in nature.

Abstract

We present a protocol to study naturalistic human communication using dual-EEG and audio-visual recordings. We describe preparatory steps for data collection including setup preparation, experiment design, and piloting. We then describe the data collection process in detail which consists of participant recruitment, experiment room preparation, and data collection. We also outline the kinds of research questions that can be addressed with the current protocol, including several analysis possibilities, from conversational to advanced time-frequency analyses.
For complete details on the use and execution of this protocol, please refer to Drijvers and Holler (2022).

Abstract

The early recognition of fundamental social actions, like questions, is crucial for understanding the speaker’s intended message and planning a timely response in conversation. Questions themselves may express more than one social action category (e.g., an information request “What time is it?”, an invitation “Will you come to my party?” or a criticism “Are you crazy?”). Although human language use occurs predominantly in a multimodal context, prior research on social actions has mainly focused on the verbal modality. This study breaks new ground by investigating how conversational facial signals may map onto the expression of different types of social actions conveyed through questions. The distribution, timing, and temporal organization of facial signals across social actions was analysed in a rich corpus of naturalistic, dyadic face-to-face Dutch conversations. These social actions were: Information Requests, Understanding Checks, Self-Directed questions, Stance or Sentiment questions, Other-Initiated Repairs, Active Participation questions, questions for Structuring, Initiating or Maintaining Conversation, and Plans and Actions questions. This is the first study to reveal differences in distribution and timing of facial signals across different types of social actions. The findings raise the possibility that facial signals may facilitate social action recognition during language processing in multimodal face-to-face interaction.

Abstract

In conversation, recognizing social actions (similar to ‘speech acts’) early is important to quickly understand the speaker’s intended message and to provide a fast response. Fast turns are typical for fundamental social actions like questions, since a long gap can indicate a dispreferred response. In multimodal face-to-face interaction, visual signals may contribute to this fast dynamic. The face is an important source of visual signalling, and previous research found that prevalent facial signals such as eyebrow movements facilitate the rapid recognition of questions. We aimed to investigate whether early eyebrow movements with natural movement intensities facilitate question identification, and whether specific intensities are more helpful in detecting questions. Participants were instructed to view videos of avatars where the presence of eyebrow movements (eyebrow frown or raise vs. no eyebrow movement) was manipulated, and to indicate whether the utterance in the video was a question or statement. Results showed higher accuracies for questions with eyebrow frowns, and faster response times for questions with eyebrow frowns and eyebrow raises. No additional effect was observed for the specific movement intensity. This suggests that eyebrow movements that are representative of naturalistic multimodal behaviour facilitate question recognition.

Abstract

Conversation is a time-pressured environment. Recognizing a social action (the ‘‘speech act,’’ such as a question requesting information) early is crucial in conversation to quickly understand the intended message and plan a timely response. Fast turns between interlocutors are especially relevant for responses to questions since a long gap may be meaningful by itself. Human language is multimodal, involving speech as well as visual signals from the body, including the face. But little is known about how conversational facial signals contribute to the communication of social actions. Some of the most prominent facial signals in conversation are eyebrow movements. Previous studies found links between eyebrow movements and questions, suggesting that these facial signals could contribute to the rapid recognition of questions. Therefore, we aimed to investigate whether early eyebrow movements (eyebrow frown or raise vs. no eyebrow movement) facilitate question identification. Participants were instructed to view videos of avatars where the presence of eyebrow movements accompanying questions was manipulated. Their task was to indicate whether the utterance was a question or a statement as accurately and quickly as possible. Data were collected using the online testing platform Gorilla. Results showed higher accuracies and faster response times for questions with eyebrow frowns, suggesting a facilitative role of eyebrow frowns for question identification. This means that facial signals can critically contribute to the communication of social actions in conversation by signaling social action-specific visual information and providing visual cues to speakers’ intentions.

Abstract

Natural human interaction requires us to produce and process many different signals, including speech, hand and head gestures, and facial expressions. These communicative signals, which occur in a variety of temporal relations with each other (e.g., parallel or temporally misaligned), must be rapidly processed as a coherent message by the receiver. In this contribution, we introduce the notion of interactionally embedded, affordance-driven gestalt perception as a framework that can explain how this rapid processing of multimodal signals is achieved as efficiently as it is. We discuss empirical evidence showing how basic principles of gestalt perception can explain some aspects of unimodal phenomena such as verbal language processing and visual scene perception but require additional features to explain multimodal human communication. We propose a framework in which high-level gestalt predictions are continuously updated by incoming sensory input, such as unfolding speech and visual signals. We outline the constituent processes that shape high-level gestalt perception and their role in perceiving relevance and prägnanz. Finally, we provide testable predictions that arise from this multimodal interactionally embedded gestalt-perception framework. This review and framework therefore provide a theoretically motivated account of how we may understand the highly complex, multimodal behaviors inherent in natural social interaction.

Abstract

Multiple layers of visual (and vocal) signals, plus their different onsets and offsets, represent a significant semantic and temporal binding problem during face-to-face conversation.
Despite this complex unification process, multimodal messages appear to be processed faster than unimodal messages.

Multimodal gestalt recognition and multilevel prediction are proposed to play a crucial role in facilitating multimodal language processing.

The basis of the processing mechanisms involved in multimodal language comprehension is hypothesized to be domain general, coopted for communication, and refined with domain-specific characteristics.
A new, situated framework for understanding human language processing is called for that takes into consideration the multilayered, multimodal nature of language and its production and comprehension in conversational interaction requiring fast processing.

Abstract

Speakers can adapt their speech and co-speech gestures based on knowledge shared with an addressee (common ground-based recipient design). Here, we investigate whether these adaptations are modulated by the speaker’s age and cognitive abilities. Younger and older participants narrated six short comic stories to a same-aged addressee. Half of each story was known to both participants, the other half only to the speaker. The two age groups did not differ in terms of the number of words and narrative events mentioned per narration, or in terms of gesture frequency, gesture rate, or percentage of events expressed multimodally. However, only the younger participants reduced the amount of verbal and gestural information when narrating mutually known as opposed to novel story content. Age-related differences in cognitive abilities did not predict these differences in common ground-based recipient design. The older participants’ communicative behaviour may therefore also reflect differences in social or pragmatic goals.