scholarly journals A stepping stone to compositionality in chimpanzee communication

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7623 ◽  
Author(s):  
Linda S. Oña ◽  
Wendy Sandler ◽  
Katja Liebal
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Social Contexts ◽  
Human Language ◽  
Affiliative Behavior ◽  
Stepping Stone ◽  
Video Footage ◽  
Compositional Structures ◽  
Group Members ◽  
Compositional System

Compositionality refers to a structural property of human language, according to which the meaning of a complex expression is a function of the meaning of its parts and the way they are combined. Compositionality is a defining characteristic of all human language, spoken and signed. Comparative research into the emergence of human language aims at identifying precursors to such key features of human language in the communication of other primates. While it is known that chimpanzees, our closest relatives, produce a variety of gestures, facial expressions and vocalizations in interactions with their group members, little is known about how these signals combine simultaneously. Therefore, the aim of the current study is to investigate whether there is evidence for compositional structures in the communication of chimpanzees. We investigated two semi-wild groups of chimpanzees, with focus on their manual gestures and their combinations with facial expressions across different social contexts. If there are compositional structures in chimpanzee communication, adding a facial expression to a gesture should convey a different message than the gesture alone, a difference that we expect to be measurable by the recipient’s response. Furthermore, we expect context-dependent usage of these combinations. Based on a form-based coding procedure of the collected video footage, we identified two frequently used manual gestures (stretched arm gesture and bent arm gesture) and two facial expression (bared teeth face and funneled lip face). We analyzed whether the recipients’ response varied depending on the signaler’s usage of a given gesture + face combination and the context in which these were used. Overall, our results suggest that, in positive contexts, such as play or grooming, specific combinations had an impact on the likelihood of the occurrence of particular responses. Specifically, adding a bared teeth face to a gesture either increased the likelihood of affiliative behavior (for stretched arm gesture) or eliminated the bias toward an affiliative response (for bent arm gesture). We show for the first time that the components under study are recombinable, and that different combinations elicit different responses, a property that we refer to as componentiality. Yet our data do not suggest that the components have consistent meanings in each combination—a defining property of compositionality. We propose that the componentiality exhibited in this study represents a necessary stepping stone toward a fully evolved compositional system.

The Facial Expressions of the Brown Capuchin Monkey (Ceb Us Apella)

Behaviour ◽  
1979 ◽  
Vol 68 (3-4) ◽  
pp. 250-276 ◽  
Author(s):  
Ronald M. Weigel
Keyword(s):  
Spatial Distribution ◽  
Facial Expressions ◽  
Cebus Apella ◽  
Social Contexts ◽  
Capuchin Monkey ◽  
Open Mouth ◽  
Food Items ◽  
Group Members

AbstractTwo captive groups of the brown capuchin, Cebus apella, were observed for approximately 146 hours, with particular attention paid to the facial expressions of this species. Seven discrete facial expressions having the properties of ritualized displays were observed. These were classified as follows according to motivational, status indication, and social functional criteria: (I) Open Mouth Bared Teeth-threat, (2) Open Mouth Staccato Beep-probably defensive threat, (3) Bared Teeth Scream-appeasement, maternal solicitation, (4) Grin-probably appeasement and reassurance, (5) Lipsmacking-reassurance, leads to passive contact, (6) Forehead Raise-affinitive, leads to active contact, and (7) Relaxed Open Mouth-play face, possibly play reassurance. An examination of the intention movements and the behaviors temporally associated with the facial expressions strongly suggests the brown capuchins studied here are providing accurate motivational information to other group members. Facial expressions morphologically similar to those observed in capuchins are also given by many species of Ceropithecinae, especially Macaca and Papio, with similar expressions occurring in similar social contexts. The presence of ritualized appeasement gestures in Cebus, Macaca, and Papio may be a function of the diversity of diet and the resulting spatial distribution of food items.

Heritage

2018 ◽  
Author(s):  
Omar Shaikh ◽  
Stefano Bonino
Keyword(s):  
Facial Expressions ◽  
South Asian ◽  
Online Video ◽  
The South ◽  
Men And Women ◽  
The Public ◽  
Video Footage ◽  
Community Heritage ◽  
Personal Accounts ◽  
The Way

The Colourful Heritage Project (CHP) is the first community heritage focused charitable initiative in Scotland aiming to preserve and to celebrate the contributions of early South Asian and Muslim migrants to Scotland. It has successfully collated a considerable number of oral stories to create an online video archive, providing first-hand accounts of the personal journeys and emotions of the arrival of the earliest generation of these migrants in Scotland and highlighting the inspiring lessons that can be learnt from them. The CHP’s aims are first to capture these stories, second to celebrate the community’s achievements, and third to inspire present and future South Asian, Muslim and Scottish generations. It is a community-led charitable project that has been actively documenting a collection of inspirational stories and personal accounts, uniquely told by the protagonists themselves, describing at first hand their stories and adventures. These range all the way from the time of partition itself to resettling in Pakistan, and then to their final accounts of arriving in Scotland. The video footage enables the public to see their facial expressions, feel their emotions and hear their voices, creating poignant memories of these great men and women, and helping to gain a better understanding of the South Asian and Muslim community’s earliest days in Scotland.

The face value of feedback: Facial behavior is shaped by goals and punishments during interaction with dynamic faces

2020 ◽  
Author(s):  
Jonathan Yi ◽  
Philip Pärnamets ◽  
Andreas Olsson
Keyword(s):  
Decision Making ◽  
Facial Expression ◽  
Facial Expressions ◽  
Large Body ◽  
Mechanistic Explanation ◽  
Experimental Manipulation ◽  
Corrugator Supercilii ◽  
Dynamic Faces ◽  
Facial Behavior ◽  
Reinforcement Learning Models

Responding appropriately to others’ facial expressions is key to successful social functioning. Despite the large body of work on face perception and spontaneous responses to static faces, little is known about responses to faces in dynamic, naturalistic situations, and no study has investigated how goal directed responses to faces are influenced by learning during dyadic interactions. To experimentally model such situations, we developed a novel method based on online integration of electromyography (EMG) signals from the participants’ face (corrugator supercilii and zygomaticus major) during facial expression exchange with dynamic faces displaying happy and angry facial expressions. Fifty-eight participants learned by trial-and-error to avoid receiving aversive stimulation by either reciprocate (congruently) or respond opposite (incongruently) to the expression of the target face. Our results validated our method, showing that participants learned to optimize their facial behavior, and replicated earlier findings of faster and more accurate responses in congruent vs. incongruent conditions. Moreover, participants performed better on trials when confronted with smiling, as compared to frowning, faces, suggesting it might be easier to adapt facial responses to positively associated expressions. Finally, we applied drift diffusion and reinforcement learning models to provide a mechanistic explanation for our findings which helped clarifying the underlying decision-making processes of our experimental manipulation. Our results introduce a new method to study learning and decision-making in facial expression exchange, in which there is a need to gradually adapt facial expression selection to both social and non-social reinforcements.

Capacity-Free Automatic Processing of Facial Expressions of Emotion

2020 ◽  
Author(s):  
Joshua W Maxwell ◽  
Eric Ruthruff ◽  
michael joseph
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Face Processing ◽  
Emotional Expressions ◽  
Facial Expression Of Emotion ◽  
Expression Of Emotion ◽  
Facial Expressions Of Emotion ◽  
Correspondence Effect ◽  
Experimental Logic ◽  
Face Task

Are facial expressions of emotion processed automatically? Some authors have not found this to be the case (Tomasik et al., 2009). Here we revisited the question with a novel experimental logic – the backward correspondence effect (BCE). In three dual-task studies, participants first categorized a sound (Task 1) and then indicated the location of a target face (Task 2). In Experiment 1, Task 2 required participants to search for one facial expression of emotion (angry or happy). We observed positive BCEs, indicating that facial expressions of emotion bypassed the central attentional bottleneck and thus were processed in a capacity-free, automatic manner. In Experiment 2, we replicated this effect but found that morphed emotional expressions (which were used by Tomasik) were not processed automatically. In Experiment 3, we observed similar BCEs for another type of face processing previously shown to be capacity-free – identification of familiar faces (Jung et al., 2013). We conclude that facial expressions of emotion are identified automatically when sufficiently unambiguous.

The face of future: Face expressions during future thinking

2021 ◽  
pp. 174702182199299
Author(s):  
Mohamad El Haj ◽  
Emin Altintas ◽  
Ahmed A Moustafa ◽  
Abdel Halim Boudoukha
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Emotional Experience ◽  
Future Scenarios ◽  
Future Thinking ◽  
Analysis Software ◽  
Emotional Facial Expressions ◽  
Facial Analysis ◽  
The Face ◽  
Face Expressions

Future thinking, which is the ability to project oneself forward in time to pre-experience an event, is intimately associated with emotions. We investigated whether emotional future thinking can activate emotional facial expressions. We invited 43 participants to imagine future scenarios, cued by the words “happy,” “sad,” and “city.” Future thinking was video recorded and analysed with a facial analysis software to classify whether facial expressions (i.e., happy, sad, angry, surprised, scared, disgusted, and neutral facial expression) of participants were neutral or emotional. Analysis demonstrated higher levels of happy facial expressions during future thinking cued by the word “happy” than “sad” or “city.” In contrast, higher levels of sad facial expressions were observed during future thinking cued by the word “sad” than “happy” or “city.” Higher levels of neutral facial expressions were observed during future thinking cued by the word “city” than “happy” or “sad.” In the three conditions, the neutral facial expressions were high compared with happy and sad facial expressions. Together, emotional future thinking, at least for future scenarios cued by “happy” and “sad,” seems to trigger the corresponding facial expression. Our study provides an original physiological window into the subjective emotional experience during future thinking.

scholarly journals Human-Robot Handshaking: A Review

2021 ◽  
Author(s):  
Vignesh Prasad ◽  
Ruth Stock-Homburg ◽  
Jan Peters
Keyword(s):  
Facial Expressions ◽  
Social Contexts ◽  
Social Robots ◽  
Human Robot Interaction ◽  
Robot Interaction ◽  
Natural Interaction ◽  
Internal Factors ◽  
Verbal Behaviour ◽  
Key Factor ◽  
Physical Touch

AbstractFor some years now, the use of social, anthropomorphic robots in various situations has been on the rise. These are robots developed to interact with humans and are equipped with corresponding extremities. They already support human users in various industries, such as retail, gastronomy, hotels, education and healthcare. During such Human-Robot Interaction (HRI) scenarios, physical touch plays a central role in the various applications of social robots as interactive non-verbal behaviour is a key factor in making the interaction more natural. Shaking hands is a simple, natural interaction used commonly in many social contexts and is seen as a symbol of greeting, farewell and congratulations. In this paper, we take a look at the existing state of Human-Robot Handshaking research, categorise the works based on their focus areas, draw out the major findings of these areas while analysing their pitfalls. We mainly see that some form of synchronisation exists during the different phases of the interaction. In addition to this, we also find that additional factors like gaze, voice facial expressions etc. can affect the perception of a robotic handshake and that internal factors like personality and mood can affect the way in which handshaking behaviours are executed by humans. Based on the findings and insights, we finally discuss possible ways forward for research on such physically interactive behaviours.

scholarly journals Facial Expression Recognition Based on Multi-Features Cooperative Deep Convolutional Network

2021 ◽  
Vol 11 (4) ◽  
pp. 1428
Author(s):  
Haopeng Wu ◽  
Zhiying Lu ◽  
Jianfeng Zhang ◽  
Xin Li ◽  
Mingyue Zhao ◽  
...  
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Video Data ◽  
Expression Recognition ◽  
Convolutional Network ◽  
Facial Movements ◽  
The Face ◽  
Deep Convolutional Network ◽  
Selection Of

This paper addresses the problem of Facial Expression Recognition (FER), focusing on unobvious facial movements. Traditional methods often cause overfitting problems or incomplete information due to insufficient data and manual selection of features. Instead, our proposed network, which is called the Multi-features Cooperative Deep Convolutional Network (MC-DCN), maintains focus on the overall feature of the face and the trend of key parts. The processing of video data is the first stage. The method of ensemble of regression trees (ERT) is used to obtain the overall contour of the face. Then, the attention model is used to pick up the parts of face that are more susceptible to expressions. Under the combined effect of these two methods, the image which can be called a local feature map is obtained. After that, the video data are sent to MC-DCN, containing parallel sub-networks. While the overall spatiotemporal characteristics of facial expressions are obtained through the sequence of images, the selection of keys parts can better learn the changes in facial expressions brought about by subtle facial movements. By combining local features and global features, the proposed method can acquire more information, leading to better performance. The experimental results show that MC-DCN can achieve recognition rates of 95%, 78.6% and 78.3% on the three datasets SAVEE, MMI, and edited GEMEP, respectively.

scholarly journals Hybrid Attention Cascade Network for Facial Expression Recognition

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2003 ◽  
Author(s):  
Xiaoliang Zhu ◽  
Shihao Ye ◽  
Liang Zhao ◽  
Zhicheng Dai
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Expression Recognition ◽  
Spatial Features ◽  
Face Images ◽  
Temporal Features ◽  
The Face ◽  
In The Wild ◽  
Fusion Features

As a sub-challenge of EmotiW (the Emotion Recognition in the Wild challenge), how to improve performance on the AFEW (Acted Facial Expressions in the wild) dataset is a popular benchmark for emotion recognition tasks with various constraints, including uneven illumination, head deflection, and facial posture. In this paper, we propose a convenient facial expression recognition cascade network comprising spatial feature extraction, hybrid attention, and temporal feature extraction. First, in a video sequence, faces in each frame are detected, and the corresponding face ROI (range of interest) is extracted to obtain the face images. Then, the face images in each frame are aligned based on the position information of the facial feature points in the images. Second, the aligned face images are input to the residual neural network to extract the spatial features of facial expressions corresponding to the face images. The spatial features are input to the hybrid attention module to obtain the fusion features of facial expressions. Finally, the fusion features are input in the gate control loop unit to extract the temporal features of facial expressions. The temporal features are input to the fully connected layer to classify and recognize facial expressions. Experiments using the CK+ (the extended Cohn Kanade), Oulu-CASIA (Institute of Automation, Chinese Academy of Sciences) and AFEW datasets obtained recognition accuracy rates of 98.46%, 87.31%, and 53.44%, respectively. This demonstrated that the proposed method achieves not only competitive performance comparable to state-of-the-art methods but also greater than 2% performance improvement on the AFEW dataset, proving the significant outperformance of facial expression recognition in the natural environment.

Can Unhappy Pictures Enhance the Effect of Personas? A User Experiment

2022 ◽  
Vol 29 (2) ◽  
pp. 1-59
Author(s):  
Joni Salminen ◽  
Sercan Şengün ◽  
João M. Santos ◽  
Soon-Gyo Jung ◽  
Bernard Jansen
Keyword(s):  
Mixed Methods ◽  
Facial Expression ◽  
Personality Traits ◽  
Facial Expressions ◽  
User Perceptions ◽  
Design Ideas ◽  
User Experiment ◽  
Pain Points

There has been little research into whether a persona's picture should portray a happy or unhappy individual. We report a user experiment with 235 participants, testing the effects of happy and unhappy image styles on user perceptions, engagement, and personality traits attributed to personas using a mixed-methods analysis. Results indicate that the participant's perceptions of the persona's realism and pain point severity increase with the use of unhappy pictures. In contrast, personas with happy pictures are perceived as more extroverted, agreeable, open, conscientious, and emotionally stable. The participants’ proposed design ideas for the personas scored more lexical empathy scores for happy personas. There were also significant perception changes along with the gender and ethnic lines regarding both empathy and perceptions of pain points. Implications are the facial expression in the persona profile can affect the perceptions of those employing the personas. Therefore, persona designers should align facial expressions with the task for which the personas will be employed. Generally, unhappy images emphasize realism and pain point severity, and happy images invoke positive perceptions.

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