More Like this

1. Social-Emotional-Sensory Design Map for Affective Computing Informed by Neurodivergent Experiences

   https://doi.org/10.1145/3449151  

   Zolyomi, Annuska; Snyder, Jaime (April 2021, Proceedings of the ACM on Human-Computer Interaction)

   One of the grand challenges of artificial intelligence and affective computing is for technology to become emotionally-aware and thus, more human-like. Modeling human emotions is particularly complicated when we consider the lived experiences of people who are on the autism spectrum. To understand the emotional experiences of autistic adults and their attitudes towards common representations of emotions, we deployed a context study as the first phase of a Grounded Design research project. Based on community observations and interviews, this work contributes empirical evidence of how the emotional experiences of autistic adults are entangled with social interactions as well as the processing of sensory inputs. We learned that (1) the emotional experiences of autistic adults are embodied and co-constructed within the context of physical environments, social relationships, and technology use, and (2) conventional approaches to visually representing emotion in affective education and computing systems fail to accurately represent the experiences and perceptions of autistic adults. We contribute a social-emotional-sensory design map to guide designers in creating more diverse and nuanced affective computing interfaces that are enriched by accounting for neurodivergent users. 

   more » « less
2. A Heterogeneous Multimodal Graph Learning Framework for Recognizing User Emotions in Social Networks

   Bhattacharyya, Sree; Yang, Shuhua; Wang, James Z (September 2024, Proceedings of the International Conference on Affective Computing and Intelligent Interaction)

   The rapid expansion of social media platforms has provided unprecedented access to massive amounts of multimodal user-generated content. Comprehending user emotions can provide valuable insights for improving communication and understanding of human behaviors. Despite significant advancements in Affective Computing, the diverse factors influencing user emotions in social networks remain relatively understudied. Moreover, there is a notable lack of deep learning-based methods for predicting user emotions in social networks, which could be addressed by leveraging the extensive multimodal data available. This work presents a novel formulation of personalized emotion prediction in social networks based on heterogeneous graph learning. Building upon this formulation, we design HMG-Emo, a Heterogeneous Multimodal Graph Learning Framework that utilizes deep learning-based features for user emotion recognition. Additionally, we include a dynamic context fusion module in HMG-Emo that is capable of adaptively integrating the different modalities in social media data. Through extensive experiments, we demonstrate the effectiveness of HMG-Emo and verify the superiority of adopting a graph neural network-based approach, which outperforms existing baselines that use rich hand-crafted features. To the best of our knowledge, HMG-Emo is the first multimodal and deep-learning-based approach to predict personalized emotions within online social networks. Our work highlights the significance of exploiting advanced deep learning techniques for less-explored problems in Affective Computing. 

   more » « less
3. What should we do with Emotion AI? Towards an Agenda for the Next 30 Years

   https://doi.org/10.1145/3678884.3689135  

   Andalibi, Nazanin; Stark, Luke; McDuff, Daniel; Picard, Rosalind; Gratch, Jonathan; Howell, Noura (November 2024, ACM)

   What should we do with emotion AI? Should we regulate, ban, promote, or re-imagine it? Emotion AI, a class of affective computing technologies used in personal and social computing, comprises emergent and controversial techniques aiming to classify human emotion and other affective phenomena. Industry, policy, and scientific actors debate potential benefits and harms, arguing for polarized futures ranging from panoptic expansion to complete bans. Emotion AI is proposed, deployed, and sometimes withdrawn in collaborative contexts such as education, hiring, healthcare, and service work. Proponents expound these technologies’ benefits for well-being and security, while critics decry privacy harms, civil liberties risks, bias, and shaky scientific foundations, and gaps between technologies’ capabilities and how they are marketed and legitimized. This panel brings diverse disciplinary perspectives into discussion about the history of emotions—as an example of ’intimate’ data—in computing, how emotion AI is legitimized, people’s experiences with and perceptions of emotion AI in social and collaborative settings, emotion AI’s development practices, and using design research to re-imagine emotion AI. These issues are relevant to the CSCW community in designing, evaluating, and regulating algorithmic sensing technologies including and beyond emotion-sensing. 

   more » « less
4. Sinful pleasures and pious woes? Using fMRI to examine evaluative and hedonic emotion knowledge

   https://doi.org/10.1093/scan/nsac024  

   Lee, Kent M.; Lee, SuhJin; Satpute, Ajay B. (March 2022, Social Cognitive and Affective Neuroscience)

   Abstract Traditionally, lust and pride have been considered pleasurable, yet sinful in the West. Conversely, guilt is often considered aversive, yet valuable. These emotions illustrate how evaluations about specific emotions and beliefs about their hedonic properties may often diverge. Evaluations about specific emotions may shape important aspects of emotional life (e.g. in emotion regulation, emotion experience and acquisition of emotion concepts). Yet these evaluations are often understudied in affective neuroscience. Prior work in emotion regulation, affective experience, evaluation/attitudes and decision-making point to anterior prefrontal areas as candidates for supporting evaluative emotion knowledge. Thus, we examined the brain areas associated with evaluative and hedonic emotion knowledge, with a focus on the anterior prefrontal cortex. Participants (N = 25) made evaluative and hedonic ratings about emotion knowledge during functional magnetic resonance imaging (fMRI). We found that greater activity in the medial prefrontal cortex (mPFC), ventromedial PFC (vmPFC) and precuneus was associated with an evaluative (vs hedonic) focus on emotion knowledge. Our results suggest that the mPFC and vmPFC, in particular, may play a role in evaluating discrete emotions. 

   more » « less
5. Surprise as an Emotion: A Response to Ortony

   https://doi.org/10.1177/17456916221132789  

   Neta, Maital; Kim, M_Justin (November 2022, Perspectives on Psychological Science)

   We write in response to an article published in this journal by Andrew Ortony titled “Are All ‘Basic Emotions’ Emotions? A Problem for the (Basic) Emotions Construct.” The author claimed that “for all its elevated status as a basic emotion, surprise fails to satisfy the minimal requirements that [he] proposed for something to be an emotion, and if it is not an emotion, it cannot possibly be a basic emotion.” Although we acknowledge the concerns brought forth by Ortony, we respectfully disagree with his conclusion about surprise. To make a case against the assertion that surprise is valence-free, we summarize an extensive body of work showing that surprise is indeed valenced—in a specific manner (i.e., ambiguously valenced)—and that it meets all of Ortony’s criteria for an emotion. In other words, rather than being described as neither positive nor negative, this emotion is either positive or negative. We consider the data with respect to surprise as a basic emotion, and we dispute the definitions of basic emotions as “widely divergent.” Future work is needed to continue defining an emotion, and a basic emotion, but we believe this is a worthy effort toward shaping a still evolving field. 

   more » « less