COVID-19 threatens lives, livelihoods, and civic institutions. Although restrictive public health behaviors such as social distancing help manage its impact, these behaviors can further sever our connections to people and institutions that affirm our identities. Three studies ( N = 1,195) validated a brief 10-item COVID-19 Threat Scale that assesses (1) realistic threats to physical or financial safety and (2) symbolic threats to one’s sociocultural identity. Studies reveal that both realistic and symbolic threats predict higher distress and lower well-being and demonstrate convergent validity with other measures of threat sensitivity. Importantly, the two kinds of threats diverge in their relationship to restrictive public health behaviors: Realistic threat predicted greater self-reported adherence, whereas symbolic threat predicted less self-reported adherence to social disconnection behaviors. Symbolic threat also predicted using creative ways to affirm identity even in isolation. Our findings highlight how social psychological theory can be leveraged to understand and predict people’s behavior in pandemics.
COVID-19 threatens lives, livelihoods, and civic institutions. Although restrictive public health behaviors such as social distancing help manage its impact, these behaviors can further sever our connections to people and institutions that affirm our identities. Three studies (N=1,195) validated a brief 10-item COVID-19 threat scale that assesses 1) realistic threats to physical or financial safety, and 2) symbolic threats to one’s sociocultural identity. Studies reveal that both realistic and symbolic threat predict higher distress and lower wellbeing, and demonstrate convergent validity with other measures of threat sensitivity. Importantly, the two kinds of threat diverge in their relationship to restrictive public health behaviors: Realistic threat predicted greater self-reported adherence, whereas symbolic threat predicted less self-reported adherence to social-disconnection behaviors. Symbolic threat also predicted using creative ways to affirm identity even in isolation. Our findings highlight how social psychological theory can be leveraged to understand and predict people’s behavior in pandemics.
We present an autoencoder-based semi-supervised approach to classify perceived human emotions from walking styles obtained from videos or from motion-captured data and represented as sequences of 3D poses. Given the motion on each joint in the pose at each time step extracted from 3D pose sequences, we hierarchically pool these joint motions in a bottom-up manner in the encoder, following the kinematic chains in the human body. We also constrain the latent embeddings of the encoder to contain the space of psychologically-motivated affective features underlying the gaits. We train the decoder to reconstruct the motions per joint per time step in a top-down manner from the latent embeddings. For the annotated data, we also train a classifier to map the latent embeddings to emotion labels. Our semisupervised approach achieves a mean average precision of 0.84 on the Emotion-Gait benchmark dataset, which contains gaits collected from multiple sources. We outperform current state-of-art algorithms for both emotion recognition and action recognition from 3D gaits by 7% -23% on the absolute.
We present a novel, real-time algorithm, EVA, for generating virtual agents with various perceived emotions. Our approach is based on using Expressive Features of gaze and gait to convey emotions corresponding to happy, sad, angry, or neutral. We precompute a data-driven mapping between gaits and their perceived emotions. EVA uses this gait emotion association at runtime to generate appropriate walking styles in terms of gaits and gaze. Using the EVA algorithm, we can simulate gaits and gazing behaviors of hundreds of virtual agents in real-time with known emotional characteristics. We have evaluated the benefits in different multi-agent VR simulation environments. Our studies suggest that the use of expressive features corresponding to gait and gaze can considerably increase the sense of presence in scenarios with multiple virtual agents.
Robots are becoming more available for workplace collaboration, but many questions remain. Are people actually willing to assign collaborative tasks to robots? And if so, exactly which tasks will they assign to what kinds of robots? Here we leverage psychological theories on person-job fit and mind perception to investigate task assignment in human–robot collaborative work. We propose that people will assign robots to jobs based on their “perceived mind,” and also that people will show predictable social biases in their collaboration decisions. In this study, participants performed an arithmetic (i.e., calculating differences) and a social (i.e., judging emotional states) task, either alone or by collaborating with one of two robots: an emotionally capable robot or an emotionally incapable robot. Decisions to collaborate (i.e., to assign the robots to generate the answer) rates were high across all trials, especially for tasks that participants found challenging (i.e., the arithmetic task). Collaboration was predicted by perceived robot-task fit, such that the emotional robot was assigned the social task. Interestingly, the arithmetic task was assigned more to the emotionally incapable robot, despite the emotionally capable robot being equally capable of computation. This is consistent with social biases (e.g., gender bias) in mind perception and person-job fit. The theoretical and practical implications of this work for HRI are being discussed.
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