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Understanding emotions is key to Affective Computing. Emotion recognition focuses on the communicative component of emotions encoded in social signals. This view alone is insufficient for a deeper understanding and computational representation of the internal, subjectively experienced component of emotions. This article presents a cognition-based method called Deep as a starting point for deeper computational modeling of the internal component of emotions. Deep incorporates an approach to query individual internal emotional experiences and to represent such information computationally. It combines social signals, verbalized introspection information, context information, and theory-driven knowledge. We apply the Deep method to the emotion of shame as an example and compare it to a typical emotion recognition model, highlighting the differences and advantages. [ABSTRACT FROM AUTHOR]

Autonomous cars are indispensable when humans go further down the hands-free route. Although existing literature highlights that the acceptance of the autonomous car will increase if it drives in a human-like manner, sparse research offers the naturalistic experience from a passenger's seat perspective to examine the humanness of current autonomous cars. The present study tested whether the AI driver could create a human-like ride experience for passengers based on 69 participants’ feedback in a real-road scenario. We designed a ride experience-based version of the non-verbal Turing test for automated driving. Participants rode in autonomous cars (driven by either human or AI drivers) as a passenger and judged whether the driver was human or AI. The AI driver failed to pass our test because passengers detected the AI driver above chance. In contrast, when the human driver drove the car, the passengers’ judgement was around chance. We further investigated how human passengers ascribe humanness in our test. Based on Lewin's field theory, we advanced a computational model combining signal detection theory with pre-trained language models to predict passengers’ humanness rating behaviour. We employed affective transition between pre-study baseline emotions and corresponding post-stage emotions as the signal strength of our model. Results showed that the passengers’ ascription of humanness would increase with the greater affective transition. Our study suggested an important role of affective transition in passengers’ ascription of humanness, which might become a future direction for autonomous driving. [ABSTRACT FROM AUTHOR]

In this article we propose the use of convolutional self-attention for attention-based representation learning, while replacing traditional vectorization methods with a transformer as the backbone of our speech model for transfer learning within our automatic deceit detection framework. This design performs a multimodal data analysis and applies fusion to merge visual, vocal, and speech(textual) channels; reporting deceit predictions. Our experimental results show that the proposed architecture improves the state-of-the-art on the popular Real-Life Trial (RLT) dataset in terms of correct classification rate. To further assess the generalizability of our design, we experiment on the low-stakes Box of Lies (BoL) dataset and achieve state-of-the-art performance as well as providing cross-corpus comparisons. Following our analysis, we report that (1) convolutional self-attention learns meaningful representations while performing joint attention computation for deception, (2) apparent deceptive intent is a continuous function of time and subjects can display varying levels of apparent deceptive intent throughout recordings, and (3), in support of criminal psychology findings, studying abnormal behavior out of context can be an unreliable way to predict deceptive intent. [ABSTRACT FROM AUTHOR]

Chronic stress risks an individual's overall well-being. Chronic stress is associated with allostatic load, the body's wear-and-tear due to prolonged heightened physiological and psychological states. Increased allostatic load among workers increases their risk of injuries and the likelihood of diseases and illnesses. An allostatic load model could explain the basis of a stress response. Stress research in affective computing uses wearable devices, data processing algorithms, and machine learning methods to create models that could benefit from an allostatic load model of stress. We emphasize the need for the allostatic load model in affective computing to create disease and illness prediction models. Predictive models could enhance safeguards in the workplace by helping to create proactive mitigation strategies against chronic stress. First, we briefly introduce allostasis’ physiological and psychological basis. Next, we reviewed stress studies within affective computing that may benefit from an allostatic load model of stress. We focused our review on studies conducted in dynamic settings, such as the workplace, and those incorporating typical stress study elements in affective computing. We conclude our review by identifying gaps between affective computing and neuroscientific stress studies and provide recommendations for adopting the allostatic load model of stress. [ABSTRACT FROM AUTHOR]