Robust Modeling of Epistemic Mental States

This work identifies and advances some research challenges in the analysis of facial features and their temporal dynamics with epistemic mental states in dyadic conversations. Epistemic states are: Agreement, Concentration, Thoughtful, Certain, and Interest. In this paper, we perform a number of statistical analyses and simulations to identify the relationship between facial features and epistemic states. Non-linear relations are found to be more prevalent, while temporal features derived from original facial features have demonstrated a strong correlation with intensity changes. Then, we propose a novel prediction framework that takes facial features and their nonlinear relation scores as input and predict different epistemic states in videos. The prediction of epistemic states is boosted when the classification of emotion changing regions such as rising, falling, or steady-state are incorporated with the temporal features. The proposed predictive models can predict the epistemic states with significantly improved accuracy: correlation coefficient (CoERR) for Agreement is 0.827, for Concentration 0.901, for Thoughtful 0.794, for Certain 0.854, and for Interest 0.913.Comment: Accepted for Publication in Multimedia Tools and Application, Special Issue: Socio-Affective Technologie

Mapping dynamic interactions among cognitive biases in depression

Depression is theorized to be caused in part by biased cognitive processing of emotional information. Yet, prior research has adopted a reductionist approach that does not characterize how biases in cognitive processes such as attention and memory work together to confer risk for this complex multifactorial disorder. Grounded in affective and cognitive science, we highlight four mechanisms to understand how attention biases, working memory difficulties, and long-term memory biases interact and contribute to depression. We review evidence for each mechanism and highlight time- and context-dependent dynamics. We outline methodological considerations and recommendations for research in this area. We conclude with directions to advance the understanding of depression risk, cognitive training interventions, and transdiagnostic properties of cognitive biases and their interactions

Holistic gaze strategy to categorize facial expression of varying intensities

Using faces representing exaggerated emotional expressions, recent behaviour and eye-tracking studies have suggested a dominant role of individual facial features in transmitting diagnostic cues for decoding facial expressions. Considering that in everyday life we frequently view low-intensity expressive faces in which local facial cues are more ambiguous, we probably need to combine expressive cues from more than one facial feature to reliably decode naturalistic facial affects. In this study we applied a morphing technique to systematically vary intensities of six basic facial expressions of emotion, and employed a self-paced expression categorization task to measure participants’ categorization performance and associated gaze patterns. The analysis of pooled data from all expressions showed that increasing expression intensity would improve categorization accuracy, shorten reaction time and reduce number of fixations directed at faces. The proportion of fixations and viewing time directed at internal facial features (eyes, nose and mouth region), however, was not affected by varying levels of intensity. Further comparison between individual facial expressions revealed that although proportional gaze allocation at individual facial features was quantitatively modulated by the viewed expressions, the overall gaze distribution in face viewing was qualitatively similar across different facial expressions and different intensities. It seems that we adopt a holistic viewing strategy to extract expressive cues from all internal facial features in processing of naturalistic facial expressions

Evaluating Engagement in Digital Narratives from Facial Data

Engagement researchers indicate that the engagement level of people in a narrative has an influence on people's subsequent story-related attitudes and beliefs, which helps psychologists understand people's social behaviours and personal experience. With the arrival of multimedia, the digital narrative combines multimedia features (e.g. varying images, music and voiceover) with traditional storytelling. Research on digital narratives has been widely used in helping students gain problem-solving and presentation skills as well as supporting child psychologists investigating children's social understanding such as family/peer relationships through completing their digital narratives. However, there is little study on the effect of multimedia features in digital narratives on the engagement level of people. This research focuses on measuring the levels of engagement of people in digital narratives and specifically on understanding the media effect of digital narratives on people's engagement levels. Measurement tools are developed and validated through analyses of facial data from different age groups (children and young adults) in watching stories with different media features of digital narratives. Data sources used in this research include a questionnaire with Smileyometer scale and the observation of each participant's facial behaviours