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

Looking Beyond a Clever Narrative: Visual Context and Attention are Primary Drivers of Affect in Video Advertisements

Emotion evoked by an advertisement plays a key role in influencing brand recall and eventual consumer choices. Automatic ad affect recognition has several useful applications. However, the use of content-based feature representations does not give insights into how affect is modulated by aspects such as the ad scene setting, salient object attributes and their interactions. Neither do such approaches inform us on how humans prioritize visual information for ad understanding. Our work addresses these lacunae by decomposing video content into detected objects, coarse scene structure, object statistics and actively attended objects identified via eye-gaze. We measure the importance of each of these information channels by systematically incorporating related information into ad affect prediction models. Contrary to the popular notion that ad affect hinges on the narrative and the clever use of linguistic and social cues, we find that actively attended objects and the coarse scene structure better encode affective information as compared to individual scene objects or conspicuous background elements.Comment: Accepted for publication in the Proceedings of 20th ACM International Conference on Multimodal Interaction, Boulder, CO, US

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

Atypical eye contact in autism: Models, mechanisms and development

An atypical pattern of eye contact behaviour is one of the most significant symptoms of Autism Spectrum Disorder (ASD). Recent empirical advances have revealed the developmental, cognitive and neural basis of atypical eye contact behaviour in ASD. We review different models and advance a new ‘fast-track modulator model’. Specifically, we propose that atypical eye contact processing in ASD originates in the lack of influence from a subcortical face and eye contact detection route, which is hypothesized to modulate eye contact processing and guide its emergent specialization during development

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

A Motivational Determinant of Facial Emotion Recognition : Regulatory Focus Affects Recognition of Emotions in Faces

Funding: The research was supported by The Netherlands Organization for Scientific Research (NWO, project 452-07-006). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Peer reviewedPublisher PD