The Belfast Induced Natural Emotion Database

For many years psychological research on facial expression of emotion has relied heavily on a recognition paradigm based on posed static photographs. There is growing evidence that there may be fundamental differences between the expressions depicted in such stimuli and the emotional expressions present in everyday life. Affective computing, with its pragmatic emphasis on realism, needs examples of natural emotion. This paper describes a unique database containing recordings of mild to moderate emotionally coloured responses to a series of laboratory based emotion induction tasks. The recordings are accompanied by information on self-report of emotion and intensity, continuous trace-style ratings of valence and intensity, the sex of the participant, the sex of the experimenter, the active or passive nature of the induction task and it gives researchers the opportunity to compare expressions from people from more than one culture.

Emotional intelligence, personality and decoding non-verbal expressions of emotion

Previous research has highlighted theoretical and empirical links between measures of both personality and trait emotional intelligence (EI), and the ability to decode facial expressions of emotion. Research has also found that the posed, static characteristics of the photographic stimuli used to explore these links affects the decoding process and differentiates them from the natural expressions they represent. This undermines the ecological validity of established trait-emotion decoding relationships. This study addresses these methodological shortcomings by testing relationships between the reliability of participant ratings of dynamic, spontaneously elicited expressions of emotion with personality and trait EI. Fifty participants completed personality and self-report EI questionnaires, and used a computer-logging program to continuously rate change in emotional intensity expressed in video clips. Each clip was rated twice to obtain an intra-rater reliability score. The results provide limited support for links between both trait EI and personality variables and how reliably we decode natural expressions of emotion. Limitations and future directions are discussed.

Tracing emotion: an overview

Computational research with continuous representations depends on obtaining continuous representations from human labellers. The main method used for that purpose is tracing. Tracing raises a range of challenging issues, both psychological and statistical. Naive assumptions about these issues are easy to make, and can lead to inappropriate requirements and uses. The natural function of traces is to capture perceived affect, and as such they belong in long traditions of research on both perception and emotion. Experiments on several types of material provide information about their characteristics, particularly the ratings on which people tend to agree. Disagreement is not necessarily a problem in the technique. It may correctly show that people’s impressions of emotion diverge more than commonly thought. A new system, Gtrace, is designed to let rating studies capitalise on a decade of experience and address the research questions that are opened up by the data now available.