The implicit relational assessment procedure: emerging reliability and validity data

The Implicit Relational Assessment Procedure (IRAP) is a measure of ‘implicit cognition' developed on the basis of a contemporary behavioural analysis of language and cognition. The IRAP has now been applied to a range of foci over five years of published research. A frequently-cited caveat in publications to date is the need for further research to gauge the reliability and validity of the IRAP as an implicit measure. This review paper will provide a critical synthesis of available evidence for reliability and validity. The review applies a multifaceted test-theory approach to validity, and reliability is assessed through meta-analysis of published data. The discussion critically considers reviewed IRAP evidence with reference to the extant literature on alternative implicit measures, limitations of studies to date, and consideration of broader conceptual issues

Highly neurotic never-depressed students have negative biases in information processing

BACKGROUND: Cognitive theories associate depression with negative biases in information processing. Although negatively biased cognitions are well documented in depressed patients and to some extent in recovered patients, it remains unclear whether these abnormalities are present before the first depressive episode. METHOD: High neuroticism (N) is a well-recognized risk factor for depression. The current study therefore compared different aspects of emotional processing in 33 high-N never-depressed and 32 low-N matched volunteers. Awakening salivary cortisol, which is often elevated in severely depressed patients, was measured to explore the neurobiological substrate of neuroticism. RESULTS: High-N volunteers showed increased processing of negative and/or decreased processing of positive information in emotional categorization and memory, facial expression recognition and emotion-potentiated startle (EPS), in the absence of global memory or executive deficits. By contrast, there was no evidence for effects of neuroticism on attentional bias (as measured with the dot-probe task), over-general autobiographical memory, or awakening cortisol levels. CONCLUSIONS: These results suggest that certain negative processing biases precede depression rather than arising as a result of depressive experience per se and as such could in part mediate the vulnerability of high-N subjects to depression. Longitudinal studies are required to confirm that such cognitive vulnerabilities predict subsequent depression in individual subjects

First impressions: A survey on vision-based apparent personality trait analysis

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Personality analysis has been widely studied in psychology, neuropsychology, and signal processing fields, among others. From the past few years, it also became an attractive research area in visual computing. From the computational point of view, by far speech and text have been the most considered cues of information for analyzing personality. However, recently there has been an increasing interest from the computer vision community in analyzing personality from visual data. Recent computer vision approaches are able to accurately analyze human faces, body postures and behaviors, and use these information to infer apparent personality traits. Because of the overwhelming research interest in this topic, and of the potential impact that this sort of methods could have in society, we present in this paper an up-to-date review of existing vision-based approaches for apparent personality trait recognition. We describe seminal and cutting edge works on the subject, discussing and comparing their distinctive features and limitations. Future venues of research in the field are identified and discussed. Furthermore, aspects on the subjectivity in data labeling/evaluation, as well as current datasets and challenges organized to push the research on the field are reviewed.Peer ReviewedPostprint (author's final draft