Compared to matched controls, adults with substance use disorders self-report significantly more difficulties with emotional awareness and regulation. Compared to matched controls, adults with substance use disorders report significantly greater expectancies not to show depression and anxiety. When viewing positive and negative images, adults with substance use disorders are significantly less flexible in their facial expression of emotion than matched controls in response to regulatory instructions. Emotion regulation should be measured and addressed as part of substance use disorder treatment.

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Selective sensitization has been proposed as an alternative explanation for enhanced responding to animal fear-relevant stimuli--snakes and spiders--during extinction of Pavlovian fear conditioning. The current study sought to replicate the phenomenon using a shock workup procedure as the sensitizing manipulation and to extend it to interpersonal and intergroup fear-relevant stimuli--angry faces and other-race faces. Assessment of selective sensitization was followed by a one-trial fear learning procedure. Selective sensitization, larger electrodermal responses to fear-relevant than to control stimuli after sensitization, or a larger increase in electrodermal responding to fear-relevant than to control stimuli after sensitization was observed across stimulus domains. However, the one-trial fear learning procedure failed to provide evidence for enhanced fear conditioning to fear-relevant stimuli. One-trial fear learning was either absent or present for fear-relevant and nonfear-relevant stimuli. The current study confirms that electrodermal responses to fear-relevant stimuli across stimulus domains are subject to selective sensitization.

The use of big data in higher education has evolved rapidly with a focus on the practical application of new tools and methods for supporting learning. In this paper, we depart from the core emphasis on application and delve into a mostly neglected aspect of the big data conversation in higher education. Drawing on developments in cognate disciplines, we analyse the inherent difficulties in inferring the complex phenomenon that is learning from big datasets. This analysis forms the basis for a discussion about the possibilities for systematic collaboration across different paradigms and disciplinary backgrounds in interpreting big data for enhancing learning. The aim of this paper is to provide the foundation for a research agenda, where differing conceptualisations of learning become a strength in interpreting patterns in big datasets, rather than a point of contention.