The (In)visibility of Psychodiagnosticians' Expertise

This study investigates decision making in mental health care. Specifically, it compares the diagnostic decision outcomes (i.e., the qualityof diagnoses) and the diagnostic decision process (i.e., pre-decisional information acquisition patterns) of novice and experienced clinicalpsychologists. Participants’ eye movements were recorded while they completed diagnostic tasks, classifying mental disorders. In line withprevious research, our findings indicate that diagnosticians’ performance is not related to their clinical experience. Eye-tracking data pro-vide corroborative evidence for this result from the process perspective: experience does not predict changes in cue inspection patterns. Forfuture research into expertise in this domain, it is advisable to track individual differences between clinicians rather than study differenceson the group level.

“Proximising” climate change reconsidered: A construal level theory perspective

Reducing the psychological distance of climate change has repeatedly been proposed as one strategy to increase individuals' motivation to respond to climate change. From the perspective of construal level theory, decreasing psychological distance should not itself influence people's willingness to act but change the processes that underlie individual decision-making. We conducted two experiments in which we manipulated the psychological distance of climate change. We found that participants with a distant focus relied more on scepticism to represent risks and make decisions about supporting climate change, whereas participants with a proximal perspective relied more on fear when making such judgements. However, the predicted Fear × Distance interaction was only found when self-reported fear rather than experimentally manipulated fear was used as a moderator. Our results suggest that simply proximising won't increase engagement and call for a more differentiated perspective on the effects of psychological distance in the context of climate change.

Granular computing as a basis of human–data interaction: a cognitive cities use case

The article proposes granular computing as a theoretical, formal and methodological basis for the newly emerging research field of human–data interaction (HDI). We argue that the ability to represent and reason with information granules is a prerequisite for data legibility. As such, it allows for extending the research agenda of HDI to encompass the topic of collective intelligence amplification, which is seen as an opportunity of today’s increasingly pervasive computing environments. As an example of collective intelligence amplification in HDI, we introduce a collaborative urban planning use case in a cognitive city environment and show how an iterative process of user input and human-oriented automated data processing can support collective decision making. As a basis for automated human-oriented data processing, we use the spatial granular calculus of granular geometry.