Forgetting having denied: The "amnesic" consequences of denial

The concept of denial has its roots in psychoanalysis. Denial has been assumed to be effective in blocking unwanted memories. In two experiments, we report that denial has unique consequences for remembering. In two experiments, participants viewed a video of a theft and half of the participants had to deny seeing certain details in the video whereas the other half had to tell the truth. One day later, all participants were given a source monitoring recognition or recall task. In these tasks, they were instructed to indicate (1) whether they could remember talking about certain details and (2) whether they could recollect seeing those details in the video. In both experiments, we found that denial made participants forget that they talked about these details while leaving memory for the video unaffected. This denial-induced forgetting was evident for both the source monitoring recognition and recall tests. Furthermore, when we asked participants after the experiment whether they could still not remember talking about these details, participants who had to deny were most likely to report that they forgot this. In contrast to a widely held belief, we show that denial does not impair memory for the experienced stimuli, but that it has a unique ability to undermine memory for what was talked about

A framework to assess quality and uncertainty in disaster loss data

There is a growing interest in the systematic and consistent collection of disasterloss data for different applications. Therefore, the collected data must follow a set oftechnical requirements to guarantee its usefulness. One of those requirements is theavailability of a measure of the uncertainty in the collected data to express its quality for agiven purpose. Many of the existing disaster loss databases do not provide such uncertainty/qualitymeasures due to the lack of a simple and consistent approach to expressuncertainty. After reviewing existing literature on the subject, a framework to express theuncertainty in disaster loss data is proposed. This framework builds on an existinguncertainty classification that was updated and combined with an existing method for datacharacterization. The proposed approach is able to establish a global score that reflects theoverall uncertainty in a certain loss indicator and provides a measure of its quality