Is identity per se irrelevant? A contrarian view of self-verification effects

Self-verification theory (SVT) posits that people who hold negative self-views, such as depressive patients, ironically strive to verify that these self-views are correct, by actively seeking out critical feedback or interaction partners who evaluate them unfavorably. Such verification strivings are allegedly directed towards maximizing subjective perceptions of prediction and control. Nonetheless, verification strivings are also alleged to stabilize maladaptive self-perceptions, and thereby hindering therapeutic recovery. Despite the widespread acceptance of SVT, I contend that the evidence for it is weak and circumstantial. In particular, I contend that that most or all major findings cited in support of SVT can be more economically explained in terms of raison oblige theory (ROT). ROT posits that people with negative self-views solicit critical feedback, not because they want it, but because they their self-view inclines them regard it as probative, a necessary condition for considering it worth obtaining. Relevant findings are reviewed and reinterpreted with an emphasis on depression, and some new empirical data reported

Implicit self-esteem and narcissism: rethinking the link

Several studies have found that explicit and implicit self-esteem interact to predict narcissism. These findings have been interpreted as supporting the hypothesis that narcissists have high but fragile self-esteem. However, we contend that these findings are neither empirically consistent nor conceptually coherent. We instead hypothesize that explicit and implicit self-esteem should predict narcissism independently, respectively in a positive and negative direction. In a large multi-session study, we examined the interrelationships between narcissism, explicit self-esteem, and three indices of implicit self-esteem (showing good psychometric properties and some convergent validity). No evidence emerged that explicit and implicit self-esteem interacted to predict narcissism. However, as predicted, two measures of implicit self-esteem were inversely related to narcissism. Potential explanations for divergent findings are considered

Promoting Handwashing and Sanitation: Evidence From a Large-Scale Randomized Trial in Rural Tanzania

This paper presents the results of two large-scale, government-led handwashing and sanitation promotion campaigns in rural Tanzania. Their results highlight the importance of focusing on intermediate outcomes of take-up and behavior change as a critical first step in large-scale programs before realizing the changes in health that sanitation and hygiene interventions aim to deliver

Stigmergy in Web 2.0: a model for site dynamics

Building Web 2.0 sites does not necessarily ensure the success of the site. We aim to better understand what improves the success of a site by drawing insight from biologically inspired design patterns. Web 2.0 sites provide a mechanism for human interaction enabling powerful intercommunication between massive volumes of users. Early Web 2.0 site providers that were previously dominant are being succeeded by newer sites providing innovative social interaction mechanisms. Understanding what site traits contribute to this success drives research into Web sites mechanics using models to describe the associated social networking behaviour. Some of these models attempt to show how the volume of users provides a self-organising and self-contextualisation of content. One model describing coordinated environments is called stigmergy, a term originally describing coordinated insect behavior. This paper explores how exploiting stigmergy can provide a valuable mechanism for identifying and analysing online user behavior specifically when considering that user freedom of choice is restricted by the provided web site functionality. This will aid our building better collaborative Web sites improving the collaborative processes

Intelligent image processing techniques for structuring a visual diary

The SenseCam is a small wearable personal device which automatically captures up to 3,500 images per day. This yields a very large personal collection of images or in a sense, a diary of a person's day. Over one million images will need to be stored each year, therefore intelligent techniques are necessary for the effective searching and browsing of this image collection for important or significant events in a person's life, and one of the issues is how to detect and then relate similar events in a lifetime. This is necessary in order to detect unusual or once-off events, as well as determining routine activities. This poster will present the various sources of data that can be collected with a SenseCam device, and also other sources that can be collected to compliment the SenseCam data sources. Different forms of image processing that can be carried out on this large set of images will be detailed, specifically how to detect what images belong to individual events, and also how similar various events are to each other. There will be hundreds of thousands of images of everyday routines; as a result more memorable events are quite likely to be significantly different to other normal reoccurring events

Combining face detection and novelty to identify important events in a visual lifelog

The SenseCam is a passively capturing wearable camera, worn around the neck and takes an average of almost 2,000 images per day, which equates to over 650,000 images per year. It is used to create a personal lifelog or visual recording of the wearer’s life and generates information which can be helpful as a human memory aid. For such a large amount of visual information to be of any use, it is accepted that it should be structured into “events”, of which there are about 8,000 in a wearer’s average year. In automatically segmenting SenseCam images into events, it is desirable to automatically emphasise more important events and decrease the emphasis on mundane/routine events. This paper introduces the concept of novelty to help determine the importance of events in a lifelog. By combining novelty with face-to-face conversation detection, our system improves on previous approaches. In our experiments we use a large set of lifelog images, a total of 288,479 images collected by 6 users over a time period of one month each

Organising a large quantity of lifelog images

Preliminary research indicates that a visual recording of one’s activities may be beneficial for sufferers of neurodegenerative diseases. However there exists a number of challenges in managing the vast quantities of data generated by lifelogging devices such as the SenseCam. Our work concentrates on the following areas within visual lifelogging: Segmenting sequences of images into events (e.g. breakfast, at meeting); retrieving similar events (“what other times was I at the park?”); determining most important events (meeting an old friend is more important than breakfast); selection of ideal keyframe to provide an event summary; and augmenting lifeLog events with images taken by millions of users from ‘Web 2.0’ websites (“show me other pictures of the Statue of Liberty to augment my own lifelog images”)