A conceptual review of research on the pathological use of computers, video games, and the internet

Author name used in this manuscript: Timothy Sim2012-2013 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Personality disorders and psychosocial problems in a group of participants to therapeutic processes for people with severe social disabilities

<p>Abstract</p> <p>Background</p> <p>Homeless people have high dropout rates when they participate in therapeutic processes. The causes of this failure are not always known. This study investigates whether dropping-out is mediated by personality disorders or whether psychosocial problems are more important.</p> <p>Method</p> <p>Eighty-nine homeless people in a socio-laboral integration process were assessed. An initial interview was used, and the MCMI II questionnaire was applied to investigate the presence of psychosocial disorders (DSM-IV-TR axis IV). This was designed as an <it>ex post-facto </it>prospective study.</p> <p>Results</p> <p>Personality disorders were very frequent among the homeless people examined. Moreover, the high index of psychosocial problems (axis IV) in this population supported the proposal that axis IV disorders are influential in failure to complete therapy.</p> <p>Conclusion</p> <p>The outcomes of the study show that the homeless people examined presented with more psychopathological symptoms, in both axis II and axis IV, than the general population. This supports the need to take into account the comorbidity between these two types of disorder among homeless people, in treatment and in the development of specific intervention programs. In conclusion, the need for more psychosocial treatments addressing the individual problems of homeless people is supported.</p

Predicting complexity perception of real world images

The aim of this work is to predict the complexity perception of real world images.We propose a new complexity measure where different image features, based on spatial, frequency and color properties are linearly combined. In order to find the optimal set of weighting coefficients we have applied a Particle Swarm Optimization. The optimal linear combination is the one that best fits the subjective data obtained in an experiment where observers evaluate the complexity of real world scenes on a web-based interface. To test the proposed complexity measure we have performed a second experiment on a different database of real world scenes, where the linear combination previously obtained is correlated with the new subjective data. Our complexity measure outperforms not only each single visual feature but also two visual clutter measures frequently used in the literature to predict image complexity. To analyze the usefulness of our proposal, we have also considered two different sets of stimuli composed of real texture images. Tuning the parameters of our measure for this kind of stimuli, we have obtained a linear combination that still outperforms the single measures. In conclusion our measure, properly tuned, can predict complexity perception of different kind of images