We think you agree: The detrimental impact of the false consensus effect on behavior

Two studies were designed to examine the impact of the false consensus effect on behavior (FCE; Ross, Greene, & House, 1977). False consensus is a form of social projection whereby individuals overestimate the degree to which others share their characteristics or beliefs. In a modified test of the theory of reasoned action, Study 1 demonstrated that the FCE independently predicts behavioral intentions regarding important social issues in a sample comprised of 205 college students. Further, results indicated that self-monitoring moderates the extent to which the FCE predicts behavioral intentions (specifically, as hypothesized, the FCE is a stronger predictor of behavior for high self-monitors). Because of the prevalence of the FCE among college students regarding potentially harmful social behaviors, Study 2 was designed to eliminate the FCE by differentially presenting students (N = 280) with alternative viewpoints regarding various issues. Presenting both sides of an argument using video-based stimuli effectively reduced the FCE. Recommendations for interventions that effectively promote beneficial social norms are discussed.     

Religiosity and Mental Health: A Meta–Analysis of Recent Studies

A meta–analysis was performed in an attempt to clarify the proposed relationship between religiosity and psychological adjustment. Specific focus was given to the issue of definition, namely, whether differences in researchers' conceptualizations of religiosity and mental health could account for the various contradictory findings by psychologists of religion. Analysis of 34 studies conducted during the past 12 years revealed that the definitions of religiosity and mental health utilized by psychologists in this field were indeed associated with different types and strengths of the correlations between religiosity and mental health. Discussion of results assesses the fit between relevant theory and the pattern of change in effect size across categories of religion and adjustment, and concludes with implications for therapeutic uses of religious involvement.     

A new EOG-based eyeblink detection algorithm

Accurate and efficient operator functional state classification and assessment based on physiological data have many important applications ranging from operator monitoring to interaction and control of human/machine systems. Eyeblink characteristics are frequently used as physiological indicators for this purpose. In this paper, we describe an efficient and robust eyeblink detection algorithm based on nonlinear analysis of the electrooculogram (EOG) signal. The performance of the algorithm was evaluated via data analysis results of several benchmark test sets in comparison with another eyeblink detection algorithm.