The effect of sexist attitudes on the perception of visual artists by community college and university students

This study compared the effects of sexist labeling on the perceptions of visual artists by the community college and university students and determined their sex role orientation. The 370 students were shown five slides of an artist's works and were given six versions of an artist's biography. It contained embedded sexual labeling (woman, girl, person/ she, man, guy, person/he). The Artist Evaluation Questionnaire was administered to the female and male community college and university students that required the students to evaluate the female and male artists on several aspects of affective and cognitive measures. The questionnaire consisted of 9 items that had to be rated by the participants. In addition, the students filled out the Demographic Questionnaire and the BEM Sex Role Inventory, titled the Attitude Questionnaire. The Analysis of Variance testing procedures were administered to analyze the responses. The results disclosed gender differences in students' ratings. The female artist's work, when the artist was referred to by the neutral sexual label, "person", received significantly higher ratings from the female students. The male students gave the female artist her highest ratings when she was referred to by the low status sexual label, "girl". Both sexes did not express statistically significant preferences for any of the male sexual labels. Gender difference became apparent when it was found that female students rated both sexes equally, and their ratings were lower than those of the male students. The male students rated the female artist's work higher than the work of the male artist. The analysis of the sex role inventory questionnaire revealed the absence of the feminine (expressive) and masculine (instrumental) personalities among the students. The personalities of almost all the students were androgynous, with a few within the range of the near feminine, and a few within the range of the near masculine. The study reveals that there are differences in perception of sexual labels among the community college and university students.

Using the Big -Five personality factors in the Minnesota Multiphasic Personality Inventory, California Psychological Inventory, and Inwald Personality Inventory to predict police performance

The purpose of this study was to determine the degree to which the Big-Five personality taxonomy, as represented by the Minnesota Multiphasic Personality Inventory (MMPI), California Psychological Inventory (CPI), and Inwald Personality Inventory (IPI) scales, predicted a variety of police officer job performance criteria. Data were collected archivally for 270 sworn police officers from a large Southeastern municipality. Predictive data consisted of scores on the MMPI, CPI, and IPI scales as grouped in terms of the Big-Five factors. The overall score on the Wonderlic was included in order to assess criterion variance accounted for by cognitive ability. Additionally, a psychologist's overall rating of predicted job fit was utilized to assess the variance accounted for by a psychological interview. Criterion data consisted of supervisory ratings of overall job performance, State Examination scores, police academy grades, and termination. Based on the literature, it was hypothesized that officers who are higher on Extroversion, Conscientiousness, Agreeableness, Openness to Experience, and lower on Neuroticism, otherwise known as the Big-Five factors, would outperform their peers across a variety of job performance criteria. Additionally, it was hypothesized that police officers who are higher in cognitive ability and masculinity, and lower in mania would also outperform their counterparts. Results indicated that many of the Big-Five factors, namely, Neuroticism, Conscientiousness, Agreeableness, and Openness to Experience, were predictive of several of the job performance criteria. Such findings imply that the Big-Five is a useful predictor of police officer job performance. Study limitations and implications for future research are discussed. ^

Learning Data-Driven Models of Non-Verbal Behaviors for Building Rapport Using an Intelligent Virtual Agent

There is a growing societal need to address the increasing prevalence of behavioral health issues, such as obesity, alcohol or drug use, and general lack of treatment adherence for a variety of health problems. The statistics, worldwide and in the USA, are daunting. Excessive alcohol use is the third leading preventable cause of death in the United States (with 79,000 deaths annually), and is responsible for a wide range of health and social problems. On the positive side though, these behavioral health issues (and associated possible diseases) can often be prevented with relatively simple lifestyle changes, such as losing weight with a diet and/or physical exercise, or learning how to reduce alcohol consumption. Medicine has therefore started to move toward finding ways of preventively promoting wellness, rather than solely treating already established illness. Evidence-based patient-centered Brief Motivational Interviewing (BMI) interven- tions have been found particularly effective in helping people find intrinsic motivation to change problem behaviors after short counseling sessions, and to maintain healthy lifestyles over the long-term. Lack of locally available personnel well-trained in BMI, however, often limits access to successful interventions for people in need. To fill this accessibility gap, Computer-Based Interventions (CBIs) have started to emerge. Success of the CBIs, however, critically relies on insuring engagement and retention of CBI users so that they remain motivated to use these systems and come back to use them over the long term as necessary. Because of their text-only interfaces, current CBIs can therefore only express limited empathy and rapport, which are the most important factors of health interventions. Fortunately, in the last decade, computer science research has progressed in the design of simulated human characters with anthropomorphic communicative abilities. Virtual characters interact using humans’ innate communication modalities, such as facial expressions, body language, speech, and natural language understanding. By advancing research in Artificial Intelligence (AI), we can improve the ability of artificial agents to help us solve CBI problems. To facilitate successful communication and social interaction between artificial agents and human partners, it is essential that aspects of human social behavior, especially empathy and rapport, be considered when designing human-computer interfaces. Hence, the goal of the present dissertation is to provide a computational model of rapport to enhance an artificial agent’s social behavior, and to provide an experimental tool for the psychological theories shaping the model. Parts of this thesis were already published in [LYL+12, AYL12, AL13, ALYR13, LAYR13, YALR13, ALY14].

Learning data-driven models of non-verbal behaviors for building rapport using an intelligent virtual agent

There is a growing societal need to address the increasing prevalence of behavioral health issues, such as obesity, alcohol or drug use, and general lack of treatment adherence for a variety of health problems. The statistics, worldwide and in the USA, are daunting. Excessive alcohol use is the third leading preventable cause of death in the United States (with 79,000 deaths annually), and is responsible for a wide range of health and social problems. On the positive side though, these behavioral health issues (and associated possible diseases) can often be prevented with relatively simple lifestyle changes, such as losing weight with a diet and/or physical exercise, or learning how to reduce alcohol consumption. Medicine has therefore started to move toward finding ways of preventively promoting wellness, rather than solely treating already established illness.^ Evidence-based patient-centered Brief Motivational Interviewing (BMI) interventions have been found particularly effective in helping people find intrinsic motivation to change problem behaviors after short counseling sessions, and to maintain healthy lifestyles over the long-term. Lack of locally available personnel well-trained in BMI, however, often limits access to successful interventions for people in need. To fill this accessibility gap, Computer-Based Interventions (CBIs) have started to emerge. Success of the CBIs, however, critically relies on insuring engagement and retention of CBI users so that they remain motivated to use these systems and come back to use them over the long term as necessary.^ Because of their text-only interfaces, current CBIs can therefore only express limited empathy and rapport, which are the most important factors of health interventions. Fortunately, in the last decade, computer science research has progressed in the design of simulated human characters with anthropomorphic communicative abilities. Virtual characters interact using humans’ innate communication modalities, such as facial expressions, body language, speech, and natural language understanding. By advancing research in Artificial Intelligence (AI), we can improve the ability of artificial agents to help us solve CBI problems.^ To facilitate successful communication and social interaction between artificial agents and human partners, it is essential that aspects of human social behavior, especially empathy and rapport, be considered when designing human-computer interfaces. Hence, the goal of the present dissertation is to provide a computational model of rapport to enhance an artificial agent’s social behavior, and to provide an experimental tool for the psychological theories shaping the model. Parts of this thesis were already published in [LYL+12, AYL12, AL13, ALYR13, LAYR13, YALR13, ALY14].^