The Validity and Comparison of Two Affiliation Tasks in Schizophrenia

Negative symptoms are related to worse psychosocial functioning in schizophrenia. The current study evaluates two behavioral affiliation tasks—the video-based Social Affiliation Interaction Task (SAIT) and the in-vivo Conversation Task (CT)—and explores whether behavioral ratings of social affiliation are associated with negative symptoms and community functioning. Participants, 20 with schizophrenia/schizoaffective disorder (SZ) and 35 healthy controls (HC), completed both tasks and measures of negative symptoms and functioning. SZ evidenced lower behavioral affiliation on the SAIT compared to HC. There were no group differences in behavioral affiliation on the CT. Within groups, behavioral affiliation was not correlated between tasks or with symptoms and functioning. Across groups, behavioral affiliation from the SAIT was correlated with symptoms and functioning. Post hoc analyses revealed higher ratings of positive facial expression and valence in the CT for HC compared to SZ. Results suggest that the method of assessing behavioral affiliaton may influence research findings.

Warning Against Faking: Boon or Bane? Effects on Test Score Validity

Previous research found personality test scores to be inflated on average among individuals who were motivated to present themselves in a desirable fashion in high stakes situations, such as during the employee selection process. One apparently effective way to reduce the undesirable test score inflation in such situations was to warn participants against faking. This research set out to investigate whether warning against faking would indeed affect personality test scores in the theoretically expected fashion. Contrary to expectations, the results did not support the hypothesized causal chain. Results across three studies show that while a warning may lower test scores in participants motivated to respond desirably (i.e., to fake), the effect of warning on test scores was not fully mediated by: a reduction in motivation to do well and self-reports of exaggerated responses in the personality test. Theoretical and practical implications are discussed.

CONCURRENT LOCALIZATION AND MAPPING WITH SONAR SENSORS AND CONSIDERATION OF VEHICLE MOTION

Simultaneous Localization and Mapping (SLAM) is a procedure used to determine the location of a mobile vehicle in an unknown environment, while constructing a map of the unknown environment at the same time. Mobile platforms, which make use of SLAM algorithms, have industrial applications in autonomous maintenance, such as the inspection of flaws and defects in oil pipelines and storage tanks. A typical SLAM consists of four main components, namely, experimental setup (data gathering), vehicle pose estimation, feature extraction, and filtering. Feature extraction is the process of realizing significant features from the unknown environment such as corners, edges, walls, and interior features. In this work, an original feature extraction algorithm specific to distance measurements obtained through SONAR sensor data is presented. This algorithm has been constructed by combining the SONAR Salient Feature Extraction Algorithm and the Triangulation Hough Based Fusion with point-in-polygon detection. The reconstructed maps obtained through simulations and experimental data with the fusion algorithm are compared to the maps obtained with existing feature extraction algorithms. Based on the results obtained, it is suggested that the proposed algorithm can be employed as an option for data obtained from SONAR sensors in environment, where other forms of sensing are not viable. The algorithm fusion for feature extraction requires the vehicle pose estimation as an input, which is obtained from a vehicle pose estimation model. For the vehicle pose estimation, the author uses sensor integration to estimate the pose of the mobile vehicle. Different combinations of these sensors are studied (e.g., encoder, gyroscope, or encoder and gyroscope). The different sensor fusion techniques for the pose estimation are experimentally studied and compared. The vehicle pose estimation model, which produces the least amount of error, is used to generate inputs for the feature extraction algorithm fusion. In the experimental studies, two different environmental configurations are used, one without interior features and another one with two interior features. Numerical and experimental findings are discussed. Finally, the SLAM algorithm is implemented along with the algorithms for feature extraction and vehicle pose estimation. Three different cases are experimentally studied, with the floor of the environment intentionally altered to induce slipping. Results obtained for implementations with and without SLAM are compared and discussed. The present work represents a step towards the realization of autonomous inspection platforms for performing concurrent localization and mapping in harsh environments.