Cognitive Behavior Therapy for Obsessive-Compulsive Behavior in Children with Autism Spectrum Disorder: A Comparison of Hierarchical Linear Modelling and Visual Analysis

Behavioral researchers commonly use single subject designs to evaluate the effects of a given treatment. Several different methods of data analysis are used, each with their own set of methodological strengths and limitations. Visual inspection is commonly used as a method of analyzing data which assesses the variability, level, and trend both within and between conditions (Cooper, Heron, & Heward, 2007). In an attempt to quantify treatment outcomes, researchers developed two methods for analysing data called Percentage of Non-overlapping Data Points (PND) and Percentage of Data Points Exceeding the Median (PEM). The purpose of the present study is to compare and contrast the use of Hierarchical Linear Modelling (HLM), PND and PEM in single subject research. The present study used 39 behaviours, across 17 participants to compare treatment outcomes of a group cognitive behavioural therapy program, using PND, PEM, and HLM on three response classes of Obsessive Compulsive Behaviour in children with Autism Spectrum Disorder. Findings suggest that PEM and HLM complement each other and both add invaluable information to the overall treatment results. Future research should consider using both PEM and HLM when analysing single subject designs, specifically grouped data with variability.

Imagery or video feedback : which is the "route" to strategic improvement?

The implementation of imagery and video feedback programs has become an important tool for aiding athletes in achieving peak performance (Halliwell, 1990). The purpose of the study was to determine the effect of strategic imagery training and video feedback on immediate performance. Participants were two university goaltenders. An alternating treatment design (ATD; Barlow & Hayes, 1979; Tawney & Gast, 1984) was employed. The strategies were investigated using three plays originating from the right side by a right-handed shooting defenceman from the blueline. The baseline condition consisted of six practices and was used to establish a stable and "ideal" measure of performance. The intervention conditions included alternating the use of strategic imagery (Cognitive general; Paivio, 1985) and video feedback. Both participants demonstrated an increase in the frequency of Cognitive general use. Specific and global performance measures were assessed to determine the relative effectiveness of the interventions. Poor inter-rater reliability resulted in the elimination of specific performance measures. Consequently, only the global measure (i.e., save percentage) was used in subsequent analyses. Visual inspection of participant save percentage was conducted to determine the benefits of the intervention. Strategic imagery training resulted in performance improvements for both participants. Video feedback facilitated performance for Participant 2, but not Participant 1. Results are discussed with respect to imagery and video interventions and the challenges associated with applied research. KEYWORDS: imagery, video, goaltenders, alternating treatment design.

Exploring an emotion intervention for preschoolers with autism spectrum disorder /

The purpose of this multiple case study was 1) to explore the effectiveness of an emotions recognition program for preschoolers with Autism Spectrum Disorders (ASD), and 2) to investigate one parent's perception of the emotions program. To address these objectives, the emotion unit scores of 7 preschoolers with ASD aged 3 to 5 years old (1 female, 6 males) were graphed and analyzed using visual inspection. In addition, the mother of 1 participant was interviewed to explore her perceptions of the emotions program and emotional learning. Overall, results revealed that participants' emotion recognition scores increased over the course of the emotions unit. The parent reported improvements in her son's expression and understanding of emotion, but noted that he continued to have difficulty with regulation of emotion. Implications for theory, education, and future research are discussed.

Automatic Structure Generation using Genetic Programming and Fractal Geometry

Three dimensional model design is a well-known and studied field, with numerous real-world applications. However, the manual construction of these models can often be time-consuming to the average user, despite the advantages o ffered through computational advances. This thesis presents an approach to the design of 3D structures using evolutionary computation and L-systems, which involves the automated production of such designs using a strict set of fitness functions. These functions focus on the geometric properties of the models produced, as well as their quantifiable aesthetic value - a topic which has not been widely investigated with respect to 3D models. New extensions to existing aesthetic measures are discussed and implemented in the presented system in order to produce designs which are visually pleasing. The system itself facilitates the construction of models requiring minimal user initialization and no user-based feedback throughout the evolutionary cycle. The genetic programming evolved models are shown to satisfy multiple criteria, conveying a relationship between their assigned aesthetic value and their perceived aesthetic value. Exploration into the applicability and e ffectiveness of a multi-objective approach to the problem is also presented, with a focus on both performance and visual results. Although subjective, these results o er insight into future applications and study in the fi eld of computational aesthetics and automated structure design.

Real-Time Automatic Object Classification and Tracking using Genetic Programming and NVIDIA R CUDA TM

Genetic Programming (GP) is a widely used methodology for solving various computational problems. GP's problem solving ability is usually hindered by its long execution times. In this thesis, GP is applied toward real-time computer vision. In particular, object classification and tracking using a parallel GP system is discussed. First, a study of suitable GP languages for object classification is presented. Two main GP approaches for visual pattern classification, namely the block-classifiers and the pixel-classifiers, were studied. Results showed that the pixel-classifiers generally performed better. Using these results, a suitable language was selected for the real-time implementation. Synthetic video data was used in the experiments. The goal of the experiments was to evolve a unique classifier for each texture pattern that existed in the video. The experiments revealed that the system was capable of correctly tracking the textures in the video. The performance of the system was on-par with real-time requirements.