The ability to accurately predict object weight is essential for skilled manipulation and recent studies suggest that such prediction is based, in part, on learned size-weight maps associated with families of objects. Weight prediction based on size-weight maps is also involved when judging weights; there is strong evidence that weight judgments are biased by expected weight, based on size. This bias is revealed by the size-weight illusion (SWI) whereby the smaller of two equally weighted and otherwise similar objects is judged to be heavier because it is heavier than expected based on its size. The overall aim of the current set of studies was to examine how size-weight maps for different families of objects are organized and represented at the perceptual and sensorimotor levels. We found that distinct and independent size-weight maps, used to predict weight, were used when lifting objects and judging their weights. At the perceptual level, interference between size-weight maps for the different sets of cubes was observed; participants could learn the inverted size-weight relationship for the green cubes when experienced alone but not when experienced along with the black cubes with a normal size-weight relationship. However, about half of participants learned to scale lift forces accurately for both sets of cubes indicating that the sensorimotor system can learn, without interference, opposite size-weight maps. We further investigated why not all participants learned to accurately scale their lift forces and found that learning to lift objects with different and arbitrary size weight maps involves visuomotor working memory resources. Moreover, an outside task that steals attentional resources can interfere even after previous learning of the size-weight maps. / Thesis (Master, Psychology) -- Queen's University, 2011-09-30 12:51:49.413

This thesis explores the application of Artificial Intelligence (AI), machine learning (ML), and statistical analysis to enhance agricultural practices, focusing on both livestock man- agement and plant biology. The first part investigates automated weight prediction of beef cattle using computer vision techniques, including YOLOv9 and InternImage with Cas- cade R-CNN for precise image segmentation. Advanced feature extraction methods utilizing ResNet, DenseNet, and ResNeXt are employed to develop ML and deep learning (DL) mod- els, providing a non-invasive alternative to traditional weight measurement techniques. The second part examines the regulation of the auxin response in Arabidopsis plants, focusing on epistatic interactions among auxin receptors. Through experimental assays and com- putational modeling, the study reveals synergistic effects that influence plant growth and development. The third part of the thesis characterizes the transcriptional specificity medi- ated by plant hormones using comprehensive data analysis, uncovering key insights into the gene regulation mechanisms influenced by auxin. Overall, the research integrates AI, ML, DL, and statistical methods to address critical challenges in agriculture and plant science, demonstrating improved predictive accuracy, enhanced understanding of hormonal signaling, and potential advancements in crop productivity and livestock management / Master of Science / This research applies advanced deep learning technology and statistical analysis to improve farming practices and plant science. The study first focuses on helping farmers predict the weight of cows using cameras and AI software instead of traditional scales, providing a faster and less stressful method for both animals and farmers. Next, the research investigates how plant hormones, specifically auxin, interact with certain proteins to regulate plant growth. Understanding these interactions helps scientists predict plant responses and enhance crop yields. Lastly, the study examines how these hormones influence specific genes, using data analysis to reveal how plants control their growth at a molecular level. By combining AI, biology, and statistical methods, this work offers new tools for improving livestock manage- ment and understanding plant growth, ultimately contributing to better farming practices and increased agricultural productivity.