| This dissertation outlines the importance of understanding how hierarchically organized categories of objects are represented and used to complete novel tasks. Four experiments are included with the goal of expanding on the current field of research. These experiments explored the behavioral factors impacting visual search efficiency, the neural correlates of categorical search, and how to best model this behavior in order to make further testable predictions. Experiment one builds off of previous research done on how hierarchical categorical cues impact search by manipulating the set size of the search display. Experiment two focuses on identifying the characteristics of the N2pc EEG component when presented with hierarchical cues. Experiment three compares and contrasts the ability of the Category-Consistent Feature (CCF) model and Multi-dimensional scaling methods to predict performance on a category verification task. Experiment four extends the CCF model from predicting overall trends in behavioral data to making predictions about search efficiency at the level of individual trials. Throughout this dissertation, the importance of understanding more about these areas of research are highlighted. Given the ubiquity of categorical search in everyday life, there is a need to further our modelling efforts to generate new predictions and avenues of research. |
