| My dissertation focuses on developing computational models of eye movements for understanding how cognitive processes (e.g., visual information processing, word recognition, attention, and oculomotor control) can work together to perform a complex everyday task (e.g., reading or scene viewing). In a theoretical framework, many biologically-inspired computational methods were used and found psychologically plausible to predict human behaviors and simulate human cognition. For eye movements in reading, I proposed models of visual encoding, word identification, and semantic integration in contexts. Using singular value decomposition (SVD), I was able to predict the most important strokes for Chinese character recognition (Wang, Angele, Schotter, Yang, Simovici, Pomplun, & Rayner, under revision). Furthermore, I used a vector space model (latent semantic analysis, LSA) to explain how readers rate the semantic transparency of English and Chinese compound words. A linear regression model was then developed to estimate contextual predictability during reading (Wang, Chen, Ko, Pomplun, & Rayner, 2010), and a connectionist model was used to represent the activations of concepts in working memory (Plummer, Wang, Tzeng, Pomplun, & Rayner, 2012).;My interests in reading and vision studies provided interdisciplinary research opportunities, which I pursued by applying methods and concepts from reading research to the viewing of real-world scenes. Regarding eye movements in natural scene viewing, I studied when and where we fixate, resulting in a model for gaze transition using LSA (Wang, Hwang, & Pomplun, 2010; Hwang, Wang, & Pomplun, 2011). The final part of my dissertation focuses on studying how texts attract attention in natural scene viewing (Wang & Pomplun, 2012) compared to attraction by saliency and edge density. I have also developed a model of this effect of texts on visual attention that includes an automatic text detector (Wang, Lu, Lim, & Pomplun, 2012).;The results of my doctoral thesis will broaden our understanding of low-level and higher-level cognitive processing as well as cultural differences during reading and real-world scene viewing. The findings should eventually lead to practical applications, e.g., contribute to the development of more effective automatic text detectors, or making a great difference to visually challenged people's lives by assisting them in reading and scene viewing. |
