I received my B.S. in psychology from Iowa State University in 1990, and then received my Ph.D. in cognitive psychology from the University of Massachusetts, Amherst in 1997. Before starting as an Assistant Professor at the University of Pittsburgh, I worked for 3 years as a post-doctoral research fellow at Carnegie Mellon University. I consider myself to be a cognitive scientist because of my broad interest in the mind/brain and because I use a variety of methods in my research.

I am mainly interested in answering the question: What determines when and where your eyes move as you read this sentence? Although this question may seem esoteric or narrow in its focus, I would argue that-to really answer this question-it is not only necessary to understand a fair bit about cognition, perception, and oculomotor control, but it is also necessary to understand how each of these activities are coordinated to guide the eyes during reading. The question thus provides an ideal window from which to examine a variety of theoretical issues related to cognition, perception, and motor control. The question is also of practical consequence because the use of eye-tracking technology to understand cognition presupposes a fairly tight coupling between the mind and the eyes-an assumption that many have questioned (Feng, 2003; Reilly & O'Regan, 1998; Yang & McConkie, 2001). By studying eye movements in the context of reading, I hope to elucidate the eye-mind link.

My research to date has largely been theoretical and has focused on developing a computational models of eye-movement control (Reichle & Perfetti, 2003; Reichle, Pollatsek, Fisher, & Rayner, 1998; Reichle, Pollatsek, & Rayner, 1999, in press; Reichle & Rayner, 2002). These models describe how linguistic processing, attention, visual acuity constraints, and the oculomotor system jointly determine when and where the eyes move during reading. In addition to providing an answer to the question of what guides eye movements during reading, the models have been used as analytical tools for investigating a variety of other questions. For example, the models-which have only a single covert state of visual attention-provide an account of data that seemingly require two attention states (Reichle & Nelson, in press). The models have also been used to examine various linguistic phenomena, such as how frequency and predictability affect word identification (Rayner, Ashby, Pollatsek, & Reichle, in press), and how poly-morphemic words (Pollatsek, Reichle, & Rayner, 2003) and words with multiple meanings (Reichle, Rayner, & Pollatsek, in preparation) are identified. Finally, the models have been used to generate predictions about the relative time course of lexical processing and eye movements in a semantic judgment task; these predictions are currently being evaluated using ERP (Reichle, Tokowicz, & Perfetti, in preparation).