The fundamental premise of my work is that computational models from cognitive psychology and cognitive science can be adapted to provide testable process models of decision-making phenomena and optimized to support the decision-making of professionals. I direct the Decision Processes Laboratory (DPL). The DPL utilizes a range of experimental methodologies (behavioral, eye-tracking, EEG) and computational techniques (statistical, mathematical, neural networks) to investigate decision-making phenomena.
The overarching theme of my research is interactionism—the belief that human behavior is inherently a function of individuals and the situations they experience. Thus, my specific streams of research focus on better understanding individuals and situations at work in mutually commensurate ways.
Cognitive control refers to the set of processes by which we direct our actions toward a specific goal. At the most basic level, control processes allow us to translate a presented stimulus into an appropriate motor action. However, these processes and representations quickly become more complex when trying to understand more involved behaviors such as learning peoples names or watching and understanding films.
My research examines the role of motivation and self-regulation in work and achievement settings. Past projects include work on how goals affect resource allocation during learning and performance and the role of self-regulation in job search and reemployment following job loss.
My research interests are in the development and application of item response theory (IRT) models to measure psychological constructs. Over the past two decades, I have developed a family of polytomous IRT models to unfold responses to test or questionnaire items. These unfolding models imply higher item scores to the extent that an individual is located close to an item on a unidimensional latent continuum. Unfolding item response models can be used to measure attitudes using responses from traditional Likert or Thurstone scales.
I am broadly interested in high-level aspects of perceptual decision-making. My research attempts to elucidate the brain mechanisms that influence what we perceive, as well as build computational models that explain current findings and lead to novel testable predictions. Specific topics include: the role of the prefrontal cortex in modulating the perceptual process, the computational principles behind attention and expectation, the mechanisms that allow us metacognitive insight into the accuracy of our perceptual decisions, and Bayesian models of perception as inference.
My area of expertise is in the cognitive neuroscience of aging. My specialties include the application of functional and structural neuroimaging methods to understand cognitive and brain aging as well as behavioral endocrinology. I have devoted much of my career to the study of the effects of steroid hormones on behavior and brain function. Among my contributions to this field are studies assessing the effect of gonadal steroids on spatial cognition, hemispheric asymmetry and interhemispheric communication.
My interests span modern psychometric methods (e.g., item response theory), cognitive and intelligence, and quantitative methods. My main research program has been to integrate cognitive theory into psychometric models and test design. To this goal, I have been developing new item response theory models and conducting empirical research on the cognitive basis of an individual's responses. Recently, this effort has lead to the exciting possibility of "tests without items".