My recent work involves the study of mindfulness as a trait and a state. See my book on the effects of mindfulness on brain, mind, and life. In our research, we're interested in a new, broader definition of mindfulness, which includes self-awareness, self-regulation, as well as self-transcendence. We're particularly interested in how these aspects foster not just personal wellbeing, but can also be of benefit to others, by fostering wisdom, virtue, compassion, and social justice.
Dr. Christopher Stanzione is an Educational Psychologist who is interested in the scientific study of human learning. He focuses on how students best acquire new skills and knowledge as they learn, and helps develop and refine instructional methods and materials to enhance the classroom learning experience. Moreover, Dr. Stanzione is interested in studying language and cognitive development in both at-risk and deaf and hard-of-hearing children.
I received my degree from Washington University in St. Louis. After moving around a bit (Binghamton University and Stanford University)I arrived at Georgia Tech in 2001. I'm a member of the Cognition and Brain Science, Cognitive Aging, and Quantitative Psychology areas of the department.
The main focus of Dr. Weiss’ research is on understanding the experience of working, especially in an immediate, first person sense. This includes interest in emotional states, focus of attention, episodic structures of personal experience, and sense of self and agency at work.
More information can be found on the website of the Work Experience Lab.
My overarching goal is to ensure that technology is developed with the end user in mind. All aspects of design, implementation, adoption, and use of a system or device can be enhanced by considering the perceptual, cognitive, and social needs and abilities of those who will use it. Research in my Sonification Lab focuses on three main areas:
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.
I work on the 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 areas of emphasis include visual metacognition, neural network models of vision, high-level processes like expectation and attention, and the role of large-scale brain networks in cognition.
I study adult learning, motivation, and development related to workers and their careers. As 21st century changes in technologies, demographics, and the global economy continue, their impacts are increasingly felt on what people do at work, their career paths, and shifting employment trends.
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 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.