Graduate Training in Cognitive Psychology
Our Cognitive area is strongly integrated with the Developmental, Neuroscience, and Social areas, providing students with a uniquely interdisciplinary training in the Cognitive Sciences. In addition to intersections with these core areas, cognitive faculty stress computational methods, providing training in cutting edge areas of cognitive modeling, programming, statistical modeling, and machine learning. Faculty labs investigate topics such as learning, decision making, memory, attention, language, reading, visual perception, and reasoning.
In addition to the areas and computational opportunities described above, students will also have access to numerous empirical tools and resources to investigate these topics. These include training in neural (fMRI, EEG, ERP), eye-tracking, and physiological techniques. Our faculty are part of the Rutgers University Brain Imaging Center (RUBIC), which houses a state-of-the-art 3T Siemens Magnetic Resonance Imaging (MRI) brain scanner. Faculty are also involved in the Institute for Data Science, with access to a Rutgers-dedicated supercomputing cluster that supports data-intensive computations.
Currently our Cognitive area includes:
Dr. Elizabeth Bonawitz, Associate Professor of Psychology, (cognitive development and computational modeling, causal and social inference, conceptual change). Dr. Bonawitz directs the Computational Cognitive Development Lab and has active projects investigating how infants, preschoolers, and adults revise their beliefs about the world. Combining tools from probability theory, machine learning, and AI with empirical studies of children’s development, she asks: how do children take noisy, ambiguous data from the environment to form rich, abstract, causal representations of the world? In a recent set of studies, she is exploring how children discover relevant hypotheses (explanations) for novel events, how assumptions about others and the environment change the process of hypothesis discovery, and how this process changes over development.
Dr. Mauricio Delgado, Professor and Chair, (Behavioral and neural correlates of reward-related processing, with an emphasis on how the affective properties of outcomes or feedback influence choice behavior. using neuroimaging and behavioral and psychophysiological methods.) Research in the lab focuses on the interaction of emotion and cognition in the human brain during learning and decision making. We use functional magnetic resonance imaging (fMRI) in conjunction with physiological and behavioral measures to investigate: 1) How the human brain learns about value (rewards and punishments); 2) How the brain uses this information to make decisions and guide behavior; and 3) How humans control or regulate their emotions to facilitate learning and decision-making. Studies range from simple processes that can be mapped on to current animal studies (e.g., learning that a stimulus predicts a reward), to more complex processes displayed during social interaction in everyday behavior (e.g., learning to trust someone during an economic exchange).
Dr. Alan Gilchrist, Professor, (Visual cognition; surface-color perception). I study visual perception, especially the perception of surface color, and especially the black-white dimension. Vision is known to be based on the image projected onto the retina, but the problem of how to assign black, white and gray values to surfaces represented in that image remains unsolved, in human vision as in computer vision. Because of variations in many factors such as the background of a surface and the lighting conditions, the perception of any one specific surface color can be associated with many patterns of local stimulation at the retina. The goal of the work is to describe the software (not the hardware, or wetware) used by the visual system to decode the retinal image. The primary method is psychophysics. Naive observers are exposed to displays specially constructed so that competing theories make opposing predictions of what observers will see. The observer reports, typically involving matches made using a color chart, are then used to evaluate theories. In my lab we have approached this problem in two ways. In earlier work, an inverse-optics approach was taken in which we attempted to determine the computations necessary to recover objective properties like surface color. More recent work has focused on the pattern of errors shown by human observers when judging surface colors. These errors are systematic, not random, and the work is based on the assumption that the pattern of errors is the signature of the software used to decode the retinal image.
Dr. William Graves, Associate Professor, (Functional brain imaging of language and reading. Tracking in both space (using magnetic resonance imaging) and time (using magnetoencephalography) how the brain computes sound and meaning from what we see.) In my lab we research the neurobiology of language, with particular emphasis on reading. We seek to answer questions such as, how does the brain translate concepts into speech, and how does the brain map from letter strings to sound and meaning? By understanding these fundamental processes, we hope to ultimately help those with language or reading disorders, such as aphasia or dyslexia.
Dr. Stephen José Hanson, Professor & RUBIC Director, (Computational neuroimaging, memory and learning, connectionist models, categorization, big data modeling). My research focus is on memory & learning, categorization, connectionist models, neural networks, and more generally cognitive and perceptual modeling.
Dr. Vanessa LoBue, Associate Professor of Psychology, (infancy, emotional development, threat perception). Dr. LoBue directs the Child Study Center and her research program investigates human behavioral responses to emotionally valenced stimuli—specifically to negative or threatening stimuli—and the mechanisms guiding the development of these responses. In one line of research, she found that humans perceive the presence of threatening stimuli very quickly, and that rapid detection begins in infancy. However, these biases can be learned, they can change over the course of development, and may reflect a broad spectrum of individual differences. Further, in a second line of research, she found that avoidance responses to threats do not develop until later in childhood, and are dependent on learning. Her current work builds on these findings to ask whether early perceptual biases for threat contribute to maladaptive avoidance responses, such as those associated with the development of fear and anxiety, and how cognition contributes to children’s learning of adaptive avoidance behaviors, such as avoidance of contagious people or contaminated objects.
Dr. Miriam Rosenberg-Lee, Assistant Professor of Psychology (functional neuroimaging of mathematical cognition; cognitive development; learning disabilities; cognition in autism spectrum disorders; learning and reasoning.). Dr. Rosenberg-Lee directs the Mathematics, Reasoning and Learning Lab and has active projects investigating how children, adolescents and adults learn mathematical information. Combining functional neuroimaging with outside the scanner learning programs, she asks: what brain activity patterns do proficient learners display? How are these patterns different in children with mathematical learning disabilities and autism spectrum disorders? What types of learning programs are most effective in different populations of learners?
Dr. Elizabeth Tricomi, Associate Professor, (Functional neuroimaging of learning and decision making; the influences of affective information on cognitive processing, neural basis of goal-directed behavior). Broadly speaking, my research focuses on the influences of affective information on cognitive processing in the brain. The affective qualities of our experience not only produce subjective feelings that may be positive or negative, but also provide information that allows us to shape future behavior. To understand how the consequences of one’s decisions can be used to determine future actions, I use functional magnetic resonance imaging (fMRI) to investigate the role of the brain’s reward processing system in feedback-based learning. My work examines contextual influences on learning and decision making, and the neural systems that underlie these processes. For example, my research indicates that the sensitivity of the striatum, a region in the basal ganglia, to reward-related information depends on factors such as whether one feels a sense of agency in producing an outcome or whether a habit has been formed after extensive experience. This research has important implications for understanding how cognitive processes such as learning and decision making are carried out in the normal brain, as well as for understanding how impairments of the brain’s reward processing system may give rise to disorders such as addiction and other compulsive behaviors.