|Perceptual Expertise Network Labs|
Indicates that the lab participates in The Preuss School's Internship Program
Category Laboratory, Vanderbilt
Thomas Palmeri, PI
Our laboratory studies perceptual categorization, perceptual decision making, and perceptual expertise using a combination of behavioral experiments, computational modeling, and cognitive neuroscience techniques.
Object Perception Laboratory, Vanderbilt
Isabel Gauthier, PI
The Object Perception Lab studies how humans perceive and sometimes develop expertise with many different kids of object categories (faces, cars, letters, musical notes, and a host of other familiar and novel objects). We use a combination of methods to investigate, in a dynamic fashion, how representations and processing strategies change during the acquisition of expertise with different kinds of tasks and objects. One influential working hypothesis is a lot of our research is that category-specific effects are automatized task effects, associated with an object category through experience. We investigate both the functional and neural locus of such expertise effects.
"Connect Head" by Matt Crump
Cognitive Neuroscience Lab, Carnegie Mellon
Marlene Behrmann, PI
Despite the fact that visual scenes may contain multiple objects and people, humans can recognize the objects and individuals with ease and accuracy. Research in my lab focuses on studying how this is achieved - what are the necessary psychological processes and representations that underlie abilities such as object segmentation and recognition, face recognition, mental imagery, reading and writing and spatial attention? Although these questions are asked within the framework of information-processing models used in cognitive psychology, I am also interested in identifying the neural mechanisms which are responsible for these complex abilities.
A final thread to my research is to conduct rehabilitation studies with the brain damaged subjects in order to treat the observed deficit. Carefully planned rehabilitation studies provide valuable information which can shed light on the mechanisms underlying visual cognition.
Gary's Unbelievable Research Unit (GURU), UCSD
The Artificial Intelligence Group at UCSD engages in a wide range of theoretical and experimental research. Areas of particular strength include machine learning, probabilistic inference, neural computation, and cognitive modeling. Within these areas, students and faculty also pursue real-world applications to problems in computer vision, speech and audio processing, information retrieval, bioinformatics, brain-computer interfaces, and computer systems and networking. The Artificial Intelligence Group is part of a larger campus-wide effort in Computational Statistics and Machine Learning (COSMAL). Interdisciplinary collaborations are strongly supported and encouraged.
Cognitive ERP Lab, Colorado, Boulder
Tim Curran, PI
My research focuses on human learning and memory. I approach these topics from a cognitive neuroscience perspective with the goals of understanding the characteristics of mental processes and how they are realized within the brain. Most of my current research uses measures of brain electrical activity (ERPs) to study the brain processes that underlie recognition memory. In particular, ERPs are being used to dissociate the influences of recollection and familiarity on recognition memory. Other ongoing research, in collaboration with the Perceptual Expertise Network , uses ERPs to investigate the manner in which visual object recognition processes are influenced by expertise.
Sheinberg Lab, Brown
David Sheinberg, PI
By combining behavioral and physiological studies in non-human primates, we hope to better understand the neural basis of high level visual cognition. Our emphasis is on explaining how the concerted activity of single neurons empowers the primate visual system to explore complex environments and recognize complex objects, and on understanding how experience affects these processes.
Vis Cog Lab, University of Victoria
Jim Tanaka, PI
In the Visual Cognition Lab (VizCogLab) at the University of Victoria, we examine the cognitive and neurophysiological processes underlying object and face recognition. A central question in our research focuses on the role that experience plays in the way we recognize objects. We are currently approaching this question from two perspectives. First, we are studying the recognition processes of object experts, such as expert biologists, dog experts, and bird experts, to see whether experts recognize objects in their domain of expertise differently than novices. Second, we are examining the visual processes that mediate face recognition. It has been claimed that face recognition is a type of recognition in which all people are experts. As a form of expertise, we might expect face recognition to share similar processes as those found for other types of expert object recognition (e.g.,birdwatching, car identification). In our lab, we address these questions using converging experimental approaches that include behavioural measures, event-related potentials and the study of brain-damaged patients. In more recent work, we are applying the principles of perceptual expertise to teach children with autism how to recognize faces. By drawing parallels and contrasts between face recognition and expert object recognition, we hope to better understand how experience shapes the way we perceive and recognize objects in the world.
Tarr Lab, Brown
Michael Tarr, PI
Our research explores how humans visually learn, process, and recognize objects. Methods include psychophysics, fMRI, and computational modeling, as well as collaborations with other labs using ERPs, neuropsychological case studies, and neurophysiology.