Research Overview
	General Research Themes Specific Research Topics Methodological Approaches Cognitive Neuroscience Visual Attention Event-Related Potentials (ERPs) Visual Cognition Visual Search Magnetoencephalography (MEG) Attention Visual Working Memory Behavior/Psychophysics Working Memory Cognitive Deficits in Schizophrenia Lesions/Neuropsychology Perception Development of Working Memory Intracranial Recordings Development Neural Substrates of Cognition Computational Modeling Psychopathology Attention & Perceptual Organization Neuroimaging
	Attention as an Embedded Process Attention is a notoriously difficult term to define. Following in the tradition of William James, we define attention in the following manner: Restricting cognitive processes to a subset of the available information in order to improve the speed or accuracy of the cognitive processes (Luck & Vecera, 2002) Most attention researchers treat attention as a single, unified, monolithic process. Our laboratory, in contrast, views attention as a type of process that is embedded within specific cognitive subsystems. That is, we have proposed that there are many attentional processes, each of which operates within a given cognitive subsystem (e.g., perception, working memory) in a manner that reflects the representational formats and computational functions of the subsystem. We call this the embedded process view of attention. The purpose of attentional mechanisms is to focus a given cognitive subsystem onto a subset of its inputs to avoid information overload. Different cognitive subsystems will be overloaded under different conditions, and so attention will operate within different subsystems under different conditions. For example, the task of finding a friend's face in a crowd is perceptually demanding, so attention will be used to scan through the faces one at a time. This task has minimal memory demands, however, so attention will not operate at the level of memory in this task. Other tasks (such as the attentional blink task) overload memory but do not overload perception, and attention will influence memory but not perception in such tasks. For more information about this view of attention, see: Luck, S. J. & Vecera, S. P. (2002). Attention. In H. Pashler (Series Ed.) & S. Yantis (Volume Ed.), Stevens' Handbook of Experimental Psychology: Vol. 1. Sensation and Perception (3rd ed., pp. 235-286). The central goal of our laboratory’s research program is to isolate and characterize the neural and cognitive mechanisms of attention that operate within well-defined cognitive subsystems. Our emphasis on multiple attentional mechanisms requires us to use methods that can isolate different cognitive subsystems so that an effect of attention within one subsystem is not mistaken for an effect within another subsystem. In particular, we rely heavily on recordings of event-related potentials (ERPs), electrical signals that are generated by the brain and can be recorded noninvasively from normal volunteers. We also make extensive use of traditional behavioral measures to demonstrate that our physiological results have functional significance. Studying Attention within Specific Cognitive Subsystems Much of our research using the ERP technique has focused on providing evidence that attention operates in different cognitive subsystems under different conditions. For example, we have used ERP recordings to demonstrate that attention modulates perceptual processing in cuing and search tasks that overload the perceptual processing system (e.g., Luck, Fan, & Hillyard, 1993; Luck, Hillyard, Mouloua, Woldorff, Clark, & Hawkins, 1994). A follow-up study using single-unit recordings in macaque monkeys indicated that this variety of attention can modulate the initial feedforward volley of sensory activity through intermediate stages of visual cortex (Luck, Chelazzi, Hillyard, & Desimone, 1997a). More recently, my laboratory has shown that attention does not modulate sensory activity in paradigms that overload short-term memory rather than perception, such as the “attentional blink” paradigm, and that attention operates at the stage of short-term memory in these paradigms (Luck, Vogel, & Shapiro, 1996; Vogel, Luck, & Shapiro, 1998). We have further shown that an even later stage of processing is influenced by attention in the “psychological refractory period” paradigm, which stresses response selection and execution (Luck, 1998). A distinctive element of my laboratory’s research program is the assumption that attentional mechanisms cannot be studied in isolation, but must be understood in the context of the cognitive subsystems they serve (e.g., subsystems for perception, short-term memory, response selection, etc.). Specifically, we believe that we cannot understand how attention operates within a given cognitive subsystem without asking why attention is necessary within that subsystem. For example, we have proposed an “ambiguity resolution” theory of visual attention in which attention is used to resolve ambiguities in the neural coding of visual information that arise when multiple objects are simultaneously present within a neuron’s receptive field (Luck, Girelli, McDermott, & Ford, 1997). In the course of exploring this theory, we have used ERP recordings to provide evidence about the conditions under which attention is necessary for the perception of various types of information (Luck & Ford, 1998; Luck et al., 1997; Luck & Hillyard, 1995), and we have also provided neurophysiological evidence that attention shifts rapidly from object to object when a complex stimulus array is scanned (Woodman & Luck, 1999, 2002). Visual Working Memory Working memory capacity is highly limited, and attention therefore plays an important role in determining which perceptual representations will be stored in working memory. Because we believe that one cannot understand attention in isolation from the cognitive subsystems in which it is embedded, we began conducting experiments on visual working memory several years ago. This has become the second major theme of our laboratory's research. In our initial set of experiments (Luck & Vogel, 1997; Vogel, Woodman, & Luck, 2001), we developed a change-detection procedure for isolation visual working memory. This procedure made it possible to measure the capacity of visual working memory for simple features and for more complex objects. Surprisingly, we found that people can only store 3-4 simple features (e.g., colored squares) in working memory at a given moment. Even more surprisingly, we found that people can remember multi-feature objects just as well as they can remember single-feature objects. For example, people can remember the color, size, and orientation of an item just as well as they can remember just the color, just the size, or just the orientation of the object. Our collaborator, Antonino Raffone, has developed a synchronization-based neural network model that can explain these results (Raffone & Wolters, 2001). We are continuing to study the basic properties of visual working memory representations. For example, one current line of research addresses whether working memory consists of a fixed set of fixed-resolution representations (slots) or a general-purpose resource that can be divided flexibly to achieve different levels of resolution. Another line of research addresses the hypothesis that there are separate working memory systems for spatial and nonspatial features. Yet another line of research addresses the extent to which visual working memory is susceptible to the same sorts of interference effects observed in long-term memory. Development and Psychopathology In collaboration with the laboratory of Prof. Lisa Oakes, we are exploring the development of visual working memory in infancy. Our initial study demonstrated that 4- and 6-month-old infants have a short-term memory capacity of only one object, but capacity appears to reach adult-like levels of 3-4 objects by 10 months of age. Future studies will explore spatial working memory and the ability of infants to store multi-feature objects (Ross-Sheehy, Oakes, & Luck, in press). In collaboration with Dr. James M. Gold of the Maryland Psychiatric Research Center, we are exploring deficits of attention and working memory in patients with schizophrenia. Our initial study demonstrated that visual working memory capacity is nearly normal in schizophrenia, and the modest impairments that could be observed appeared to reflect a disruption of attention rather than a reduction in capacity per se (Gold, Wilk, McMahon, Buchanan, & Luck, 2003). We are currently funded by the NIMH to study attentional impairments in these patients. Using the embedded-process framework discussed above, we have designed tasks that are designed to isolate the operation of attention within specific cogntiive subsystems. Cognitive Neuroscience Approaches We strongly believe that research on cognition benefits greatly from the use of neuroscience methods and from using neuroscience data to provide inspirations and constraints. In addition to ERPs, we have also used a variety of neuroscience techniques. For example, we collaborate with a laboratory in Magdeburg, Germany to conduct MEG experiments, which provide better spatial resolution than ERP recordings. We have also conducted single-unit recordings in monkeys to get very precise data about the operation of attention in visual cortex. We have also tested split-brain patients to understand the contributions of the left and right hemispheres to attention, and we are currently testing patients with focal brain damage to understand the neural substrates of visual working memory. We are also beginning a collaboration with an epilepsy group to conduct intracranial recordings. Finally, we are working with a computational neuroscientist, Antonino Raffone, to construct detailed neural network models of attention.