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Neuronal Mechanisms of Attention
Summary: John Maunsell is interested in understanding how attention influences the representation of sensory information in cerebral cortex, and how these changes improve behavioral performance.
Attention plays a critical role in perception. At any moment, we can give full attention to only a tiny fraction of the sensory information reaching our brain. Attention to a particular location enhances detection, improves discrimination, and speeds responses at that position relative to others. Often attention makes the difference between seeing something or missing it entirely. These pronounced changes in performance are associated with changes in the way the brain processes sensory information. My laboratory investigates how attention changes the way that individual neurons represent visual information, and how those changes affect behavior.
We use microelectrodes to record the electrical signals of neurons in the visual regions of the cerebral cortex of monkeys. The monkey's visual system has been extensively studied, and its structure and function are very similar to the structure and function of the human visual system. The part of cerebral cortex that serves vision is divided into dozens of separate areas, each of which has its own representation of the visual scene and contains neurons that are specialized for representing a particular sort of visual information. For example, some areas are specialized to represent motion, and each neuron within those areas responds only to stimuli moving in a particular direction, regardless of the color, size, or shape of the moving stimuli. Neurons in other cortical areas are specialized to represent other types of visual information, such as the orientation of edges or stimulus color. Cortical areas also differ in the complexity of the sensory information that they represent: while some cortical areas contain neurons that respond well to any contour or edge, neurons in other areas respond only to specific, complicated patterns, forms, or objects. For example, neurons in some parts of visual cortex are active only when the subject views faces.
The responses of neurons within these specialized cortical areas depend not only on the signals coming from the eyes but also on signals related to attention, which come from other parts of the brain. Neurophysiological studies from many laboratories have shown that neurons respond differently when monkeys shift their attention from one stimulus to another, even when those shifts in attention are made without moving their eyes. Most neurons respond more strongly to a stimulus when the animal pays attention to it. Thus, attention boosts the neuronal signals representing the part of the scene that the viewer considers interesting, while suppressing signals related to the rest.
In a series of experiments, we have shown that attention adjusts the strength of neuronal responses dynamically but does not greatly alter the selectivity of neurons for particular stimuli. As mentioned above, each sensory neuron is most sensitive to a particular visual attribute, such as color, orientation, or direction of motion, and each responds strongly only to a particular range of stimuli that matches its selectivity (e.g., a particular range of colors or orientations). We examined whether attention to stimulus orientation affects orientation-selective neurons by restricting their responses to a narrower range of orientations. Attention did not change the range of orientations to which a neuron responded. Instead, attention changes the strength, or gain, of neuronal responses without changing what they respond to. We have recently shown that attention uses a mechanism that is central to all sensory processing to achieve this gain control.
When a neuron is driven by two stimuli at the same time, those stimuli do not usually have an additive effect. Instead, the neuron will typically produce a response that is intermediate between those produced when each stimulus is presented alone. This occurs because the circuitry in the cerebral cortex allows each stimulus to affect the gain of the neuron's response to the other stimulus. We have recently shown that the strength of this gain-control mechanism determines how much the neuron's response can be affected by attention. Specifically, neurons that show little effect of one stimulus on the other show little effect of attention, while those with powerful stimulus interactions are strongly modulated by attention. The finding that attention uses a well-established stimulus-processing circuit will help us understand how attention interacts with sensory signals in determining behavioral performance.
In addition to our experiments on attention, we have also begun exploring how learning in adult life affects sensory representations in cortex. To approach this question, we used microelectrodes to activate tiny groups of neurons in the primary visual cortex (V1) of monkeys. Electrical stimulation of human V1 causes people to see tiny spots of light, called phosphenes. We trained the monkeys to report when they saw phosphenes. As expected, the monkeys reported seeing a phosphene when a tiny electrical current was applied in V1. Notably, the amount of current needed to generate a response decreased over time. Over the course of thousands of trials, the threshold current dropped and stabilized, following the sort of time course that is seen when humans (or monkeys) learn to detect small differences in a visual stimulus.
After thresholds for detecting electrical stimuli had stabilized, we tested how well the animals could detect visual stimuli that activated the same part of V1. Remarkably, the animals were very poor at detecting visual stimuli after they had become expert at detecting electrical stimuli. This deficit does not arise from damage to cortex: with practice, visual threshold recovered completely, but retraining on visual detection in turn left the animals with high thresholds for detection of electrical stimulation.
These results suggest that each local region of cerebral cortex can be optimized for detecting only one pattern of neuronal activity (electrically induced or visually induced, in our case) and that learning to detect a new pattern of neuronal activity comes at the cost of performance on other patterns. We believe that changes we have seen in this experiment depend on the learning mechanism that underlies most adult learning. This experimental approach may allow us to link studies on the biochemical and cellular mechanisms of learning with circuit mechanisms and behavioral consequences. We are exploring this possibility and also examining whether the changes we have seen occur if we apply electrical stimuli that the animal does not pay attention to.
A grant from the National Institutes of Health provided partial support for these projects.
Last updated April 09, 2009
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