The environment contains far more information than the brain can process at once. To cope with such information overload, humans need to selectively attend to relevant information and prioritize its processing. In many situations, humans need to select arbitrary features and objects in the scene and maintain attention on the selected information. It is often assumed that an attentional priority signal encodes the current focus of attention and its deployment. However, how the brain computes and maintains attentional priority for features and objects is not known. The long-term goal is to understand how the brain selects different types of information and how selection shapes perception to serve goal-oriented behavior. The objective of this proposal is to delineate the cortical circuitry representing attentional priority for features and objects using functional magnetic resonance imaging (fMRI). Based on recent data obtained in our laboratory, we hypothesize that the dorsal frontoparietal network represents different types of selected information with distinct neural populations, forming a multiplexed representation of attentional priority. In this proposal, we wil test this hypothesis by pursuing four specific aims. First, we will determine the neural representation of attentional priority for visual objects. Second, we will seek to establish a quantitative link between priority signals and task performance. Third, we will determine the relationship between attentional priority signals for features and objects and those for spatial locations. Fourth, we will evaluate the degree of categorical representation of attentional priorit, which is essential for flexible deployment of attention. The proposed research is expected to significantly advance our understanding of how the brain selects and maintains non- spatial information, thus filling in a critical gap in the current scientific knowledge. A deeper understanding of how the brain selects features and objects will provide important constraints for models of attention and can potentially transform our understanding of visual information processing and cognitive control. The proposed research is innovative both in terms of conceptual and methodological advances. Conceptually, the research will test the novel hypothesis that the dorsal frontoparietal network represents attention priority for non-spatial dimensions, challenging the prevailing view that these cortical areas mainly represent spatial information. Methodologically, the application of cutting-edge machine learning and data mining techniques (pattern classification, similarity and clustering analysis) represents a novel approach that more fully exploits the complexity and richness of fMRI data than conventional methods. Finally, the proposed research can make connections to other fields such as category learning and decision making, and suggest interesting future directions to examine common neural processes underlying these cognitive functions.

Public Health Relevance

Impairment in attention is associated with many neuropsychological conditions, such as neglect due to stroke, attention deficit-hyperactivity disorder, autism, and Alzheimer's disease, as well as certain visual problems such as strabismic amblyopia. By elucidating the basic brain mechanisms of attention, the proposed research will inform our understanding of the nature of attentional deficits in these disorders and contribute to the basic research foundation that will ultimately guide the diagnosis and treatment of these disorders.

National Institute of Health (NIH)
National Eye Institute (NEI)
Research Project (R01)
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Mechanisms of Sensory, Perceptual, and Cognitive Processes Study Section (SPC)
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Flanders, Martha C
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Michigan State University
Schools of Arts and Sciences
East Lansing
United States
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Gong, Mengyuan; Liu, Taosheng (2018) Reward differentially interacts with physical salience in feature-based attention. J Vis 18:12
Liu, Taosheng; Cable, Dylan; Gardner, Justin L (2018) Inverted Encoding Models of Human Population Response Conflate Noise and Neural Tuning Width. J Neurosci 38:398-408
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Jigo, Michael; Gong, Mengyuan; Liu, Taosheng (2018) Neural Determinants of Task Performance during Feature-Based Attention in Human Cortex. eNeuro 5:
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Qian, Cheng S; Brascamp, Jan W; Liu, Taosheng (2017) On the functional order of binocular rivalry and blind spot filling-in. Vision Res 136:15-20
Liu, Taosheng; Jigo, Michael (2017) Limits in feature-based attention to multiple colors. Atten Percept Psychophys 79:2327-2337
?entürk, Gözde; Greenberg, Adam S; Liu, Taosheng (2016) Saccade latency indexes exogenous and endogenous object-based attention. Atten Percept Psychophys 78:1998-2013
Liu, Taosheng (2016) Neural representation of object-specific attentional priority. Neuroimage 129:15-24
Wang, Yixue; Miller, James; Liu, Taosheng (2015) Suppression effects in feature-based attention. J Vis 15:15

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