Attending to certain visual features or objects may have significant consequences for performance in some circumstances, but is not necessarily important in all tasks. Understanding the role of attention in the performance of visual tasks requires the characterization of the mechanisms of attention and a taxonomy of task conditions under which attention is important. We extend a powerful method and model for identifying and characterizing the effect of attention on perceptual performance. The method combines an external noise approach (adding visual noise similar to random TV noise) with different visual tasks similar to everyday or operator tasks involving complex displays to identify the mechanism(s) of attention in each task and to understand when attention is important to high performance. The approach provides a multi-dimensional characterization of task performance in terms of signal strength, external noise, and target similarity. We use a number of task manipulations to assay the role of attention, including spatial cuing, inhibition of return, visual search and visual short-term memory. The results from these empirical observations will be used to construct and test a taxonomy of visual attention. The goal is to develop an overarching system and theoretical structure to organize all the empirical observations about when and how and why attention can improve human performance. In addition, we evaluate whether and when training can eliminate attention demands.

Public Health Relevance

Understanding when attention is a limiting factor in performance and whether training can eliminate attention demands will have theoretical and practical implications for understanding attention deficits in individuals exhibiting abnormalities of attention associated with mental health conditions. This analysis can additionally constrain process models of attention in normal observers and may suggest constraints on the relevant properties of neurological models of attention.

National Institute of Health (NIH)
National Institute of Mental Health (NIMH)
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Cognition and Perception Study Section (CP)
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Rossi, Andrew
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University of California Irvine
Schools of Arts and Sciences
United States
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Baek, Jongsoo; Lesmes, Luis Andres; Lu, Zhong-Lin (2016) qPR: An adaptive partial-report procedure based on Bayesian inference. J Vis 16:25
Cabrera, Carlos Alexander; Lu, Zhong-Lin; Dosher, Barbara Anne (2015) Separating decision and encoding noise in signal detection tasks. Psychol Rev 122:429-60
Hetley, Richard; Dosher, Barbara Anne; Lu, Zhong-Lin (2014) Generating a taxonomy of spatially cued attention for visual discrimination: effects of judgment precision and set size on attention. Atten Percept Psychophys 76:2286-304
Lu, Zhong-Lin; Li, Xiangrui; Tjan, Bosco S et al. (2011) Attention extracts signal in external noise: a BOLD fMRI study. J Cogn Neurosci 23:1148-59
Shapiro, Arthur G; Knight, Emily J; Lu, Zhong-Lin (2011) A first- and second-order motion energy analysis of peripheral motion illusions leads to further evidence of ""feature blur"" in peripheral vision. PLoS One 6:e18719
Dosher, Barbara Anne; Han, Songmei; Lu, Zhong-Lin (2010) Perceptual learning and attention: Reduction of object attention limitations with practice. Vision Res 50:402-15
Dosher, Barbara Anne; Han, Songmei; Lu, Zhong-Lin (2010) Information-limited parallel processing in difficult heterogeneous covert visual search. J Exp Psychol Hum Percept Perform 36:1128-44
Liu, Shiau-Hua; Dosher, Barbara Anne; Lu, Zhong-Lin (2009) The role of judgment frames and task precision in object attention: Reduced template sharpness limits dual-object performance. Vision Res 49:1336-51
Jeon, Seong-Taek; Lu, Zhong-Lin; Dosher, Barbara Anne (2009) Characterizing perceptual performance at multiple discrimination precisions in external noise. J Opt Soc Am A Opt Image Sci Vis 26:B43-58
Lu, Zhong-Lin; Tse, Hennis Chi-Hang; Dosher, Barbara Anne et al. (2009) Intra- and cross-modal cuing of spatial attention: Time courses and mechanisms. Vision Res 49:1081-96

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