The centroid paradigm: Quantifying feature-based attention in terms of attention filters

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• 作者：Peng Sun ; Charles Chubb ; Charles E. Wright…
• 关键词：Selective attention ; Methodology ; Centroid estimation ; Statistical summary representations (SSRs) ; Feature ; based attention ; Attention filter
• 刊名：Attention, Perception, & Psychophysics
• 出版年：2016
• 出版时间：February 2016
• 年：2016
• 卷：78
• 期：2
• 页码：474-515
• 全文大小：7,522 KB
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• 作者单位：Peng Sun (1) (2)
  Charles Chubb (1)
  Charles E. Wright (1)
  George Sperling (1)
  
  1. Department of Cognitive Sciences, University of California, Irvine, CA, 92697-5100, USA
  2. Department of Psychology, New York University, New York, NY, 10003, USA
  
• 刊物主题：Cognitive Psychology;
• 出版者：Springer US
• ISSN：1943-393X

文摘

This paper elaborates a recent conceptualization of feature-based attention in terms of attention filters (Drew et al., Journal of Vision, 10(10:20), 1–16, 2010) into a general purpose centroid-estimation paradigm for studying feature-based attention. An attention filter is a brain process, initiated by a participant in the context of a task requiring feature-based attention, which operates broadly across space to modulate the relative effectiveness with which different features in the retinal input influence performance. This paper describes an empirical method for quantitatively measuring attention filters. The method uses a “statistical summary representation” (SSR) task in which the participant strives to mouse-click the centroid of a briefly flashed cloud composed of items of different types (e.g., dots of different luminances or sizes), weighting some types of items more strongly than others. In different attention conditions, the target weights for different item types in the centroid task are varied. The actual weights exerted on the participant’s responses by different item types in any given attention condition are derived by simple linear regression. Because, on each trial, the centroid paradigm obtains information about the relative effectiveness of all the features in the display, both target and distractor features, and because the participant’s response is a continuous variable in each of two dimensions (versus a simple binary choice as in most previous paradigms), it is remarkably powerful. The number of trials required to estimate an attention filter is an order of magnitude fewer than the number required to investigate much simpler concepts in typical psychophysical attention paradigms. Keywords Selective attention Methodology Centroid estimation Statistical summary representations (SSRs) Feature-based attention Attention filter