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Attention filters for features

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An attention filter is a brain process, initiated by a participant in the context of a task requiring feature based selective attention, that operates broadly across space to modulate the relative effectiveness with which different features in the retinal input influence performance. The method for quantitatively measuring attention filters 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 an order of magnitude quicker and more efficient than previous measurements of human selective attention. To be described: (1) Three useful statistics to describe attention filters: efficiency, fidelity, and data driveness, (2) some important procedural improvements: singleton trials, constant dispersion, and (3) illustrative examples as time permits: Attention filters for light versus dark dots, the speed with which attention filters are formed, filters with equal item weights for targets of all contrasts versus proportional weights and other transformations, 32 attention filters for single colors, confirmations of the differences between filters for hue versus filters for saturation and lightness, a simple dimension for which humans cannot form a SSR attention filters, and more (or fewer) examples as time permits.

Reference: Sun, R., Chubb, C., Wright, C. E., Sperling, G. (2016). The centroid paradigm: Quantifying feature-based attention in terms of attention filters. Attention, Perception, and Psychophysics, 78(2) 474-515. DOI 10 .3758/s13414-015-0978-2

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