Disc Size Supports Top-Down, Selective Attention in a Task Requiring Integration across Multiple Target

Blair, Garrett J.; Wright, Charles E.; Chubb, Charlie; Sun, Peng; Sperling, George

doi:10.1167/15.12.897

Cited by 1 publication

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…All participants achieve strikingly different attention filters in the two different attention conditions. However, the attention filters achieved differ in important ways from the target filters higher are commonly observed in attending to black versus white items (Inverso et al, In press) or red vs. green items or large vs. small items (Blair et al, 2015), and these represent highly selective attention filters. On the other hand, in the Dots experiment, filter selectivities achieved by participants S1, S2, S3 and S4 in the Dark-only (Light-only) attention condition were all lower than 10: 7.29, 7.97, 3.91 and 4.89 (9.55, 6.88, 5.28, 3.78).…”

Section: Figmentioning

confidence: 87%

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

Sun

Chubb

Wright

et al. 2015

Atten Percept Psychophys

Self Cite

View full text Add to dashboard Cite

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][2][3][4][5][6][7][8][9][10][11][12][13][14][15][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.

show abstract

Section: Figmentioning

confidence: 87%