Precise attention filters for Weber contrast derived from centroid estimations

Abstract

How well can observers selectively attend only to dots that are lighter or darker than the background when all dot intensities are present? Observers estimated centroids of briefly flashed, sparse clouds of 8 or 16 dots, ranging in intensity from dark black to bright white on a gray background. Attention instructions were to equally weight: (i) dots brighter than the background, assigning zero weight to others; (ii) dots darker than the background, assigning zero weight to others; (iii) all dots. For each observer, a quantitative estimate of the operational attention filter (the weight exerted in the centroid estimates as a function of dot intensity) was derived for each attention instruction in each dot condition. Attended dots typically have 4× the weights of unattended dots. Whereas observers performed remarkably well in estimating centroids and achieving the three required attention filters, they achieved higher accuracy when equally weighing all dots than when selectively attending to dots of only one contrast polarity. Although their attention filters are similar, individual observers use significantly different parameters in their centroid computations. The complete model of performance enables perceptual measurements of observers' attention filters for shades of gray that are as accurate as physical measurements of color filters.

Evidence against global attention filters selective for absolute bar-orientation in human vision

“…The strategy in brief is as follows: If participants do have access to a neural image (Robson, 1980) in which, for example, vertical bars produce strong activation but horizontal bars do not, then they should be able to use this neural image to make accurate estimates of the centroids of just the vertical bars in displays comprising mixtures of vertical target bars and horizontal distractor bars. The experiments reported here demonstrate that participants are dramatically worse at this centroid-estimation task than they are if target vs. distractor items are defined by brightness (black vs. white) instead of by orientation, suggesting that although human vision possesses attention filters selective for black vs. white (Drew, Chubb & Sperling, 2010), it does not possess corresponding attention filters selective for vertical vs. horizontal bar-orientation.…”

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

“…As noted by Drew et al (2010), in the absence of general training in the centroid task, different participants show significant individual differences in the centroid computations they use. In particular, some participants tend to overweight the contributions of peripheral items relative to items near the center of the cloud whereas other participants show the opposite tendency.…”

“…The centroid paradigm, first used by Drew et al (2010) and substantially refined here, offers a number of important advantages over previous methods used to measure attention filters Chubb & Talevich, 2002), including the following:…”

“…These questions led us to conceptualize FBA in terms of attention filters (Drew et al, 2010). An attention filter is a process, initiated by a participant in the context of a task requiring FBA, which operates broadly across space to modulate the relative effectiveness with which different features in the retinal input influence performance.…”

Human attention filters for single colors

“…The task is to then mouse-click the apparent center of the dot cloud. Drew et al (5) first used a centroid task to estimate attention filters. The procedure was substantially refined by Sun et al (6) into the centroid paradigm used here.…”