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Stefanie A. Drew, Charles F. Chubb, Tobin Ehrlich, Tim Rubin, George Sperling; Binary versus graded filters for selectively attending to dots of different contrasts. Journal of Vision 2008;8(6):881. doi: 10.1167/8.6.881.
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© ARVO (1962-2015); The Authors (2016-present)
Previously we established that in estimating the centroids of sparse dot-clouds, observers are able to apply attentional filters that select dots based on the sign of dot contrast, giving equal weight to all pertinent dots (dots darker than the background, lighter, or all dots). To further investigate these top-down attentional filters, we examined whether they could be made sensitive to contrast amplitude. Participants indicated with mouse-clicks the locations of centroids for briefly flashed, sparse clouds comprising either 8 or 16 dots of various intensities, attending to either (i) dots darker than the background, (ii) dots lighter than the background, or (iii) all dots present, with a variation to our original instructions. Whereas observers previously attempted to weight all pertinent dots equally, in the current study observers were instructed to weight pertinent dots in proportion to their contrast amplitudes, assigning more weight to dots with extreme contrasts. For example, in the attend-to-dark condition, observers were required to estimate the centroid of only the dots darker than the background, giving the greatest weight to black dots, slightly less to dark gray, etc. Three conditions were tested: (i) to find the centroid of only the dots darker than the background, giving the most weight to black dots, (ii) to find the centroid of only the dots lighter than the background, giving the most weight to white dots, and (iii) to find the centroid of all of the dots present, with the most weight assigned to the extreme black and white dots. The observed graded-attention filters differed only slightly from those observed under equal weighting instructions. This suggests that observers do not have access to separate attention filters that operate on a gradient scale.
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