Human attention filters for single colors

Significance The eyes present the brain much more information than it could possibly process. One important way to prioritize information is by selective attention to features, processing only items containing the attended features and blocking others (i.e., forming an attention filter). Here we demonstrate an extremely efficient paradigm and a powerful analysis to quantitatively measure, as accurately as one might measure physical color filters, 32 such human attention filters for single colors. These data are an essential basis for a theory of attention to color. The centroid paradigm itself, because it quickly and quantitatively characterizes basic attention processes, has numerous applications. The visual images in the eyes contain much more information than the brain can process. An important selection mechanism is feature-based attention (FBA). FBA is best described by attention filters that specify precisely the extent to which items containing attended features are selectively processed and the extent to which items that do not contain the attended features are attenuated. The centroid-judgment paradigm enables quick, precise measurements of such human perceptual attention filters, analogous to transmission measurements of photographic color filters. Subjects use a mouse to locate the centroid—the center of gravity—of a briefly displayed cloud of dots and receive precise feedback. A subset of dots is distinguished by some characteristic, such as a different color, and subjects judge the centroid of only the distinguished subset (e.g., dots of a particular color). The analysis efficiently determines the precise weight in the judged centroid of dots of every color in the display (i.e., the attention filter for the particular attended color in that context). We report 32 attention filters for single colors. Attention filters that discriminate one saturated hue from among seven other equiluminant distractor hues are extraordinarily selective, achieving attended/unattended weight ratios >20:1. Attention filters for selecting a color that differs in saturation or lightness from distractors are much less selective than attention filters for hue (given equal discriminability of the colors), and their filter selectivities are proportional to the discriminability distance of neighboring colors, whereas in the same range hue attention-filter selectivity is virtually independent of discriminabilty.

[1]  James T Todd,et al.  Are discrimination thresholds a valid measure of variance for judgments of slant from texture? , 2010, Journal of vision.

[2]  George Sperling,et al.  Measuring and modeling the trajectory of visual spatial attention. , 2002, Psychological review.

[3]  George Sperling,et al.  Precise attention filters for Weber contrast derived from centroid estimations. , 2010, Journal of vision.

[4]  Alex L. White,et al.  Feature-based attention involuntarily and simultaneously improves visual performance across locations. , 2011, Journal of vision.

[5]  G. Boynton,et al.  Effects of feature-based attention on the motion aftereffect at remote locations , 2006, Vision Research.

[6]  Marisa Carrasco,et al.  Feature-based attention modulates orientation-selective responses in human visual cortex , 2010 .

[7]  Laurent Itti,et al.  A Bayesian model for efficient visual search and recognition , 2010, Vision Research.

[8]  George Sperling,et al.  Evidence against global attention filters selective for absolute bar-orientation in human vision. , 2015, Journal of vision.

[9]  D. Ariely Seeing Sets: Representation by Statistical Properties , 2001, Psychological science.

[10]  G. Sperling,et al.  How do the S-, M- and L-cones contribute to motion luminance assessed using minimum motion? , 2013 .

[11]  Albert Yonas,et al.  Infants and adults use line junction information to perceive 3D shape. , 2012, Journal of vision.

[12]  George Sperling,et al.  The centroid paradigm: Quantifying feature-based attention in terms of attention filters , 2015, Attention, Perception, & Psychophysics.

[13]  Z W Pylyshyn,et al.  Tracking multiple independent targets: evidence for a parallel tracking mechanism. , 1988, Spatial vision.

[14]  S. Liversedge,et al.  Oxford handbook of eye movements , 2011 .

[15]  David E. Meyer,et al.  Using Repetition Detection to Define and Localize the Processes of Selective Attention , 1993 .

[16]  P. Cavanagh,et al.  Tracking multiple targets with multifocal attention , 2005, Trends in Cognitive Sciences.

[17]  Viola S. Störmer,et al.  Feature-Based Attention Elicits Surround Suppression in Feature Space , 2014, Current Biology.

[18]  D H Brainard,et al.  The Psychophysics Toolbox. , 1997, Spatial vision.

[19]  George Sperling,et al.  Black-white asymmetry in visual perception. , 2012, Journal of vision.

[20]  Taosheng Liu,et al.  Constant spread of feature-based attention across the visual field , 2011, Vision Research.

[21]  Isabel Gauthier,et al.  Interference in character processing reflects common perceptual expertise across writing systems. , 2011, Journal of vision.

[22]  G. Boynton,et al.  Feature-Based Attentional Modulations in the Absence of Direct Visual Stimulation , 2007, Neuron.

[23]  S. Luck,et al.  Feature-based attention modulates feedforward visual processing , 2009, Nature Neuroscience.

[24]  A. Stockman,et al.  The spectral sensitivities of the middle- and long-wavelength-sensitive cones derived from measurements in observers of known genotype , 2000, Vision Research.

[25]  R. Shapley,et al.  Context affects lightness at the level of surfaces. , 2013, Journal of vision.

[26]  J. Wolfe,et al.  Guided Search 2.0 A revised model of visual search , 1994, Psychonomic bulletin & review.

[27]  Stefan Treue,et al.  Feature-based attention influences motion processing gain in macaque visual cortex , 1999, Nature.

[28]  B. Scholl Objects and attention: the state of the art , 2001, Cognition.

[29]  Taosheng Liu,et al.  Suppression effects in feature-based attention. , 2015, Journal of vision.

[30]  Robert Desimone,et al.  Parallel and Serial Neural Mechanisms for Visual Search in Macaque Area V4 , 2005, Science.

[31]  L. Feigenson,et al.  Multiple Spatially Overlapping Sets Can Be Enumerated in Parallel , 2006, Psychological science.

[32]  S. Andersen,et al.  Behavioral performance follows the time course of neural facilitation and suppression during cued shifts of feature-selective attention , 2010, Proceedings of the National Academy of Sciences.

[33]  G Sperling,et al.  Measuring the amplification of attention. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[34]  George Sperling,et al.  Sensitive calibration and measurement procedures based on the amplification principle in motion perception , 2001, Vision Research.

[35]  M. Carrasco Visual attention: The past 25 years , 2011, Vision Research.

[36]  Z. Pylyshyn Some puzzling findings in multiple object tracking: I. Tracking without keeping track of object identities , 2004 .

[37]  W. Geisler,et al.  Models of overt attention , 2011 .

[38]  S A Hillyard,et al.  Feature-selective attention enhances color signals in early visual areas of the human brain , 2006, Proceedings of the National Academy of Sciences.

[39]  P. Cavanagh,et al.  A minimum motion technique for judging equiluminance , 1983 .