Suppression effects in feature-based attention

Yixue Wang $Department of Psychology, Michigan State University,

East Lansing, MI, USA

James Miller $Department of Psychology, Michigan State University,

East Lansing, MI, USA

Taosheng Liu $Department of Psychology & Neuroscience Program,

Michigan State University, East Lansing, MI

Attending to a feature enhances visual processing of thatfeature, but it is less clear what occurs to unattendedfeatures. Single-unit recording studies in middletemporal (MT) have shown that neuronal modulation isa monotonic function of the difference between theattended and neuron’s preferred direction. Such arelationship should predict a monotonic suppressiveeffect in psychophysical performance. However, pastresearch on suppressive effects of feature-basedattention has remained inconclusive. We investigatedthe suppressive effect for motion direction, orientation,and color in three experiments. We asked participantsto detect a weak signal among noise and provided apartially valid feature cue to manipulate attention. Wemeasured performance as a function of the offsetbetween the cued and signal feature. We also includedneutral trials where no feature cues were presented toprovide a baseline measure of performance. Acrossthree experiments, we consistently observedenhancement effects when the target feature and cuedfeature coincided and suppression effects when thetarget feature deviated from the cued feature. The exactprofile of suppression was different across featuredimensions: Whereas the profile for direction exhibited a‘‘rebound’’ effect, the profiles for orientation and colorwere monotonic. These results demonstrate thatunattended features are suppressed during feature-based attention, but the exact suppression profiledepends on the specific feature. Overall, the results arelargely consistent with neurophysiological data andsupport the feature-similarity gain model of attention.

Introduction

Selective attention prioritizes the processing of asmall portion of information from the vast amount of

information that impinges on the visual system. Theeffects of attention have been extensively studied atboth the behavioral and physiological levels, particu-larly for spatial attention (for reviews see Carrasco,2011; Kastner & Ungerleider, 2000; Reynolds &Chelazzi, 2004). However, attention can also selectspecific features (e.g., red color, rightward motion),even when different features occupy the same spatiallocation (Scolari, Ester, & Serences, 2014; Maunsell &Treue, 2006). In this study, we focus on the perceptualeffects of feature-based attention, and in particular,how attention affects the representation of visualfeatures.

This question has been directly assessed in single-unit recording studies where monkey subjects attendedto directions of motion while neuronal activity in themotion sensitive area, MT, was measured (Martinez-Trujillo & Treue, 2004; Treue, & Martinez-Trujillo,1999). These authors found that attending to adirection enhanced activity for neurons preferringsimilar directions, while it suppressed activity forneurons preferring dissimilar directions, essentiallygiving rise to a sharpened population activity for theattended feature. This finding has led to the influentialfeature-similarity gain model of attention, whichmaintains that the amount of neuronal modulationdepends on the similarity between the attended featureand the neuron’s preferred feature. Many studies inhumans, using both neuroimaging and psychophysics,have found results consistent with feature-similaritygain model (Arman, Ciaramitaro, & Boynton, 2006;Bondarenko et al., 2012; Lankheet & Vestraten, 1995;Liu & Hou, 2011; Liu, Larsson, & Carrasco, 2007; Liu& Mance, 2011; Saenz, Buracas, & Boynton, 2002,2003; Serences & Boynton, 2007; White & Carrasco,2011; Zhang & Luck, 2008). However, these studiestypically employed only two feature values (e.g.,

Citation: Wang, Y., Miller, J., & Liu, T. (2015). Suppression effects in feature-based attention. Journal of Vision, 15(5):15, 1–15,http://www.journalofvision.org/content/15/5/15, doi:10.1167/15.5.15.

Journal of Vision (2015) 15(5):15, 1–16 1http://www.journalofvision.org/content/15/5/15

doi: 10 .1167 /15 .5 .15 ISSN 1534-7362 � 2015 ARVOReceived August 21, 2014; published April 24, 2015

Downloaded From: http://jov.arvojournals.org/pdfaccess.ashx?url=/data/Journals/JOV/933757/ on 04/29/2015 Terms of Use:

upward vs. downward motion, vertical vs. horizontalorientation) and assessed the effect of attention bycomparing the attended versus unattended feature. Assuch, these studies did not permit a systematicassessment of the profile of attentional modulationwithin a feature dimension.

A basic prediction from the neurophysiological data(Khayat, Neibergall, & Martinez-Trujillo, 2010; Mar-tinez-Trujillo & Treue, 2004; Treue & Martinez-Trujillo, 1999) is that as stimulus features deviate morefrom the attended feature, their representations aremonotonically modulated such that similar features areenhanced and dissimilar features are suppressed. Thisprediction has received limited investigation in theliterature. Two studies, one testing orientation (Tombu& Tsotsos, 2008) and one testing motion direction (Ho,Brown, Abuyo, Ku, & Serences, 2012) have foundnonmonotonic modulation of feature processing, in-consistent with this prediction. For both direction andorientation, these authors found the highest perfor-mance for the attended feature, with decreasingperformance as the feature deviated more from theattended feature, but a ‘‘rebound’’ effect such that themost dissimilar feature (orthogonal orientation andopposite direction) exhibited a relative increase inperformance. These studies used different designs andtasks to measure the effect of attention. Furthermore,certain elements in the design and data of these studieslimited the inferences drawn from their results. Forexample, Tombu and Tsotsos (2008) did not include aneutral condition, so it is difficult to assess enhance-ment versus suppression, whereas Ho et al. (2012) useda visual search task that likely involved shifts of spatialattention, which could confound the interpretation oftheir results. We will present more in-depth discussionsof these studies in the General discussion.

Our goal here is to measure the modulation profiledue to feature-based attention, for both the directionand orientation features. In addition, we also examinedthe modulation profile for color, an important visualfeature but with little systematic investigation in termsof how attention affects its perceptual representation.We used the same task for all three features, in a designthat maximized our ability to detect modulations of thesensory representations of these features. Our resultsshowed clear suppression effects for all features thatwere largely consistent with the predictions of thefeature-similarity gain model.

Experiment 1: Attention to directionof motion

In the first experiment, we tested the effect offeature-based attention to directions of motion. Par-

ticipants were cued to attend to a particular directionand detected a coherent motion signal that could movein various directions. This allowed us to assess howattention modulates direction-selective mechanismsduring motion detection.

Methods

Participants

A total of six observers participated in thisexperiment. All of them were graduate and under-graduate students at Michigan State University andwere naive as to the purpose of the experiment. Allparticipants had normal or corrected-to-normal vision.Participants gave informed consent and were compen-sated at the rate of $10/hr. All experimental protocolswere approved by the Institutional Review Board atMichigan State University.

Visual stimuli

Visual stimuli were generated using MGL (http://justingardner.net/mgl), a set of OpenGL librariesrunning in Matlab (Mathworks, Natick, MA). We usedthe random dot motion stimulus, which was based onclassic studies in neurophysiology (Newsome & Pare,1988). The motion stimulus consisted of white movingdots (size: 0.058) in a circular aperture (68), presentedon a dark background. The circular aperture wascentered on the fixation point (white, size: 0.38), whichwas surrounded by a small occluding region (0.78) ofthe background luminance such that no dots wouldappear too close to the fixation point. The dots wereplotted in three interleaved sets of equal number, withan effective density of 16.8 dots/deg2/s and a speed of48/s. Each single dot was presented on the screen forone video frame (life time¼16.7 ms). Importantly, onlya portion of dots moved in a particular directionbetween frames, while the rest of the dots were redrawnin random locations. The stimuli were presented on a19-in. CRT monitor refreshed at 60 Hz and set at aresolution of 1024 · 768. Observers were stabilizedwith a chinrest and viewed the display from a distanceof 57 cm in a dark room.

Task and procedures: Training

We first trained all observers to familiarize them withthe task, until their performance stabilized so that wecould choose appropriate stimulus levels for theattention task (see below). Observers detected thepresence of coherent motion in a two-interval forcedchoice (2-IFC) (Figure 1). Each trial started with a 0.5-sfixation interval, then a 0.7-s blank interval, after whichtwo intervals of random dot motion stimuli were

Journal of Vision (2015) 15(5):15, 1–16 Wang, Miller, & Liu 2

Downloaded From: http://jov.arvojournals.org/pdfaccess.ashx?url=/data/Journals/JOV/933757/ on 04/29/2015 Terms of Use:

shown, each for 0.3 s, and separated by 0.7 s, andfinally followed by a 0.5-s feedback interval after theresponses. One interval always contained 0% coherentmotion (noise) while the other interval contained amotion stimulus at one of seven coherence levels: 2%,4%, 8%, 16%, 32%, 64%, and 96% (signal), via themethod of constant stimuli. The presentation order ofthe signal and noise interval was randomized. Observ-ers were instructed to judge which interval containedthe coherent motion signal by pressing the 1 or 2 key onthe numeric keypad of a standard computer keyboard.A sound was played for incorrect interval judgmentsafter observer pressed the key. An intertrial interval of1.5 s then followed.

We fit the proportion of correct data with a Weibullfunction using maximum-likelihood estimation, asimplemented in psignifit (Wichmann & Hill, 2001; seeFigure 2A for an example fit). We defined threshold asthe coherence level corresponding to ;65% correct inthe 2-IFC task. Observers performed the motiondetection task in 48-trial blocks until their coherencethreshold did not show a sizeable drop in consecutivefour-block units. On average, the practice session was27 blocks long (i.e., 1,296 trials), with a range of 18–30blocks across observers. The training task was dividedinto three to four sessions, each lasting about 60 min.

Task and procedures: Attention

The attention task was similar to the training task,except we introduced a cueing and validity manipula-tion (Figure 1). The strength of the motion signal wasdetermined by data in the training sessions. At thebeginning of each trial in the cued blocks, an arrow cueappeared for 0.5 s (length: 0.38, center 0.658 to thefixation). This cue accurately indicated the direction ofthe signal on 69% of the trials. In the remaining 31% oftrials, the invalid trials, the signal moved in a differentdirection from the cued direction, with the differenceset to one of the 12 possible offset values (6308, 6608,6908, 61208, 61508, 61808). On each trial, the cue’sdirection was randomly sampled from 120 possible

directions ranging from 08 to 3578 (step size: 38).Observers were instructed to attend to the cueddirection, as it would help them to detect the motionsignal. We also ran the neutral (no-cue) condition inseparate blocks, in which the fixation dot turned greento indicate the impending motion stimuli. Signaldirection in the neutral trials was randomly sampledfrom the same 120 possible directions as in the cuedtrials. In both the cued and neutral condition, observerswere instructed to report which interval contained thecoherent motion signal by pressing the 1 or 2 key on thenumeric keypad. There was no feedback at the end ofthe trial during the attention task.

Each session consisted of four blocks, with oneneutral block and three cued blocks. The order of theseblocks was randomized for each session. The neutralblock contained 40 trials and cued blocks contained 50trials, and each observer completed five sessions. Intotal, each observer completed 200 neutral trials and750 cued trials, which was composed of ;520 validtrials and ;19 invalid trials in each target offset (;38trials when collapsed across the sign of the offset, seeResults below).

Results and discussion

The average coherence threshold was 0.10 (SD ¼0.02) as determined in the training sessions (seeMethods). We calculated accuracy as a function of theoffset between the cue and signal direction (a signaldetection analysis using d0 as the performance measureproduced very similar results in this and subsequentexperiments). Since the clockwise and counterclockwiseoffsets showed similar results, we collapsed across thesign of the offset to produce six offset levels (308, 608,908, 1208, 1508, 1808). Average performance showed aU-shaped function (Figure 3). Figure 3 also showsperformance in the neutral condition (dashed horizon-tal line), when participants had no expectation of thesignal direction. We used the neutral condition to assessattentional effect—a higher-than-neutral performance

Figure 1. Schematic of a trial in Experiment 1. Observers detected the presence of coherent motion in a 2-IFC task.

Journal of Vision (2015) 15(5):15, 1–16 Wang, Miller, & Liu 3

Downloaded From: http://jov.arvojournals.org/pdfaccess.ashx?url=/data/Journals/JOV/933757/ on 04/29/2015 Terms of Use:

would indicate attentional enhancement and a lower-than-neutral performance would indicate attentionalsuppression. The U-shaped function crossed the neutralperformance level, showing both enhancement andsuppression due to feature-based attention. At theindividual level, we found five out of six participantswho showed the U-shaped function. Compared to theneutral performance, valid cues (08 offset) yieldedsignificantly higher accuracy, t08(5)¼ 6.53, p , 0.01,demonstrating an enhancement. The invalid cuesproduced significantly lower accuracies in 608, 908,1208, and 1508 offset conditions, t608(5)¼4.55, p , 0.01;t908(5)¼ 3.04, p , 0.05; t1208(5)¼ 4.54, p , 0.01; t1508(5)¼ 2.58, p , 0.05, but not in 308 and 1808 offsetconditions, t308(5)¼ 0.04, p¼ 0.97; t1808(5)¼ 0.22, p¼0.83. To further characterize the shape of the modu-lation function, we fit the average cued data with both alinear and a quadratic function. In addition, we also fitthe same data with a linear function with an interceptbut zero slope. We refer to this function as ‘‘no-modulation fit,’’ as it describes a scenario whereperformance is not affected by the cueing manipula-tion. We then compared the fits using BayesianInformation Criterion (BIC), and found a quadratic fitto be a better description of the data than a linear fit

Figure 2. Threshold data from the training sessions of the three experiments. Data from a representative subject from each

experiment is plotted. Solid lines are Weibull fits of the psychometric functions. (A) Threshold data from Experiment 1. (B) Threshold

data from Experiment 2. (C) Threshold data from Experiment 3, in which we separately measured threshold for each of the six colors,

coded by the color of the symbols and lines (see Figure 6B). Threshold data are noisier in the color experiment, presumably due to a

smaller number of trials per color.

Figure 3. Results for Experiment 1. Group averaged accuracy as

a function of the cue-to-target offset in direction. Error bars are

the estimated within-subject standard error following the

method of Loftus and Masson (1994). Asterisks indicate the

significance level in paired t tests (*p , 0.05). The solid line is a

quadratic fit of the average accuracy in the cued trials as a

function of direction offset.

Journal of Vision (2015) 15(5):15, 1–16 Wang, Miller, & Liu 4

Downloaded From: http://jov.arvojournals.org/pdfaccess.ashx?url=/data/Journals/JOV/933757/ on 04/29/2015 Terms of Use:

(Bayes factor ¼ 2.76 · 103) and the no-modulation fit(Bayes factor ¼ 3.07 · 103).

These results showed a significant enhancementeffect when the cued direction was consistent with thatof the target, demonstrating that participants wereindeed utilizing the cue to orient their attention. Theattentional enhancement effect was consistent with theidea that attending to a feature enhances its processing,which has been supported by a large number of studiesusing a variety of tasks (Arman et al., 2006; Lankheet& Vestraten, 1995; Liu & Hou, 2011; Liu et al., 2007;Liu &Mance, 2011; Saenz et al., 2002, 2003; Serences &Boynton, 2007; White & Carrasco, 2011; Zhang &Luck, 2008). Our observed attentional enhancementthus suggests that a similar feature-enhancementmechanism also operates in our motion detection task.Importantly, we also systematically manipulated thetarget offset from the cued direction, which allowed usto measure how attention modulates the activity ofdirection selective mechanisms. Here we observed asuppression effect in most of the offset conditions.Interestingly, the suppression effect exhibited a U-shaped function such that maximum suppressionoccurred for offsets close to orthogonal direction (908),but no suppression was found for the oppositedirection (1808). Interestingly, this ‘‘rebound effect’’was not predicted by single-unit data from monkey MT(Martinez-Trujillo & Treue, 2004; Treue & Martinez-Trujillo, 1999), which showed maximal suppression forneurons tuned to the opposite direction as the attendeddirection. Thus, if detection relies on the neurons mostsensitive to the target feature (Butts & Goldman, 2006;Hol & Treue, 2001; Jazayeri & Movshon, 2006; Regan& Beverley, 1985), one would expect a monotonicmodulation function such that the opposite directionwill exhibit the largest suppression. This was clearly notwhat we observed. The implications of this finding willbe discussed in more detail in the General discussion.For now, we note that our experiment revealed a robustsuppression effect due to feature-based attention tomotion direction.

Experiment 2: Attention toorientation

Experiment 2 was designed to investigate whetherattention-induced suppression is a general phenomenonor specific to motion direction. Orientation is one of themost important visual features and has been widelyused in studies of feature-based attention. However, itis not clear whether attention to an orientation causes asuppression of other orientations and if such suppres-sion also exhibits a ‘‘rebound’’ effect. Here we used a

similar experimental paradigm to test the effect oforientation-based attention.

Methods

Participants

Six observers participated in this experiment; allwere graduate and undergraduate students at MichiganState University and were naive as to the purpose of theexperiment. All participants had normal or corrected-to-normal vision. Participants gave informed consentand all were compensated at the rate of $10/hr. Fourobservers also participated in Experiment 1.

Visual stimuli

The orientation stimuli were Gabor patches (spatialfrequency, 2 cycles/8) in a circular aperture (size, 68)presented on a gray background (56 cd/m2). Thecircular aperture was centered on the fixation cross(black, size: 0.28), which was presented in the center ofthe screen throughout the experiment. The maskstimulus contained random pixel noise in the sameaperture, with pixel luminance drawn from a Gaussiandistribution (M¼ 56 cd/m2, SD¼ 28 cd/m2), truncatedat 62 standard deviations. The stimuli were presentedon a 19-in. CRT monitor (1024 · 768 pixel, 60-Hzrefresh rate, with gamma-corrected luminance levels),and observers viewed the display at a distance of 57 cm,with their heads stabilized via a chinrest in a darkroom.

Task and procedures: Training

Similar to Experiment 1, here we first trained eachobserver on the task to select appropriate stimuluslevels for the attention task. Observers detected thepresence of a Gabor patch in a 2-IFC task (Figure 4).Each trial started with a 0.5-s fixation interval, then a0.5-s blank interval, after which two intervals of stimuliwere shown, separated by 0.5 s. Each interval containedfive frames of stimulus, with each frame presented for0.017 s (one video frame). In the noise interval, all fiveframes were noise stimuli (each noise stimulus wasrandomly generated on each trial); in the signalinterval, the third frame was a Gabor patch while theremaining four frames were noise stimuli (Figure 4).The contrast of the Gabor patch was varied via themethod of constant stimuli: 0.12, 0.18, 0.24, 0.36, 0.48,0.60. The presentation order of the signal and noiseintervals was randomized. Observers were instructed toreport which interval contained the signal (Gaborpatch) by pressing the 1 or 2 key on the numeric keypadof a standard computer keyboard. Observers wereinstructed to respond as accurately as possible. Asound was played as feedback on incorrect trials after

Journal of Vision (2015) 15(5):15, 1–16 Wang, Miller, & Liu 5

Downloaded From: http://jov.arvojournals.org/pdfaccess.ashx?url=/data/Journals/JOV/933757/ on 04/29/2015 Terms of Use:

observer response, after which an intertrial interval of1.5 s followed.

We fit the proportion correct data with a Weibullfunction using psignifit (Wichmann & Hill, 2001; seeFigure 2B for an example fit) and defined threshold asthe contrast level corresponding to ;65% correct in the2-IFC task. Observers performed the orientationdetection task in 60-trial blocks until their contrastthreshold did not show a sizeable drop in consecutivefour-block units. On average, the practice session was18 blocks long (i.e., 1,080 trials), with a range of 16–21blocks across observers. The training task was dividedinto two sessions, each lasting about 40 min.

Task and procedures: Attention

The attention task was similar to the training task,except we introduced a cueing and validity manipula-tion. For the orientation signal, the contrast level wasdetermined by data in the training sessions (see above).At the beginning of the trial, a line cue appeared for0.5 s (length: 0.68, drawn through fixation), to indicatethe likely orientation of the signal. The cue was valid on69% of trials, when the signal was in the cuedorientation. On the invalid trials (31%), the signal wasin a different orientation than the cued orientation,with the difference set to one of six possible offsetvalues (6308, 6608, 6908). On each trial, the cue’sdirection was randomly sampled from 30 possibleorientations ranging from 08 to 1748 (step size¼ 68).Observers were instructed to attend to the cuedorientation, as it would help them detect the orientationsignal. We also ran a neutral (no-cue) condition inseparate blocks, in which the fixation dot turned greento indicate the impending stimuli. Signal orientation inthe neutral trials was randomly sampled from the same30 possible orientations as in the cued trials. In both the

cued and neutral condition, observers were instructedto report which interval contained the orientationsignal (Gabor patch) by pressing the 1 or 2 key on thenumeric keypad.

Each session consisted of five blocks, with oneneutral block and four cued blocks. The order of theseblocks was randomized for each session. The neutralblock contained 48 trials and cued block contained 60trials. Each observer completed two sessions, for a totalof 96 neutral trials and 480 cued trials, which wascomposed of ;330 valid trials and ;25 invalid trials ineach target offset (;50 trials when collapsed across thesign of the offset, see Results below).

Results and discussion

The average contrast threshold was 0.20 (SD¼ 0.03)as determined in the training sessions. We calculatedaccuracy as a function of the offset between the cue andsignal orientation. The clockwise and counterclockwiseoffsets showed similar results; hence we collapsedacross the sign of the offset to produce three offsetlevels (308, 608, 908). Average performance showed amonotonic decreasing function (Figure 5). Performancein the neutral condition, when participants had noexpectation of the signal orientation, served as thebaseline to assess attentional effects. The monotonicdecreasing function crossed the neutral performancelevel, showing both enhancement and suppressioneffects. Compared to the neutral condition, valid cues(offset 08) yielded significantly higher accuracy, t(5) ¼3.83, p , 0.05, while invalid cues produced significantlylower accuracy in the 908 offset condition, t908(5)¼3.13,p , 0.05, but not in the 308 and 608 offset conditions,t308(5)¼ 0.8, p¼ 0.46; t608(5)¼ 1.75, p¼ 0.14. We fit theaverage cued data with a linear, a quadratic, and a

Figure 4. Schematic of a trial in Experiment 2. Observers detected the presence of an oriented grating in a 2-IFC task. In one interval,

two noise frames preceded and followed the grating; in the other interval, five noise frames were shown consecutively.

Journal of Vision (2015) 15(5):15, 1–16 Wang, Miller, & Liu 6

Downloaded From: http://jov.arvojournals.org/pdfaccess.ashx?url=/data/Journals/JOV/933757/ on 04/29/2015 Terms of Use:

zero-slope linear (no-modulation) function. Modelcomparison using BIC revealed a linear fit to be a betterdescription of the data than a quadratic fit (Bayesfactor¼ 1.5) and the no-modulation fit (Bayes factor¼625).

We found a significant facilitation in performancewhen the cued orientation was consistent with thetarget orientation, which validates the cueing manipu-lation in this experiment. Again, this result suggeststhat the processing of the cued orientation feature wasenhanced, consistent with previous studies of feature-based attention. Importantly, when target orientationdiffered from the cued orientation, a monotonic declineof performance was observed, such that the orthogonalorientation was significantly suppressed. Interestingly,we did not observe a rebound effect as in Experiment 1where the most dissimilar feature showed less sup-pression than intermediate features. Thus the reboundeffect is likely not a general phenomenon in feature-based attention, but could be specific to direction ofmotion. The monotonic decline is more consistent withthe prediction of the feature-similarity gain model.

Experiment 3: Attention to color

In this experiment, we further explored attention-induced suppression in the domain of color. Color isanother salient visual feature that has been extensivelyused in studies of visual attention. However, previous

research tended to treat colors as discrete categories(e.g., searching a red item among blue and green items),thus making it difficult to investigate how attentionaffects the underlying visual representation of color,which is based on the continuous physical attribute ofwavelength. This is presumably caused by the com-plexity of color mechanisms (Eskew, 2009), as well asthe technical difficulty in manipulating color stimuli.

We are inspired by recent work in visual perceptionand memory using the Commission internationale del’eclairage (CIE) color space. For example, Brouwerand Heeger (2009) found that fMRI response patternsin high-level visual areas can be used to reconstruct theCIE color space. Zhang and Luck (2008) showed thatmemory representation for colors can be modeled by anormal distribution in the CIE color space. Theseresults suggest that the CIE color space (specifically,the CIE L*a*b space) can serve as a proxy of theinternal color representation, which provided a repre-sentational space for us to investigate the effect ofattention on color representation. We devised a coloranalog of the motion coherence stimulus and askedobservers to detect a color target in a 2-IFC proceduresimilar to previous experiments to test the effect ofcolor-based attention.

Methods

Participants

Six graduate and undergraduate students fromMichigan State University participated in this experi-ment. All participants had normal or corrected-to-normal vision and reported to have normal colorvision. We informally assessed their color vision bypresenting them the Dvorine Pseudo-IsochromaticPlates (Dvorine, 1963), for which all participantscorrectly identified all the numbers. Participants gaveinformed consent and were compensated at the rate of$10/hr. One of the observers participated in the othertwo experiments.

Visual stimuli

The color stimuli were static arrays of 240 coloreddots (dot size: 0.18), whose locations were restricted to a48-thick annulus centered on the fixation (inner radius¼18, outer radius¼58). The dots were drawn in one of sixpossible colors, which were evenly spaced along a circlein CIE L*a* b* color space (radius¼ 79, a¼ 25, b¼ 38,luminance ¼ 74). The hues were selected based on apilot color-naming experiment, which showed thatthese colors can be roughly described as orange, yellow,green, blue, purple, and red (in order of appearance onthe circle; Figure 6B). The spatial location of each dotwas randomly assigned for each stimulus. Stimuli were

Figure 5. Results for Experiment 2. Group averaged accuracy as

a function of the cue-to-target offset in orientation. Other

notations are same as in Figure 2. The solid line is a linear fit of

the average accuracy in the cued trials as a function of

orientation offset.

Journal of Vision (2015) 15(5):15, 1–16 Wang, Miller, & Liu 7

Downloaded From: http://jov.arvojournals.org/pdfaccess.ashx?url=/data/Journals/JOV/933757/ on 04/29/2015 Terms of Use:

presented on a 21-in. CRT monitor with a refresh rateof 100 Hz. The monitor was calibrated with an i1Prospectrophotometer (X-Rite, Grand Rapids, MI), toderive the transformation from the CIE L*a*b space tothe monitor RGB space (Westland & Ripamonti,2004). Participants rested their heads on a chin rest thatwas positioned 57 cm away from the monitor.

We introduced a color analog of motion coherenceusing the dot stimuli. Zero color coherence is defined asequal proportion in the number of dots among all sixcolors (i.e., 40 dots per color), whereas a nonzerocoherence indicates one color had a larger proportionthan the other colors. In the latter case, the color with alarger proportion is referred to as dominant color, andthe other five colors are equally proportioned. Nu-merically, the coherence was defined by the followingequation:

color coherence ¼ Ps� Pn

where Ps is the proportion of dots in the dominantcolor, and Pn is proportion of dots in each nondom-inant color, with the following constraint:

Pn ¼ ð1� PsÞ=5

That is, all nondominant colors are equally propor-tioned after accounting for the dominant color.

Task and procedures: Training

We first trained each observer on the task to selectappropriate color coherence levels for the attentiontask. Observers detected the presence of a dominantcolor in a 2-IFC task (Figure 6A). Each trial startedwith a 0.5-s fixation period, after which two intervals ofstimuli were shown, each for 0.3 s and separated by 0.4s. One interval contained a 0% coherent stimulus wherethe six colors were equal in proportion (noise), whilethe other interval contained a nonzero coherentstimulus where one color had a larger proportion thanthe other five colors (signal). We used the method ofconstant stimuli with six coherence levels (1%, 5%,10%, 15%, 20%, 25%, as defined above) to trainobservers on the basic color detection task. Observerswere instructed to report the interval that contained thedominant color by pressing the 1 or 2 key on thenumeric keypad of a standard computer keyboard, andthey were instructed to respond as accurately aspossible. A sound was played as feedback on incorrecttrials after observer response, after which an intertrialinterval of 1.5 s followed.

We fit the proportion correct data with a Weibullfunction using psignifit (Wichmann & Hill, 2001; seeFigure 2C for an example fit) and found the data couldbe well fit by a Weibull. We defined threshold as the

Figure 6. (A) Schematic of a trial in Experiment 3. Observers detected the presence of a dominant color in a 2-IFC task. (B) Depiction

of the color wheel in the CIE L*a*b space. The circles represent the six equally spaced hues on the wheel that were used as colors for

the dot stimuli. (C) Average accuracy on neutral trials for individual colors. Error bars are the estimated within-subject standard error.

Journal of Vision (2015) 15(5):15, 1–16 Wang, Miller, & Liu 8

Downloaded From: http://jov.arvojournals.org/pdfaccess.ashx?url=/data/Journals/JOV/933757/ on 04/29/2015 Terms of Use:

coherence level corresponding to ;70% correct in the2-IFC task. Separate fits were performed for eachtarget color to derive six color coherence thresholds.Observers practiced the color detection task in blocksof 72 trials (average: 18 blocks, range: 15–21). Thewhole training procedure was divided into two to threesessions, each lasting about 45 min.

Task and procedures: Attention

The attention task is similar to the training task,except we introduced a cueing and validity manipula-tion. For the strength of the color signal, we usedcoherence levels as measured in the training sessions(see above). Note the signal strength was different foreach target color, as determined in the training sessions.This was done to compensate for possible differences insensitivity to different colors. At the beginning of thetrial, a color cue appeared for 0.6 s (radius 0.38, drawnon fixation), to indicate the likely color of the signal.The cue was valid on 70% of trials, when the signal wasthe same color as the cue. In the remaining 30% of trials(invalid trials), the signal had a different color than thecue, with the difference set to one of six possible offsetvalues along the color wheel (6608, 61208, 61808). Oneach trial, the cued color was randomly selected fromthe six colors (orange, yellow, green, blue, purple, andred). Observers were instructed to attend to the cuedcolor, as it would help them to detect the color signal.We also ran the neutral (no-cue) condition in separateblocks, in which the fixation dot turned gray to indicatethe impending stimuli. In both the cued and neutralcondition, observers were instructed to report whichinterval contained the color signal by pressing the 1 or 2key on the numeric keypad. Observers performed thecolor detection task in three separate sessions, with sixneutral blocks and 20 cued blocks in total. The neutralblock contained 30 trials and cued block contained 96trials. In total, each observer completed 180 neutraland 1920 cued trials, which was composed of 224 validtrials and 16 invalid trials in each target offset for eachcolor (;32 trials when collapsed across the sign of theoffset).

Results and discussion

The average coherence threshold across colors andobservers was 0.13 (SD ¼ 0.01). Because we measuredseparate coherence threshold for each color, we firstexamined performance in the neutral condition for thesix colors. Figure 6C shows average performance acrossobservers in the neutral condition for each of the sixcolors. Although performance was around 70% (ex-pected level given the thresholding procedure), therewere also some variations among colors. This is

probably caused by relatively small amount of datacollected for each color in the thresholding sessions,making the estimate less accurate. Importantly, per-formance for all colors was in an intermediate rangesuch that both attentional enhancement and suppres-sion can be observed (i.e., no floor or ceiling effect inthe neutral condition).

For the cued trials, we calculated accuracy as afunction of the offset between the cued and signalcolor. Because of the variation in neutral performanceamong different colors, we used the difference betweeneach color’s cued and neutral scores as the measure ofattentional effects in all subsequent analysis. We firstexamined attentional effects for individual cued colors(Figure 7). In general, the cued colors had the highestperformance level and were consistently above zero,whereas the uncued colors showed a decline inperformance. The decline was generally monotonic andalways reached below zero for large offsets. Given thesimilarity of attentional effects across colors, weaveraged these modulation functions across colors andcollapsed across positive and negative offsets. The finalcolor modulation function exhibited a monotonicdecreasing shape as the cue-target offset increased(Figure 8). Note because we plotted the difference scorebetween cued and neutral condition, a positive valuewould indicate attentional enhancement and a negativevalue would indicate attentional suppression. The colormodulation function crossed the zero point, showingboth enhancement and suppression due to feature-based attention. We used one-sample t tests to comparethe cueing effect to 0, to assess the statistical reliabilityof enhancement and suppression effects. Valid cues(offset 08) yielded significant enhancement, t08(5)¼4.52,p , 0.01, whereas invalid cues produced a suppressioneffect at all offset values, t608(5)¼3.59, p , 0.05; t1208(5)¼ 4.78, p , 0.01; t1808(5)¼ 5.98, p , 0.01. We fit theaverage cued data with a linear, a quadratic, and azero-slope linear (no-modulation) function. Modelcomparison using BIC found a quadratic fit to be abetter description of the data than a linear fit (Bayesfactor ¼ 4.05 · 105) and the no-modulation fit (Bayesfactor ¼ 1.67 · 107).

We found attending to a color produced facilitationwhen the target color was consistent with the cuedcolor and suppression when target color deviated fromthe cued color. The shape of the attentional modulationfollowed a monotonic decreasing function such thatmaximum suppression occurred for the color oppositeto the cued color on the color circle (1808 offset).Overall, these results are consistent with predictions ofthe feature-similarity gain model. Thus, the model,although first proposed based on results from motiondirection (Martinez-Trujillo & Treue, 2004; Treue &Martinez-Trujillo, 1999), appears to also operate in thedomain of color-based attention.

Journal of Vision (2015) 15(5):15, 1–16 Wang, Miller, & Liu 9

Downloaded From: http://jov.arvojournals.org/pdfaccess.ashx?url=/data/Journals/JOV/933757/ on 04/29/2015 Terms of Use:

General discussion

We conducted three experiments with a detectionparadigm and found that feature-based attentionmodulated performance via both enhancement andsuppression along the feature dimension. In Experi-ment 1, observers’ performance profiles relating thecued and actual target direction followed a U-shapefunction, with an enhancement effect when cue-to-target separation were around 08, and a suppressioneffect when cue-to-target separation were around 908,

and finally a rebound effect when that separation waslarger than 908. In Experiment 2, the performanceprofile relating the cued and actual orientation showeda monotonic decreasing trend, where suppression wasmost pronounced when cue-to-target separation was908. In Experiment 3, the performance profile relatingthe cued and actual color also showed a monotonicdecreasing trend, with maximum suppression occurringat cue-to-target separation of 1808. There was noappreciable rebound effect in the latter two experi-ments. These results largely support the feature-similarity gain model by providing a perceptualcorrelate for the underlying neuronal modulation dueto attention.

Relationship to previous work

Several previous studies have measured the effect offeature-based attention on feature-selective mecha-nisms. In addition to the Ho et al. (2012) and Tombuand Tsotsos (2008) and studies mentioned in theIntroduction, three other studies have also reportedeffects of attention on feature tuning. Using a maskingtechnique, Baldassi and Verghese (2005) found that anorientation cue selectively reduced detection thresholdat the cued orientation. Paltoglou and Neri (2012) useda noise classification image technique to measuretuning and found feature-based attention sharpeneddirection tuning but not orientation tuning. Ling, Liu,& Carrasco (2009) measured direction discrimination

Figure 8. Results for Experiment 3, collapsed across individual

cued colors, showing cueing effect as a function of the cue-to-

target offset on the color circle. Other notations are the same

as in Figure 2. The solid line is a quadratic fit of the cueing effect

as a function of color offset.

Figure 7. Results for individual cued colors in Experiment 3.

Each panel plots the group averaged cueing effect, defined as

performance difference between cued versus neutral trials for a

particular cued color. The color of the target stimuli was shown

as the color of the plot symbol, with the color at 08 as the cued

color. Error bars are the estimated within-subject standard

error.

Journal of Vision (2015) 15(5):15, 1–16 Wang, Miller, & Liu 10

Downloaded From: http://jov.arvojournals.org/pdfaccess.ashx?url=/data/Journals/JOV/933757/ on 04/29/2015 Terms of Use:

threshold with an external noise technique and used acomputational model to infer that feature-basedattention both enhanced and suppressed neural popu-lation response in a manner consistent with the feature-similarity gain model.

These studies used similar attentional manipulationsin that observers were cued to attend to a particularfeature prior to stimulus presentation. Beyond thiscommonality, however, there were many variations indesign choices across experiments in terms of stimulus,task, and the measure of tuning. Perhaps not surpris-ingly, the results are also divergent. Whereas twostudies (Baldassi & Verghese, 2005; Paltoglou & Neri,2012) did not find evidence for suppression (anotherstudy by White & Carrasco, 2011, also failed to findsuppression for the feature opposite to the cuedfeature), the other three studies (Ho et al., 2012; Tombu& Tsotsos, 2008; Ling et al., 2009) all reported somedegree of suppression. Among the latter studies, theexact profile of attention-induced suppression alsodiffered. Thus there lacks a consistent picture fromthese studies, and it is difficult to compare them directlydue to significant variations in the experimentalmethodology.

We would like to offer that our study was optimizedto measure the effect of attention on feature-selectivemechanisms. First, we presented only one stimulus at atime in a fixed location, unlike most previous studiesthat presented multiple stimuli for which participantshad to locate the target. This type of search-likeparadigm would require shifts of spatial attention.Thus both spatial and feature-based attention wasinvolved in shaping performance, which could compli-cate the interpretation of the results. Our 2-IFC taskonly presented a single stimulus at fixation, and thuseliminated the contribution of spatial attention. Sec-ond, the perceptual judgment in our task (determiningwhich interval contained the target) was orthogonal tothe cued feature. This is important because perfor-mance was not influenced by participants’ perceivedsimilarity among features. Had we asked participantsto report which interval contained the cued feature, wewould be measuring perceived similarity among fea-tures, instead of how attention affected featurerepresentation (see Paltoglou & Neri, 2012, for morediscussions). Lastly, our measure of attentional mod-ulation is based on performance in a detection task,which presumably relies most on neurons tuned to thetarget feature (Butts & Goldman, 2006; Hol & Treue,2001; Jazayeri & Movshon, 2006; Regan & Beverley,1985). Thus performance on our task provides a closeproxy to the underlying neural activity. In contrast,some previous studies relied on sophisticated dataprocessing and modeling, for example, the psycho-physical reverse correlation, which assumes a linearobserver model (Paltoglou & Neri, 2012), or a

computational model (Ling et al., 2009), which relies onassumptions about the neural architecture and neuro-nal response properties. Based on these considerations,we believe our simple protocol provides the most directpsychophysical measure of the effect of feature-basedattention that is easy to interpret without complicationsdue to design and analytic factors.

Attentional modulation of motion direction

In Experiment 1, attending to a direction produced aU-shaped modulation profile such that motion detec-tion was enhanced for target moving in the attendeddirection, but impaired for a wide range of intermediatedirections, and showed a rebound effect in the oppositedirection. The rebound effect seems to be a robustfinding, as it was also observed by Ho et al. (2012)under a different experimental setup. This nonmono-tonic modulation profile is unexpected given neuro-physiological data showing a monotonic modulation ofMT neuronal activity as a function of the differencebetween the preferred and attended direction (Marti-nez-Trujillo & Treue, 2004).

One possible reconciliation between neurophysio-logical and behavioral data is that observers voluntarilyattended to the axis of motion when cued to a direction.We think this is unlikely because we emphasized thedistinction between direction and axis in our instruc-tions and all participants were well-trained psycho-physical observers. In addition, if participants alwaysattended to the motion axis, we should expect equalperformance in the valid (08 offset) and opposite (1808offset) direction, which was clearly not the case.

Another possibility is the existence of neuralmechanisms tuned to the axis of motion. Direction-selective neurons tuned to two opposite directions havebeen found in both MT (Albright, 1984) and V1(Conway & Livingstone, 2003; Livingstone & Conway,2003). These neurons were probably not sampled inneurophysiological studies of attention because theyconstitute a minority of neurons in the visual cortexand researchers usually record from a limited numberof neurons that reach certain inclusion criterion(Martinez-Trujillo & Treue, 2004; Treue & Martinez-Trujillo, 1999). However, these opposite-tuned neuronscould contribute to performance in perceptual tasks.For example, it is possible that attention to upwarddirection enhances activity in neurons tuned to upwardmotion, as well as in neurons tuned to both upward anddownward motion, and the enhancement in this lattergroup of neurons makes it easier to detect the 1808offset direction in our task.

To get further insight on this issue, we assessedparticipants’ ability to report motion direction in ourtask without any attentional cueing. In a subset of

Journal of Vision (2015) 15(5):15, 1–16 Wang, Miller, & Liu 11

Downloaded From: http://jov.arvojournals.org/pdfaccess.ashx?url=/data/Journals/JOV/933757/ on 04/29/2015 Terms of Use:

training trials during Experiment 1, we asked partici-pants to report the direction of the target (after theyhad indicated which interval contained the target), byclicking on a circle with a computer mouse to indicatethe target direction. We then calculated the angulardifference between the true target direction and thereported direction on trials that they correctly reportedthe target interval. The distributions of these direc-tional errors were plotted in angular histograms inFigure 9. At high coherence (32%), the vast majority oferrors centered around zero, suggesting participantsclearly perceived the correct motion direction and byextension understood the concept of direction (asopposed to axis or orientation). However, even at thisrelatively high coherence level, there was a visibleoverrepresentation in the opposite direction at 1808. Atthe two lower coherence levels (8% and 16%), whichwere comparable to the coherence level used inExperiment 1 (10%), we observed a decrease in theproportion of 08 responses and an increase in the 1808responses. These results suggest that motions in theopposite directions are more easily confused with eachother, and the results are consistent with the notionthat bidirectional tuning might underlie performance inthe motion detection task. Thus, we believe our resultscan still be consistent with the feature-similarity gainmodel, if one takes into account of the bidirectionaltuning in the visual cortex.

Attentional modulation of orientation and color

In Experiment 2, we found a monotonic attentionalmodulation for orientation such that as the targetorientation deviated more from the cued orientation,performance gradually declined. This result is directlypredicted by the feature-similarity gain model. Incontrast to a previous study (Tombu & Tsotsos, 2008),we did not observe any rebound effect. However, thislatter study did not include a neutral condition, makingit difficult to assess any suppression effect. Further-

more, the task required participants to judge whetherstripes were straight or jagged, and attentional effectwas only observed for jagged stimuli. This may becaused by the fact that the jagged stimuli were createdby shifting segments of the stripes in an approximatelyorthogonal direction to the stripes’ main orientation(see figure 1 in Tombu & Tsotsos, 2008), making iteasier to detect the jaggedness if participants wereattending to the 908 offset orientation. Thus we believethe observed rebound effect in Tombu and Tsotsos(2008) might be due to specific choices in their stimulusand task design. Our orientation data showed amonotonic modulation profile, suggesting that atten-tion enhances neurons tuned to the attended orienta-tion but suppresses neurons tuned to other orientationswith the amount of suppression scaling with thedissimilarity between cued and preferred orientation,that is, the principle of feature-similarity gain (Marti-nez-Trujillo & Treue, 2004; Treue & Martinez-Trujillo,1999). Thus, although the feature-similarity model wasbased on measurement of MT neurons’ response tomotion stimuli, our data predict that orientation-tunedneurons will exhibit similar attentional modulationswhen attention is directed to orientation.

In Experiment 3, we again found a monotonic-modulation function for attention to color. As thetarget color deviated more from the cued color,detection performance showed a gradual decline. Thisresult is also consistent with the feature-similarity gainmodel, when applied to the dimension of hue specifiedby angles in the CIE color space. This might besomewhat surprising, as early stages of color processingis dominated by the trichromatic mechanism on theretina and opponent process in lateral geniculatenucleus (LGN) and V1 (Soloman & Lennie, 2007),neither of which exhibits tuning to hues in a continuousfashion. However, recent work has suggested thatextrastriate areas such as V4 contain mechanisms tunedto hues along the spectrum (Brouwer & Heeger, 2009;Conway, Moeller, & Tsao, 2007). It is thus plausiblethat attention modulates these hue-tuned neuronalmechanisms according to the feature-similarity gain

Figure 9. Pointing results from a subset of practice sessions in Experiment 1. Distribution of errors in pointing to target direction for

three coherence levels, depicted as angular histograms in polar coordinate. Only trials on which observers correctly reported the

target interval were shown. Coherence and number of trials are shown at the top of each panel.

Journal of Vision (2015) 15(5):15, 1–16 Wang, Miller, & Liu 12

Downloaded From: http://jov.arvojournals.org/pdfaccess.ashx?url=/data/Journals/JOV/933757/ on 04/29/2015 Terms of Use:

principle. Our data would predict such a result in aneurophysiological experiment.

Although the profile of attentional modulation isquite similar for color and orientation, we alsoobserved a subtle difference, in that orientationmodulation is best described by a linear trend, whereascolor modulation is best described by a quadratic trend.Basically, the suppression of orientation was propor-tional to the orientation offset, whereas the suppressionof color was reduced at large hue offsets. This couldreflect a more uniform representation of featuresimilarity for orientation than that for hues. Perceptu-ally, it seems to be the case that orientation similarity isproportional to differences in angle, whereas huesimilarity is less so—at large offsets, two colors willappear quite dissimilar regardless of their separation(e.g., both the green and blue colors appear verydifferent from the red color in Figure 6B, although redis closer to blue than to green in the color space). Thisimplies that the modulation due to feature-similaritygain is not strictly linear for color-tuned mechanisms.

A recent study by Stormer and Alvarez (2014)reported a ‘‘surround suppression’’ effect in color spacein a feature-based attention task. In a behavioralexperiment, they presented colored dots in both the leftand right visual fields and asked observers to detectbrief intervals of coherent motion in both dot fields,while varying the similarity between the dot colors. Inaddition, dots of a third color were used as distractersin both visual fields and participants were cued toattend to two colors on each trial. They found thatdetection accuracy decreased and then increased as afunction of the similarity between the two cued colors.Using a similar paradigm in a subsequent electroen-cephalography (EEG) experiment, they found steady-state visual evoked potential (SSVEP) amplitude for anignored color in the opposite hemifield to the attendedcolor also showed a decrease followed by an increase asa function of color similarity. These results areseemingly at odds with ours, which showed monotonicattentional modulation for color. However, there weremany differences in the tasks used between the studies,making it difficult to directly compare the results. Herewe note two important differences between the tasks.Stormer and Alvarez (2014) always presented adistracter color in the same location as the cued color,which might have encouraged a strategy to explicitlysuppress the distracter color. In addition, participantswere cued to attend to two colors in the behavioralexperiment, which showed a stronger surround sup-pression effect than the EEG experiment. The need toignore the distracter color and to attend to two colors,as well as the requirement to report on another featuredimension (direction of motion), made the taskssomewhat complicated, which could make it moredifficult to control strategic factors. In our task,

participants always attended to a single color in a singlelocation and reported about the color feature in astandard cue validity paradigm. We believe that thissimpler design allows for more direct measures of theeffect of attention to color. Further research is neededto reconcile these results regarding the surroundsuppression effect in attention to color.

Challenges and future work

We have interpreted our findings in the frameworkof attention modulating the sensory representation ofvisual features. This framework is supported byneurophysiological data showing feature-based atten-tion can modulate neuronal responses in visual cortex(Khayat et al., 2010; Martinez-Trujillo & Treue, 2004;Treue & Martinez-Trujillo, 1999). However, anotherprominent view in the literature is that attentionreduces uncertainty at the decision stage withoutaltering the sensory representation (e.g., Eckstein,Thomas, Palmer, & Shimozaki, 2000; Palmer, Vergh-ese, & Pavel, 2000; Verghese, 2001). Although thesetheories were originally proposed in the domain ofspatial attention, in principle, selective readout offeature detectors could also produce performancefacilitation and suppression in feature-based attentiontasks. A possible experimental remedy is to use apostcue to indicate the target feature, thus reducinguncertainty at the decision stage. However, postcuesfurther complicate the overall task and could alter taskstrategy, and it is unlikely that postcues can eliminateselective readout. Thus, based on behavioral dataalone, it would be difficult to distinguish the sensory-based versus decision-based accounts of performancemodulation due to attention. Regardless of the preciseunderlying mechanisms, however, our results stillprovide a systematic characterization of how attentionaffects perceptual performance in three feature dimen-sions.

These results can be used to constrain furtherdevelopment of models of attention. We believe apromising avenue of future research would be toconstruct neurally plausible, computational models ofperceptual performance (e.g., Eckstein, Peterson,Pham, & Droll, 2009; Jazayeri & Movshon, 2006). Onecan then implement the sensory- versus decision-basedmechanisms in the model and examine how well eachmechanism explains behavioral performance. Suchmodels will also allow us to examine how physiologicalparameters (such as neuronal tuning width, attentionalgain/suppression, and decision weights) influence be-havior. Ultimately, however, the distinction betweensensory- versus decision-based mechanisms might bebest resolved at the neuronal level. Here, it is worthpointing out that the original neuronal data on feature-

Journal of Vision (2015) 15(5):15, 1–16 Wang, Miller, & Liu 13

Downloaded From: http://jov.arvojournals.org/pdfaccess.ashx?url=/data/Journals/JOV/933757/ on 04/29/2015 Terms of Use:

similarity gain (Martinez-Trujillo & Treue, 2004) wereobtained with a task that is somewhat different fromthe tasks used in many other studies. Notably, theneutral condition required attention to the centralfixation, which might not be the ideal baselinecondition (see further discussions by White & Carrasco,2011). Thus, more neuronal level data obtained withprotocols that better isolate feature-based attentionwould be highly informative, particularly if combinedwith computational models that can link neuronalactivity to behavioral performance.

Summary and conclusion

We examined the profile of attentional modulationin a cueing paradigm combined with a validitymanipulation. We found suppression effects for allthree features we tested: direction of motion, orienta-tion, and color. The modulation profile exhibited a U-shaped function for direction, but largely linearfunctions for orientation and color. The data fororientation and color followed the predictions of thefeature-similarity gain model, whereas the directiondata were seemingly inconsistent with the model.However, after taking into account of neural mecha-nisms tuned to the axis of motion, the direction resultscan be reconciled with the predictions of feature-similarity gain. Overall, these results suggest that thefeature-similarity gain principle of attention operatesacross a variety of feature dimensions and can affectthe perception of features, in addition to neuronalactivity.

Keywords: attention, feature, suppression

Acknowledgments

We thank Rebecca Howe for assistance in datacollection. This work was supported by a NIH grant(R01EY022727).

Commercial relationships: none.Corresponding author: Taosheng Liu.Email: tsliu@msu.edu.Address: Department of Psychology, Michigan StateUniversity, East Lansing, MI, USA.

References

Albright, T. D. (1984). Direction and orientationselectivity of neurons in visual area MT of the

macaque. Journal of Neurophysiology, 52(6), 1106–1130.

Arman, A. C., Ciaramitaro, V. M., & Boynton, G. M.(2006). Effects of feature-based attention on themotion aftereffect at remote locations. VisionResearch, 46(18), 2968–2976.

Baldassi, S., & Verghese, P. (2005). Attention tolocations and features: Different top-down modu-lation of detector weights. Journal of Vision, 5(6),556–570, http://www.journalofvision.org/content/5/6/7, doi:10.1167/5.6.7. [PubMed] [Article]

Bondarenko, R., Boehler, C. N., Stoppel, C. M.,Heinze, H. J., Schoenfeld, M. A., & Hopf, J. M.(2012). Separable mechanisms underlying globalfeature-based attention. Journal of Neuroscience,32(44), 15284–15295.

Brouwer, G. J., & Heeger, D. J. (2009). Decoding andreconstructing color from responses in humanvisual cortex. Journal of Neuroscience, 29(44),13992–14003.

Butts, D. A., & Goldman, M. S. (2006). Tuning curves,neuronal variability, and sensory coding. PLoSBiology, 4, e92.

Carrasco, M. (2011). Visual attention: The past 25years. Vision Research, 51, 1484–1525.

Conway, B. R., & Livingstone, M. S. (2003). Space-time maps and two-bar interactions of differentclasses of direction-selective cells in macaque V1.Journal of Neurophysiology, 89(5), 2726–2742.

Conway, B. R., Moeller, S., & Tsao, D. Y. (2007).Specialized color modules in macaque extrastriatecortex. Neuron, 56, 560–573.

Dvorine, I. (1963). Dvorine pseudo-isochromatic plates.New York: Harcourt, Brace, & World.

Eckstein, M. P., Peterson, M. F., Pham, B. T., & Droll,J. A. (2009). Statistical decision theory to relateneurons to behavior in the study of covert visualattention. Vision Research, 49(10), 1097–1128.

Eckstein, M. P., Thomas, J. P., Palmer, J., &Shimozaki, S. S. (2000). A signal detection modelpredicts the effects of set size on visual search forfeature, conjunction, triple conjunction, and dis-junction displays. Perception & Psychophysics,62(3), 425–451.

Eskew, R. T. (2009). Higher order color mechanisms: Acritical review. Vision Research, 49, 2686–2704.

Ho, T. C., Brown, S., Abuyo, N. A., Ku, E. J., &Serences, J. T. (2012). Perceptual consequences offeature-based attentional enhancement and sup-pression. Journal of Vision, 12(8):15, 1–17, http://www.journalofvision.org/content/12/8/15, doi:10.1167/12.8.15. [PubMed] [Article]

Journal of Vision (2015) 15(5):15, 1–16 Wang, Miller, & Liu 14

Downloaded From: http://jov.arvojournals.org/pdfaccess.ashx?url=/data/Journals/JOV/933757/ on 04/29/2015 Terms of Use:

Hol, K., & Treue, S. (2001). Different populations ofneurons contribute to the detection and discrimi-nation of visual motion. Vision Research, 41, 685–689.

Jazayeri, M., & Movshon, J. A. (2006). Optimalrepresentation of sensory information by neuralpopulations. Nature Neuroscience, 9, 690–696.

Kastner, S., & Ungerleider, L. G. (2000). Mechanismsof visual attention in the human cortex. AnnualReview of Neuroscience, 23, 315–341.

Khayat, P. S., Neibergall, R., & Martinez-Trujillo, J. C.(2010). Attention differentially modulates similarneuronal responses evoked by varying contrast anddirection stimuli in area MT. Journal of Neurosci-ence, 30, 2188–2197.

Lankheet, M. J. M., & Vestraten, F. A. J. (1995).Attentional modulation of adaptation to two-component transparent motion. Vision Research,35(10), 1401–1412.

Ling, S., Liu, T., & Carrasco, M. (2009). How spatialand feature-based attention affect the gain andtuning of population responses. Vision Research,49, 1194–1204.

Liu, T., & Hou, Y. (2011). Global feature-basedattention to orientation. Journal of Vision, 11(10):8,1–8, http://www.journalofvision.org/content/11/10/8, doi:10.1167/11.10.8. [PubMed] [Article]

Liu, T., Larsson, J., & Carrasco, M. (2007). Feature-based attention modulates orientation-selectiveresponses in human visual cortex. Neuron, 55, 313–323.

Liu, T., & Mance, I. (2011). Constant spread of feature-based attention across the visual field. VisionResearch, 51, 26–33.

Livingstone, M. S., & Conway, B. R. (2003). Sub-structure of direction-selective receptive fields inmacaque V1. Journal of Neurophysiology, 89(5),2743–2759.

Loftus, G. R., & Masson, M. E. (1994). Usingconfidence intervals in within-subject designs.Psychonomic Bulletin & Review, 1, 476–490.

Martinez-Trujillo, J. C., & Treue, S. (2004). Feature-based attention increases the selectivity of popula-tion responses in primate visual cortex. CurrentBiology, 14, 744–751.

Maunsell, J. H. R., & Treue, S. (2006). Feature-basedattention in visual cortex. Trends in Neuroscience,29, 317–322.

Newsome, W. T., & Pare, E. B. (1988). A selectiveimpairment of motion perception following lesionsof the middle temporal visual area (MT). Journal ofNeuroscience, 8, 2201–2211.

Palmer, J., Verghese, P., & Pavel, M. (2000). Thepsychophysics of visual search. Vision Research,40(10–12), 1227–1268.

Paltoglou, A. E., & Neri, P. (2012). Attentionalcontrol of sensory tuning in human visual per-ception. Journal of Neurophysiology, 107(5), 1260–1274.

Regan, D., & Beverley, K. I. (1985). Postadaptationorientation discrimination. Journal of the OpticalSociety America A, 2, 147–155.

Reynolds, J. H., & Chelazzi, L. (2004). Attentionalmodulation of visual processing. Annual Review ofNeuroscience, 27, 611–647.

Saenz, M., Buracas, G. T., & Boynton, G. M. (2002).Global effects of feature-based attention in humanvisual cortex. Nature Neuroscience, 5(7), 631–632.

Saenz, M., Buracas, G. T., & Boynton, G. M. (2003).Global feature-based attention for motion andcolor. Vision Research, 43, 629–637.

Scolari, M., Ester, E. F., & Serences, J. T. (2014).Feature- and object-based attentional modulationin the human visual system. In K. Nobre & S.Kastner (Eds.), Oxford handbook of attention.Oxford, UK: Oxford University.

Serences, J. T., & Boynton, G. M. (2007). Therepresentation of behavioral choice for motion inhuman visual cortex. Journal of Neuroscience,27(47), 12893–12899.

Soloman, S. G., & Lennie, P. (2007). The machinery ofcolour vision. Nature Review Neuroscience, 8(4),276–286.

Stormer, V. S., & Alvarez, G. A. (2014). Feature-basedattention elicits surround suppression in featurespace. Current Biology, 24(17), 1985–1988.

Tombu, M., & Tsotsos, J. K. (2008). Attending toorientation results in an inhibitory surround inorientation space. Perception & Psychophysics,70(1), 30–35.

Treue, S., & Martinez-Trujillo, J.C. (1999). Feature-based attention influences motion processing gainin macaque visual cortex. Nature, 399, 575–579.

Verghese, P. (2001). Visual search and attention: Asignal detection theory approach. Neuron, 31(4),523–535.

Westland, S., & Ripamonti, C. (2004). Computationalcolour science using MATLAB. Chichester, UK:John Wiley & Sons.

White, A. L., & Carrasco, M. (2011). Feature-basedattention involuntarily and simultaneously im-proves visual performance across locations. Journalof Vision, 11(6):15, 1–10, http://www.

Journal of Vision (2015) 15(5):15, 1–16 Wang, Miller, & Liu 15

Downloaded From: http://jov.arvojournals.org/pdfaccess.ashx?url=/data/Journals/JOV/933757/ on 04/29/2015 Terms of Use:

journalofvision.org/content/11/6/15, doi:10.1167/

11.6.15. [PubMed] [Article]

Wichmann, F. A., & Hill, N. J. (2001). The psycho-

metric function: I. Fitting, sampling, and goodness

of fit. Perception & Psychophysics, 63(8), 1293–1313.

Zhang, W., & Luck, S. J. (2008). Discrete fixed-resolution representations in visual working mem-ory. Nature, 453(8), 233–236.

Journal of Vision (2015) 15(5):15, 1–16 Wang, Miller, & Liu 16

Downloaded From: http://jov.arvojournals.org/pdfaccess.ashx?url=/data/Journals/JOV/933757/ on 04/29/2015 Terms of Use: