Capacity limit of feature-based attention to color

Taosheng Liu1,2, Michael Jigo1, Samantha Blair1

1Department of Psychology, Michigan State University, East Lansing, MI 2Neuroscience Program, Michigan State University, East Lansing, MI

Correspondence:

Taosheng Liu PhD Department of Psychology Michigan State Univeristy 316 Physics Rd East Lansing, MI 48864 Email: tsliu@msu.edu

Acknowledgment:

We would like to thank Yixue Wang for help in data collection. This work is supported by a grant from NIH (EY022727).

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Abstract

Attention to a feature enhances the sensory representation of that feature. Although much

has been learned about the properties of attentional modulation when attending to a single

feature, the capacity of feature-based attention is not well established. We investigated

this question in a series of experiments using a color detection task while varying the

number of precues that directed participants’ attention to colors. We measured the

threshold for color detection in a color coherence stimulus. We found consistent

facilitation of detection performance in the one-cue condition compared to the no-cue and

two-cue conditions, and no improvement in the two-cue condition compared to the no-

cue condition. Additional experiments demonstrated that insufficient cue-processing time

or selectively attending to a single color could not explain performance in the two-cue

condition. Thus, our results show that we can only attend to a single color at a time. This

severe capacity limit is likely due to an inability to simultaneously modulate

representations of multiple colors with attention. We further discuss the implications of

our results for studies on attentional control setting and states of working memory.

Keywords: visual attention, feature, color, capacity

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Introduction

Attention is a crucial cognitive mechanism that selects a limited set of sensory

input from the environment for prioritized processing. Classical studies of visual attention

have emphasized the selection of spatial locations (Posner, 1980; Carrasco, 2006). More

recent studies have also shown that attention can select non-spatial properties such as

features and objects (Maunsell & Treue, 2006; Scolari, Ester, & Serences, 2014). In this

research we focus on feature-based attention, which refers to the selection of specific

values within a feature dimension (e.g., selecting the red color among other colors,

selecting the vertical orientation among other orientations).

It is now well-established that attending to a feature can enhance its early sensory

representations, as shown by a variety of studies employing psychophysical (Arman et

al., 2006; Liu & Hou, 2011; Liu & Mance, 2011; Saenz et al., 2003; White & Carrasco,

2011), neurophysiological (Cohen & Maunsell, 2011; Martinez-Trujillo & Treue, 2004)

and brain imaging measures (Liu, Larsson, & Carrasco, 2007; Saenz et al., 2002). An

enhanced feature representation would be useful for other cognitive operations requiring

the processing of that feature (e.g., during visual search for a specific feature). This body

of work generally tested attention to a single feature, thus leaving open an important

question regarding the capacity of feature-based attention, i.e., how many features can be

attended to simultaneously? Answering this question would deepen our understanding of

the mechanisms of attention, and also have practical implications on optimizing human

performance in visually guided tasks.

The capacity of feature-based attention has received limited investigation. Two

previous studies used directional cues to direct attention to motion and manipulated the

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reliability of the cue (Ball & Sekuler, 1981; Herrmann, Heeger, & Carrasco, 2012). A

reliable cue would indicate a narrow range of possible directions for an upcoming

moving target, whereas a unreliable cue would indicate a wide range of possible target

directions. It was found that performance deteriorated as the cue became less reliable.

This implies a limit in feature-based attention in that attention cannot be directed to more

directions as effectively as to fewer directions. However, these studies do not provide a

precise estimate of the capacity of feature-based attention, nor were they designed to

achieve such an objective. A recent study by us addressed this question by manipulating

the number of discrete directional cues in a motion detection task (Liu, Becker, & Jigo,

2013). Compared to a baseline neutral condition, performance was improved when

participants attended to a single direction, as well as when they attended to two

orthogonal directions. However, there was a significant performance decrement when

attending to two directions compared to attending to a single direction, thus revealing a

limitation in our ability to attend to multiple directions. What we could not ascertain,

however, was whether participants could only attend to one direction, or, they could

attend to two directions but with less efficiency. Both scenarios are possible explanations

of our previous results on attention to motion (Liu, Becker, & Jigo, 2013).

In the present study, we investigated the capacity of attention to the color feature,

due to the following considerations. First, color is an intrinsically important visual feature

and has been shown to be particularly effective in guiding attention (Motter & Belky,

1998; Williams, 1966). For this reason, color is possibly the most widely used feature in

studies of visual attention. Hence, it is important to know whether results obtained for

motion direction can be generalized to color. Indeed, studies on attentional control setting

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(ACS) have found that attention can be captured by two possible colors (Adamo et al.,

2008; Moore & Weissman, 2010; Irons, Folk, & Remington, 2012; Becker, Ravizza, &

Peltier, 2015). Thus attention to color might have a higher capacity than attention to

motion, such that participants can simultaneously attend to two colors. Second, studying

color offers another opportunity to get a better estimate of the number of features that can

be concurrently attended. Our previous results on motion did not allow us to distinguish

the two possible scenarios mentioned above. As will be seen later, our color results

provided a more definitive estimate of the capacity of feature-based attention.

Here we manipulated the number of color precues and measured the detection

threshold of a color signal in a psychophysical task. This allowed us to assess how

sensitivity to color changes as a function of number of attentional cues. To preview our

results, our findings support the idea that participants can only effectively attend to a

single color at a time.

Experiment 1a

In this first experiment, we used a color detection task, modeled after our motion

experiment (Liu, Becker, & Jigo, 2013), and varied the number of color cues to test the

capacity of feature-based attention to color. There was always a single color target on

every trial, but the target color was conveyed to participants either alone or in a group of

other colors. This allowed us to test how efficiently participants can attend to multiple

colors.

Methods

Participants

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Six students from Michigan State University participated in this experiment. All

participants had normal or corrected-to-normal acuity, and reported to have normal color

vision. We informally assessed their color vision by presenting them the Dvorine Pseudo-

Isochromatic Plates (Dvorine, 1963) for which all participants correctly identified all the

numbers (this assessment was also performed for all participants in the subsequent

experiments). Participants gave informed consent and were compensated at a rate of

$10/hr.

Color Selection: isoluminance setting procedure

To study color-based attention, we needed to ensure that our stimuli only differed

in color (or more precisely, hue), but not in other characteristics, such as luminance.

Otherwise, participants might use luminance, instead of color, to guide their attention.

Thus, for each participant, we selected six color values at their own psychophysical

isoluminance. We started with six saturated and distinct hues (red, green, blue, yellow,

purple, and orange), and used the method of heterochromatic flicker photometry to equate

the brightness among the colors for each participant (Kaiser, 1991; Lee, Martin &

Valberg 1988). Participants viewed a flickering checkerboard (8 Hz) consisting of a

constant gray (6.34 cd/m2) and one of the six colors (Figure 1A), whose luminance was

adjusted by the participant to minimize their perception of flicker. Each participant

repeated four measurements for each of the six color-gray pairings, and the mean across

the four measurements was used as the luminance value for that color. In general, we did

not observe significant variations in these isoluminance settings across participants (see

Figure 1B for colors from the average setting values).

Visual Stimuli: attention task

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The stimuli were static arrays of 240 colored dots (dot size: 0.1°), whose locations

were restricted to a 4° thick annulus centered on the fixation dot (inner radius = 1°, outer

radius = 5°). The dots were drawn in six colors, the values of which were individually

determined for each participant (see above). The spatial location of each dot was

randomly assigned for each stimulus. Stimuli were presented on a 21” CRT monitor with

a refresh rate of 75Hz and a resolution of 1024×768. Participants rested their heads on a

chin rest that was positioned 57 cm away from the monitor.

We manipulated the proportion of a particular color, and referred to this measure

as color coherence. Zero color coherence was defined as an equal proportion in the

number of dots among all six colors (i.e., 40 dots per color), whereas a non-zero

coherence indicated that one color had a larger proportion than the others. In the latter

case, the color with a larger proportion was referred to as the dominant color, and the

other five colors were equally proportioned. Numerically, the coherence was defined by

the following equation:

color coherence = Ps – Pn

where Ps was the proportion of dots in the dominant color, and Pn was proportion of dots

in each non-dominant color, with the following constraint:

Pn = (1-Ps)/5

That is, all non-dominant colors were equally proportioned after accounting for

the dominant color. The color coherence is a color analog of motion coherence

implemented in the classic, and widely used, random-dot motion stimulus (Newsome &

Pare, 1988), and it has been used in our previous study (Wang, Miller, & Liu, 2015).

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Task and Procedures

Participants detected the presence of a dominant color in a two-interval forced

choice task (2IFC, see Figure 2). Each trial started with a 0.5 s cue period followed by a

0.7 s blank interval, after which two intervals of stimuli were shown, each for 0.3 s and

separated by 0.7 s. One interval contained a 0% coherent stimulus in which the six colors

were in equal proportion (noise), while the other interval contained a non-zero coherent

stimulus in which one color had a larger proportion than the other five colors (signal). We

used six coherence levels (2%, 6%, 12%, 18%, 24%, 30%, 36%) with the method of

constant stimuli to measure performance in this color detection task. Participants were

instructed to report the interval that contained the dominant color by pressing the “1” or

“2” key on the numeric keypad of a standard computer keyboard for the first and second

interval, respectively. Participants were instructed to respond as accurately as possible. A

sound was played on incorrect trials after the participant’s response as feedback. An

inter-trial interval of 1.5 s followed the participant’s response.

The dominant color could be any one of the six colors. To manipulate feature-

based attention, we presented one of four types of cues at the beginning of a trial. In the

no-cue (baseline) condition, the fixation dot dimmed to indicate the upcoming color

stimulus. In the one-cue condition, an additional circle (radius 0.5°) drawn in the

dominant color was shown either on the left or right of fixation (distance to fixation 1.5°)

to indicate the signal color. In the two-cue condition, two circles were shown on the left

and right side of fixation, one of which was drawn in the dominant color while the other

was drawn in another color, selected randomly from the remaining five colors. In the

three-cue condition, three circles were shown on the left, right, and above the fixation.

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One circle was drawn in the dominant color while the other two circles were drawn with

two other colors. Participants were instructed to attend to the cued color(s), as it would

help them to detect the signal. We used a long cue-to-stimulus delay (1.2 s) to ensure that

participants had sufficient time to encode and use the cues (Ball & Sekuler, 1981; Liu,

Stevens et al., 2007).

Participants performed the task in blocks of 35 trials, within which the type of cue

(no-cue, one-cue, two-cue, three-cue) was held constant. Cue type was indicated by a

prompt at the beginning of each block. Within a block, the dominant color, coherence

level of the color signal, and cue location of the dominant color (in the one-cue, two-cue,

and three-cue conditions) were all randomized. Thirty-two blocks of trials were run, with

eight blocks for each cue type. The order of blocks was pseudo-randomized such that for

every four blocks, each of the four cue types occurred once in a random order. This

ensured that there were no consistent order effects among the different cue conditions.

The experiment was completed in two separate hour-long sessions.

The majority of our participants were not familiar with the color detection task. In

our experience, there is a considerable perceptual learning effect for participants who

have never seen this type of stimulus. Thus, in addition to the main experiment, we also

ran a practice session using exactly the same procedure as the no-cue condition described

above. Participants performed the color detection task in 35-trial blocks until their

coherence threshold did not show a sizable drop in 3 consecutive blocks. The practice

session always took place on a different day and before the main experiment. We also ran

the practice session for participants in subsequent experiments who have not had much

experience with our random dot color stimuli.

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Data analysis

Proportion correct data were fitted by a three-parameter Weibull function with a

location parameter, a slope parameter, and a lapse parameter representing deviation from

1 at the upper asymptote (e.g., due to pressing the wrong buttons). The lower asymptote

was fixed at 0.5 (it was fixed at 0 in the one-interval Yes/No task in Experiments 2 and

3). Fits were performed using maximum-likelihood estimation, as implemented in the

Palamedes Toolbox (Prins & Kingdom, 2009). Threshold was defined as the coherence

level corresponding to an accuracy of 75% in Experiment 1 and 50% in Experiments 2

and 3.

Results and Discussion

Accuracy showed a monotonic increase as a function of color coherence, which

was well fit by a sigmoid function such as Weibull (Figure 3A). Compared to the baseline

condition (no-cue), a single cue shifted the psychometric function to the left, indicating

that a lower color coherence was needed to detect the signal at the same criterion

performance. Neither the two-cue nor the three-cue condition shifted the psychometric

function to the left, indicating no cueing benefit. Indeed, the psychometric functions for

the two- and three-cue conditions were slightly shifted to the right. To quantify the

cueing effect, we fitted individual participant data with the Weibull function and obtained

thresholds, defined as the color coherence corresponding to 75% accuracy. Figure 3B

shows the group-averaged thresholds for the four cue types, which were significantly

different from each other as assessed by a one-way repeated measures ANOVA (F(3,

15)=8.21, p < 0.05). Post-hoc paired comparisons showed that the one-cue condition had

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a significantly lower threshold than the baseline (no-cue) condition (t(5)=4.04, p < 0.05),

the two-cue condition (t(5)= 3.20, p < 0.05), and the three-cue condition (t(5)=4.17, p <

0.05). No other comparisons reached significance.

These results showed that knowing the color of the upcoming target facilitates its

detection. This finding is consistent with many earlier studies that showed that attending

to a feature enhances its processing (Lankheet & Vestraten, 1995; Saenz, Buracas &

Boynton, 2002, 2003; Arman, Ciaramitaro & Boynton 2006; Liu, Larsson & Carrasco,

2007; Liu & Mance, 2011; Liu & Hou, 2011; White & Carrasco, 2011). Furthermore, by

manipulating the number of attended colors, our results revealed a severe capacity limit

in attention to color. The coherence thresholds for the two-cue and three-cue conditions

were similar to that of the baseline condition. A trivial explanation for these results is that

participants simply forgot which colors to attend after the cue period. This is very

unlikely because working memory capacity for simple colors is at least 3-4 (Cowan,

2001; Luck & Vogel, 1997; Zhang, & Luck, 2008). Furthermore, all participants indeed

subjectively reported to know which colors to attend in all conditions when we queried

them at the end of the experiment.

These results are thus consistent with the idea that the focus of attention contains

a single active item (a color feature in this case). Before accepting this conclusion,

however, we need to consider some alternative explanations. One possible explanation

for the absence of a cueing effect in the two-cue and three-cue conditions is that

participants needed more time to process multiple cues. Although our choice of stimulus-

onset-asynchrony (SOA) between the cue and the dot stimulus was quite long (1.2 s), and

should have been sufficient given previous research showing that a single feature cue

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took somewhere between 300 and 500 ms to exert its effects (Liu, Stevens, & Carrasco,

2007), it is possible that multiple color cues need more time to be processed in order to

facilitate target detection. Therefore, in Experiment 1b, we used an even longer cue-to-

target SOA to test whether more processing time would help participants use the

information from multiple cues.

Experiment 1b

In this experiment, we prolonged the cue-to-stimulus SOA to investigate whether

the lack of cueing effect in Experiment 1a was due to an insufficient amount of time

given to process the cues. We removed the three-cue condition in this experiment because

performance did not differ between the two-cue and three-cue conditions; if participants

could not utilize two cues they probably could not utilize three cues either. Thus, we

focused on whether attending to two colors can improve performance if participants were

given more time to process the cue.

Methods

Participants

Six students from Michigan State University participated in this experiment, two

of which participated in Experiment 1a. All participants had normal or corrected-to-

normal acuity, as well as reported to have normal color vision. Participants gave

informed consent and were compensated at the rate of $10/hr.

Task and Procedures

The experiment was identical to Experiment 1a with the following exceptions.

There were three conditions: no-cue (baseline), one-cue, and two-cue. The cue-to-

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stimulus SOA was prolonged to 2 s with a 0.5 s cue duration and 1.5 s blank interval.

Participants were instructed that the cues were always valid and should be used to detect

the signal interval. There were six 35-trial blocks for each cue type (18 blocks total).

Every three blocks were pseudo-randomized such that they would contain a random

permutation of the three cue types. The experiment was completed in two 40-min

sessions on separate days.

Results and Discussion

Compared to the baseline condition, the one-cue condition shifted the

psychometric function to the left, but the two-cue condition did not produce a sizable

leftward shift (Figure 4A). Group averaged coherence thresholds (Figure 4B) were

significantly different from each other (F(2,10)=5.60, p <0.05). Post-hoc comparisons

showed that detection threshold was significantly lower for the one-cue condition than for

the no-cue (t(5)=3.10, p < 0.05) and two-cue conditions (t(5)=2.74, p < 0.05), but did not

differ between the no-cue and two-cue conditions (t(5)=0.59, p = 0.58).

Again, we observed a reliable cueing effect in the one-cue condition and the lack

of a cueing effect in the two-cue condition, which replicated the results in Experiment 1a.

Despite increasing the cue-to-stimulus SOA, two cues did not improve performance

compared to the baseline. These results again suggest that feature-based attention to color

is limited to a single color. There was, however, another caveat due to the 2IFC task and

the nature of the color coherence stimulus. Consider a two-cue trial, where one cue was

the dominant color (say red) and the other cue was a non-dominant color (say green). The

dominant color had a higher proportion in the signal interval than the noise interval. At

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the observed coherence threshold of ~10%, the signal interval contained ~25% of the

dominant color, whereas the noise interval contained 16.7% (1/6) of the dominant color.

Thus a mechanism that amplifies the dominant color will favor the correct choice of the

signal interval. However, the cued, but non-dominant color was actually more prevalent

in the noise interval (16.7%) than the signal interval (~15%). This difference was

relatively small, but a mechanism that amplifies the cued non-dominant color would

favor the choice of the noise interval. Could it be that participants actually attended to

two colors on the two-cue trials, but the attentional enhancement of the cued non-

dominant color favored the choice of the noise interval, thus reducing the overall

performance on the two-cue trials? Numerically, this seems implausible, as the

proportion difference between the signal and noise interval for the dominant color (25%

vs. 16.7%) was much larger than that for the non-dominant color (15% vs. 16.7%). Thus,

even if such an effect exists, it should not completely cancel out the cueing benefit, which

was what we have found so far. Nevertheless, such a possibility does add some

uncertainty to our interpretation, and we sought to improve our paradigm in the

subsequent experiments to seek additional evidence.

Experiment 2

As discussed above, the cued, but non-dominant, color could lead to erroneous

responses in the two-cue condition in Experiment 1 because of the requirement of explicit

comparison between the two intervals in the 2IFC task. We reasoned that if we removed

the explicit comparison by using a single interval “yes/no” task, then this problem would

be alleviated. Thus in this experiment, we presented either a signal or noise stimulus in a

single interval and asked participants to respond whether they detected the signal.

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Methods

Participants

Six students from Michigan State University participated in this experiment, one

of which participated in Experiment 1. All participants had normal or corrected-to-normal

acuity, as well as reported to have normal color vision. Participants gave informed

consent and were compensated at the rate of $10/hr.

Task and Procedures

This experiment was similar to Experiment 1 so only the differences are

emphasized here. We used a one interval target-present/target-absent (Yes/No) task. Each

trial started with a 0.5 s cue period, followed by a 0.7 s blank period and a single 0.3 s

stimulus interval (Figure 5). Half of the trials contained a 0% coherent stimulus where

the six colors were in equal proportion (target absent), while the other half of trials

contained a non-zero coherent stimulus where one color had a larger proportion than the

other five colors (target present). We used six coherence levels (8%, 12%, 18%, 24%,

36%, 60%) with the method of constant stimuli. The coherence values were different

from Experiment 1 because pilot study indicated that the single-interval task was more

difficult, thus requiring a larger range to cover the entire psychometric function.

Participants were instructed to report the presence of the target by pressing 1 and its

absence by pressing 2 on the numeric keyboard of a standard computer keyboard. They

were instructed to respond as accurately as possible. The same auditory feedback and

inter-trial interval as in Experiment 1 were used.

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Participants performed the task in blocks of 36 trials, within which the type of cue

(no-cue, one-cue, two-cue) was held constant. Cue type was indicated by a prompt at the

beginning of each block. Within a block, the dominant color, coherence level, and cue

location of the dominant color were all randomized. Forty-two blocks of trials were run,

with fourteen blocks for each cue type. The order of blocks was pseudo-randomized such

that for every three blocks, each of the three cue types occurred once in a random order.

The experiment was completed in two hour-long sessions, which occurred on separate

days.

Results and Discussion

In the analysis of overall performance, accuracy was defined as the difference

between the hit rate and false alarm rate. Accuracy on the task showed a monotonic

increase as a function of color coherence (Figure 6A). Compared to the baseline

condition (no-cue), a single cue shifted the psychometric function to the left, indicating a

cueing benefit. However, two cues did not produce a leftward shift. We fitted individual

participant data with the Weibull function and obtained thresholds, defined as the color

coherence that produced an accuracy of 50%. Figure 6B shows the group-averaged

thresholds for the three cueing conditions, which were significantly different from each

other as assessed by a one-way repeated measures ANOVA (F(2, 10)=14.73, p < 0.05).

Post-hoc comparisons showed that the one-cue condition had a lower threshold than both

the baseline (no-cue) condition (t(5)=3.46, p < 0.05) and the two-cue condition

(t(5)=6.33, p < 0.05). Critically, the two-cue condition was not different from the

baseline.

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Once again we observed a reliable cueing effect in the one-cue condition but none

in the two-cue condition, which replicated the results in Experiment 1. Despite removing

the comparison process that was inherent in our 2IFC paradigm, our results still suggest

that two color cues cannot be attended to simultaneously, again, consistent with the

notion that only a single color feature can be in the focus of attention. Although this

result has been quite consistent across the three experiments so far, it is also intriguing in

that the two-cue condition did not produce any performance benefit compared to the

baseline (no-cue) condition. One might expect a reduced, but nevertheless detectable,

benefit of two cues over baseline, given that two cues still provided useful (albeit less

specific) information and we know participants can use one cue effectively. In the next

experiment, we explored one potential scenario that could occur in the two-cue condition,

namely, that participants chose one cue to attend on every trial.

Experiment 3

Here we further probed what can explain the lack of any cueing effect in the two-

cue condition. One possibility is that participants randomly chose one cue to attend on a

two-cue trial. If this were the case, then on half of the trials, their performance should be

facilitated by attending to the right color, similar to the one-cue condition. On the other

half of the trials, they would be attending to a non-dominant color. In this case,

performance would be either similar to the baseline, which seems likely given our

previous numerical reasoning (at the end of Experiment 1), or below baseline, if the small

difference in color proportion leads to incorrect responses. One way to test these

possibilities is to explicitly instruct participants to attend to only one color on a two-cue

trial. This essentially creates a situation where the cue is 50% valid, which we refer to as

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“partial one-cue”. If performance in this condition still showed no improvement over

baseline, this would imply that participants indeed chose one cue to attend on the two-cue

trials in Experiments 1 & 2. If, however, performance in the partial one-cue trials was

better than baseline, then it would suggest that participants did not voluntarily choose one

cue to attend in the original two-cue trials, but that other factors limited their

performance.

Methods

Participants

Six students from Michigan State University participated in this experiment, one

of which participated in Experiments 1 & 2. All participants had normal or corrected-to-

normal acuity, as well as reported to have normal color vision. Participants gave

informed consent and were compensated at the rate of $10/hr.

Task and Procedures

The experiment was identical to Experiment 2, with the only difference being that

the two-cue condition was replaced by a partial one-cue condition in which a cue with

50% validity was presented on each trial; therefore, the cued color matched the dominant

color on half of the trials (valid trials) and a non-dominant color on the other half of the

trials (invalid trials). Valid and invalid trials were analyzed both together and separately,

thus, we ultimately analyzed five different cue types: no-cue (baseline), one-cue, valid

cue, invalid cue, and partial one-cue. The partial one-cue was intended to simulate

participants attending to a single cue on every trial of the two-cue condition. We did not

present two cues and ask participants to only attend to one, because the presence of two

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cues could lead them to automatically attend to both colors, which would be impossible

to control.

Participants performed the task in blocks of 48 trials, within which the type of cue

(no-cue, one-cue, partial one-cue) was held constant. Cue type was indicated by a prompt

at the beginning of each block. Within a block, the dominant color, coherence level, and

cue location of the target color were all randomized. Forty-two blocks of trials were run,

with 12 blocks for the no-cue and one-cue conditions, and 18 blocks for the partial one-

cue condition. We had more blocks of the partial one-cue condition in order to collect an

adequate number of valid and invalid trials. The order of blocks was pseudo-randomized

such that for every seven blocks, the partial one-cue condition was presented three times,

while the no-cue and one-cue conditions were each presented twice. The order of the

conditions was randomized every seven blocks. The experiment was completed in two

hour-long sessions on separate days.

Results and Discussion

Accuracy was defined as the difference between the hit rate and false alarm rate.

For the partial one-cue condition, we calculated both the overall accuracy and separate

accuracies for valid and invalid trials. Thus, we ultimately analyzed five different cues

types: no-cue (baseline), one-cue, valid cue, invalid cue, and partial one-cue. Accuracy

showed a monotonic increase as a function of color coherence (Figure 7A). Compared to

the baseline condition (no-cue), the one-cue condition and valid cues in the partial one-

cue condition shifted the psychometric function to the left. Conversely, invalid cues did

not appreciably shift the psychometric function away from the baseline. Finally, the

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overall performance in the partial one-cue condition was midway between that of valid

and invalid cues, and also midway between no-cue and one-cue conditions.

We also fitted individual participant data and obtained color coherence thresholds,

corresponding to 50% accuracy. Figure 7B shows the group-averaged thresholds for the

no-cue, one-cue, and partial one-cue conditions. We focused our comparison among these

three cue types without the valid and invalid conditions because the partial one-cue was

comprised of, and thus not independent from, valid and invalid cues. Thresholds were

significantly different from each other (F(2, 10)=36.78, p < 0.01). Post-hoc paired

comparisons showed that one-cue lowered the threshold compared to the baseline

(t(5)=7.30, p < 0.01) and partial one-cue (t(5)=4.85, p < 0.05). Critically, partial one-cue

also significantly lowered the threshold compared to the baseline (t(5)=5.94, p < 0.05).

We also compared thresholds in the valid and invalid trials to baseline and found that

valid cues lowered the threshold (mean=0.18, t(5)=5.91, p < 0.05), while invalid cues did

not change the threshold (mean=0.22, t(5)=1.02, p = 0.35).

Again, we found a robust cueing effect in the one-cue condition. Interestingly, the

partial one-cue condition also produced a reliable cueing effect compared to the baseline.

This was due to a positive cueing effect on the valid trials, and a lack of (but not

negative) cueing effect on the invalid trials. These two effects combined to give rise to an

overall smaller, but significant, cueing effect compared to the baseline. Thus, when we

explicitly instructed participants to attend to one cue with a 50% validity, we observed a

cueing benefit, unlike the two-cue condition in the previous experiments, which showed a

lack of any cueing benefit. The inference is thus that participants in the previous

experiments did not choose to attend only one cue on the two-cue trials, because if they

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did, we should have observed a cueing benefit similar to the partial one-cue condition in

this experiment. Thus, the lack of cueing effects in the standard two-cue condition must

be due to other constraints in the attentional system, to which we turn our discussion in

the next section.

General Discussion

We measured the detection threshold for a weak color signal in a noisy stimulus

while manipulating the number of color cues to direct attention. Over four experiments

employing both single-interval and two-interval tasks, results showed a consistent

pattern: attending to one color improved performance, while attending to two colors did

not improve performance relative to a neutral baseline without any precue. These results

suggest that participants can only effectively attend to a single color at a time, indicating

a severe capacity limit of feature-based attention to color.

Capacity of feature-based attentional modulation

There are several design features of our experiments that afforded us to

investigate feature-based attentional modulation of sensory representations. First, we

measured detection threshold in a psychophysical task to index the sensitivity to color.

Second, we presented a single stimulus at fixation, thus eliminating the contribution of

spatial attention. The experimental design is similar to our previous work on attention to

motion directions (Liu, Becker, & Jigo, 2013). We also obtained mostly consistent results

with the motion study, in that attending to two features is less effective than attending to

one feature, but results differed when comparing the two-cue to the no-cue baseline.

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Attending to two directions yielded better performance than the baseline, but attending to

two colors yielded similar performance as the baseline. As mentioned in the Introduction,

the former result is consistent with either participants attending to only one direction, or

with participants attending to two directions but with less efficiency. The color results are

less ambiguous in that they strongly suggest that participants can attend to only one color

at a time.

Although the color results are more definitive than the motion results, they also

raised a question, regarding the consistent observation that the two-cue condition did not

afford any performance benefit over the no-cue baseline. If participants could attend to

one color on the two-cue trials, then on half the trials they would attend to the target

color, similar to the one-cue condition, and on the other half of the trials they would

attend to the wrong color, which could be similar to the no-cue condition. If so, then on

average, performance on two-cue trials should be better than no-cue baseline, though this

performance benefit should be reduced. In Experiment 3, we explicitly tested this

hypothesis by asking participants to attend to only one color on two-cue trials (the partial

one-cue condition). Results from this experiment confirmed the above intuition in that

now the partial one-cue trials exhibited a small, but significant cueing effect over the no-

cue baseline. These results suggest that on the standard two-cue trials in Experiments 1

and 2, participants probably did not choose one color to attend, because if they did, the

two-cue condition should exhibit a cueing effect, like the partial one-cue condition.

Hence, the most likely explanation on two-cue trials is that participants attempted to

attend to both colors, but such an effort did not enhance either color’s representation.

Data from the one-cue condition showed that the attentional control mechanism can

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enhance the representation of a single color. When two colors need to be simultaneously

attended, however, the limit in attentional control is such that neither color can be

enhanced. This interpretation is consistent with a similar finding in research on active

working memory templates, discussed below.

Because our previous results on motion (Liu, Becker, & Jigo, 2013) showed a

performance benefit in the two-cue condition relative to a no-cue baseline, it seems that

attention to motion direction has a higher capacity than attention to color. It is unclear

what caused this difference. One possibility is that participant can partially attend to two

directions; another possibility is that participants attended to one direction but the

enhancement spreads to neighboring directions. We favor the latter interpretation based

on considerations of parsimony, although further research is needed to understand the

basis of the observed difference between motion and color. Nevertheless, the overall

results demonstrate a highly limited capacity in attending to multiple features. Thus,

although subjectively it seems we are able to attend to multiple, or at least two, simple

features, psychophysical performance showed either a modest or no improvement over a

neutral condition. These results demonstrate a severe limit in the size of the attentional

focus for visual features, and could inform further development of theories of visual

attention (Wolfe, 1994; Tsotsos et al., 1995; Reynolds & Heeger, 2009; Lee & Maunsell,

2009).

Relationship to studies on attentional capture and working memory

Our results also have implications on a body of literature using contingent

attentional capture (Folk, Remington, & Johnston, 1992) to assess the capacity of the

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attentional control setting (ACS). In this paradigm, the effect of peripheral, task-

irrelevant, color cues on identifying a subsequent target was measured, and the

interference effect of the cue (i.e., attentional capture) was taken as evidence that the

cued color was in the focus of attention. A number of studies have shown that cueing one

of two colors produced capture effect when searching for targets defined by either color,

suggesting that the ACS can contain two colors (Adamo et al., 2008; Moore &

Weissman, 2010; Irons, Folk, & Remington, 2012; Becker, Ravizza, & Peltier, 2015). In

another study using a visual search task, participants were asked to find a colored target

that can assume one of two possible colors among distractors of other colors (Beck,

Hollingworth, & Luck, 2012). From fixation duration patterns, the authors inferred that

simultaneously searching through both colors was possible, again supporting the notion

that ACS can contain two colors.

These above studies have exclusively tested the color feature, and they seem to

contradict our current claim that attention can be directed only to one color at a time.

However, it is not clear whether these different paradigms tap into the same process. We

measured how actively attending to colors improves detection sensitivity in a

psychophysical procedure, whereas studies on ACS measured how task-irrelevant colors

impaired performance, often in terms of a slowdown in response latency. Our task

presumably provided a more direct measure of the attentional modulation of early

sensory representations. The effects revealed in the ACS studies could be due to later

stages of processing such as sensory read-out and response selection. We have previously

made similar arguments in our motion study (Liu, Becker, & Jigo, 2013), with a caveat

that they were based on comparing two different feature dimensions (motion and color).

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The present experiments further strengthened this argument by demonstrating the

capacity limit in attention to colors. This early vs. late conjecture is supported by

electroencephalography evidence that color congruency only modulated late event-related

potentials in a contingent capture paradigm, suggesting that ACS operates at stages after

initial selection (Adamo, Pun, & Ferber, 2010).

Another body of literature relevant to this discussion is the research examining

how working memory biases attentional selection. In this paradigm, participants were

first asked to remember some features for an upcoming memory test, which was followed

by an intervening visual search task in which the remembered features could appear as

distractors (Pashler & Shiu, 1999; Downing, 2000; Soto, Heinke, Humphreys, & Blanco,

2005). It was found that the memorized feature impeded search performance relative to a

non-memorized feature, which suggests that the memorized feature was part of the active

working memory. Given that it is generally believed that top-down attentional selection is

mediated by the content of active working memory (Wolfe, 1994; Desimone & Duncan,

1995; Hamker, 2005), we would expect that performance in our task should be related to

the number of active items in working memory. A recent theoretical synthesis based on

behavioral and neural data proposed that contents in visual working memory can be

divided into two states, an active state that contains only one item, and an accessory state

that contains the rest of the items in storage. Furthermore, the active memory

representation serves as an attention template that guides selection, whereas the accessory

items do not directly interact with attention (Olivers, Peters, Houtkamp, & Roelfsema,

2011). This view fits nicely with our results, which showed only one color in the

attentional focus. It is worth noting that the working memory studies used a very different

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paradigm where the memory items were irrelevant to the search task and a distractor cost

was measured. Our paradigm required active attention to the features and measured how

detection sensitivity was improved by attention. Thus our results provide converging

evidence for the view that there is only one active attention template at a time (also see

Houtkamp & Roelfsema, 2009).

Interestingly, an analogous effect with our two-cue condition was reported in a

recent study on working memory’s guidance of attention (van Moorselaar, Theeuwes, &

Olivers, 2014). These investigators found that holding one color in working memory

produced a robust distractor cost, whereas holding two colors did not produce any cost,

compared to a neutral condition. It was suggested that when two colors are held in

working memory, neither item can become the active template (van Moorselaar,

Theeuwes, & Olivers, 2014). Our finding of a lack of cueing effect in the two-cue

condition is consistent with this suggestion. It could be the case that there is some

threshold activation that an item must surpass to become the active template, and the act

of attending to two items does not give enough activation to either item, thus rendering

neither item active.

Conclusions

Our results demonstrate that people cannot actively pay attention to more than one

color during a threshold detection task. These results are probably due to an inability to

simultaneously modulate multiple sensory representations. This view is consistent with

studies on different states of working memory and their influence on attention. Overall,

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these studies reveal a severe bottleneck in our ability to attend to multiple features and

inform ways to optimize performance in visual tasks.

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References

Adamo, M., Pun, C., Pratt, J., & Ferber, S. (2008). Your divided attention, please! The maintenance of multiple attentional control sets over distinct regions in space. Cognition, 107(1), 295–303.

Adamo, M., Pun, C., & Ferber, S. (2010). Multiple attentional control settings influence late attentional selection but do not provide an early attentional filter. Cognitive Neuroscience, 1(2), 102-110.

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

Ball, K., & Sekuler, R. (1981). Cues reduce direction uncertainty and enhance motion detection. Perception & Psychophysics, 30(2), 119-128.

Beck, V. M., Hollingworth, A., & Luck, S.J. (2012). Simultaneous control of attention by multiple working memory representations. Psychological Science, 23(8), 887-898.

Becker, M. W., Ravizza, S. M., & Peltier, C. (2015). An inability to set independent attentional control settings by hemifield. Attention, Perception, & Psychophysics, 1-13.

Carrasco, M. (2006). Covert attention increases contrast sensitivity: Psychophysical, neurophysiological, and neuroimaging studies. In Visual Perception, S. Martinez-Conde, S. L. Macknik, L. M. Martinez, J. M. Alonso, and P. U. Tse, eds. (Amsterdam: Elsevier), pp. 33–70.

Cohen, M. R., & Maunsell, J. H. R. (2011). Using neuronal populations to study the mechanisms underlying spatial and feature attention. Neuron, 70(6), 1192-1204.

Cowan, N. (2001). The magical number 4 in short-term memory: A reconsideration of mental storage capacity. Behavioral and Brain Sciences, 24(1), 87–114.

Cousineau, D. (2005). Confidence intervals in within-subject designs: A simpler solution to Loftus and Masson’s method. Tutorials in quantitative methods for psychology, 1(1), 42-45.

Desimone, R., & Duncan, J. (1995). Neural mechanisms of selective visual attention. Annual Review of Neuroscience, 18(1), 193-222.

Downing, P. E. (2000). Interactions between visual working memory and selective attention. Psychological Science, 11(6), 467-473.

Dvorine, I. (1963). Dvorine Pseudo-Isochromatic Plates. Harcourt, Brace & World: New York, NY.

Folk, C. L., Remington, R. W., & Johnston, J. C. (1992). Involuntary covert orienting is contingent on attentional control settings. Journal of Experimental Psychology: Human Perception & Performance, 18(4), 1030–1044.

2

Hamker, F. H. (2005). The reentry hypothesis: the putative interaction of the frontal eye field, ventrolateral prefrontal cortex, and areas V4, IT for attention and eye movement. Cerebral Cortex, 15(4), 431-447.

Herrmann, K., Heeger, D.J. & Carrasco, M. (2012). Feature-based attention enhances performance by increasing response gain. Vision Research, 74, 10-20.

Houtkamp, R., & Roslfsema, P. R. (2009). Matching of visual input to only one item at any one time. Psychological Research, 73, 317-326.

Irons, J. L., Folk, C. L., & Remington, R. W. (2012). All set! Evidence of simultaneous attentional control settings for multiple target colors. Journal of Experimental Psychololgy: Human Perception & Performance, 38(3), 758–775.

Kaiser, P. K. (1991). Flicker as a function of wavelength and hetero-chromatic flicker photometry. In: Limits of vision (J. J. Kulikowski, V. Walsh, I. J. Murray, eds), pp. 171–190. Basingstoke: MacMillan.

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

Lee, B. B., Martin, P. R., & Valberg, A. (1988). The physiological basis of heterochromatic flicker photometry demonstrated in the ganglion cells of the macaque retina. Journal of Physiology, 404(1), 323-347.

Lee, J., & Maunsell, J. H. (2009). A normalization model of attentional modulation of single unit responses. PLoS ONE 4(2): e4651.

Liu, T., Becker, M. W., Jigo, M. (2013). Limited featured-based attention to multiple features. Vision Research. 85(7), 36-44.

Liu, T. & Hou, Y. (2011) Global feature-based attention to orientation. Journal of Vision, 11(10), 1-8.

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

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

Liu, T., Stevens, S. T., & Carrasco, M. (2007). Comparing the time course and efficacy of spatial and feature-based attention. Vision Research, 47, 108-113.

Luck, S. J., & Vogel, E. K. (1997). The capacity of visual working memory for features and conjunctions. Nature, 390(6657), 279–281.

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

Maunsell, J. H. R., & Treue, S. (2006). Feature-based attention in visual cortex. Trends in Neurosciences, 29(6), 317-322.

Moore, K. S., & Weissman, D. H. (2010). Involuntary transfer of a top-down attentional set into the focus of attention: Evidence from a contingent attentional capture paradigm. Attention, Perception & Psychophysics, 72(6), 1495–1509.

3

Motter, B. C., & Belky, E. J. (1998). The guidance of eye movements during active visual search. Vision Research, 38, 1805–1815.

Newsome, W. T., & Pare, E. B. (1988). A selective impairment of motion perception following lesions of the middle temporal visual area (MT). The Journal of Neuroscience, 8(6), 2201-2211.

Olivers, C. N., Peters, J., Houtkamp, R., & Roelfsema, P. R. (2011). Different states in visual working memory: When it guides attention and when it does not. Trends in cognitive sciences, 15(7), 327-334.

Pashler, H., & Shiu, L. P. (1999). Do images involuntarily trigger search? A test of Pillsbury’s hypothesis. Psychonomic Bulletin & Review, 6(3), 445-448.

Posner, M.I. (1980) Orienting of attention. Quarterly Journal of Experiment Psychology, A, 32(1): 3–25.

Prins, N & Kingdom, F.A.A. (2009) Palamedes: Matlab routines for analyzing psychophysical data. http://www.palamedestoolbox.org

Reynolds, J. H., & Heeger, D. J. (2009). The normalization model of attention. Neuron, 61(2), 168-185.

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

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

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

Soto, D., Heinke, D., Humphreys, G. W., & Blanco, M. J. (2005). Early, involuntary top-down guidance of attention from working memory. Journal of Experimental Psychology: Human Perception & Performance, 31(2), 248.

Tsotsos, J. K., Culhane, S. M., Wai, W. Y. K., Lai, Y., Davis, N., & Nuflo, F. (1995). Modeling visual attention via selective tuning. Artificial Intelligence, 78(1), 507-545.

van Moorselaar, D., Theeuwes, J., & Olivers, C. N. (2014). In competition for the attentional template: Can multiple items within visual working memory guide attention? Journal of experimental psychology: human perception and performance, 40(4), 1450.

Wang, Y., Miller, J. R., & Liu, T. (2015). Suppression effects in feature-based attention. Journal of Vision, 15(5):15, 1-16.

White, A. L., & Carrasco, M. (2011). Feature-based attention involuntarily and simultaneously improves visual performance across locations. Journal of Vision, 11(6), 1-10.

Williams, L. G. (1966). The effect of target specification on objects fixated during visual search. Perception & Psychophysics, 1, 315– 318.

3

Wolfe, J. M. (1994). Guided Search 2.0: A revised model of visual search. Psychonomic Bulletin & Review 1(2): 202–238.

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

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Figures

Figure 1: A, Schematic of the checkerboard stimulus used in the isoluminance procedure.

Gray squares at a constant luminance and color squares (in this example, green) with

adjustable luminance were used to construct a counter-phase flickering checkerboard. B,

Average color values across participants from the isoluminance setting procedure during

Experiment 1a. The colors are generally muted because light colors (e.g., yellow and

orange) needed to be shown at lower luminance values to equate their subjective

brightness with dark colors (e.g., red and blue).

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Figure 2. Schematic of trials in Experiment 1a. Each trial commenced with one of four

cue types: no-cue, one-cue, two-cue, and three-cue. The one, two, and three cue types

contained the color that would be the dominant color in the signal interval. Two intervals

of the color stimuli then followed, with one interval containing a larger proportion of one

color (signal interval) and the other containing an equal proportion of all six colors (noise

interval). Participants were instructed to report which interval contained the signal. In this

example, the signal is in the first interval, with the dominant color being purple.

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Figure 3. Results from Experiment 1a. A, Group-averaged psychometric functions for

each cue type fitted with the Weibull function. Vertical dashed lines correspond to the

thresholds for each cue type. B, Group-averaged thresholds for each cue type. Error bars

are ± 1 SEM within subjects following the method of Cousineau (2005). Asterisks

indicate the significance level in paired t-tests (*: p < 0.05).

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Figure 4. Results from Experiment 1b. A, Group-averaged psychometric functions for

each cue type fitted with the Weibull function. B, Group-averaged thresholds for each

cue type. Plotting conventions are the same as in Figure 3.

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Figure 5. Schematic of trials in Experiment 2. After the cue display and the cue-to-

stimulus interval, a single stimulus was shown that contained either the signal or noise.

Participants were instructed to report whether the signal was present.

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Figure 6. Results from Experiment 2. A, Group-averaged psychometric functions for each

cue type fitted with the Weibull function. B, Group-averaged thresholds for each cue

type. Plotting conventions are the same as in Figure 3.

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Figure 7. Results from Experiment 3. A, Group-averaged psychometric functions for each

cue type fitted with the Weibull function. Vertical dashed lines correspond to the

thresholds for each cue type. B, Group-averaged thresholds for each cue type. Plotting

conventions are the same as in Figure 3 (*: p < 0.05; **: p < 0.01).